diff --git a/segmentation-3.0-b32-f16.mlmodelc/analytics/coremldata.bin b/segmentation-3.0-b32-f16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b7fda399a137c77661df251007d9785c8ca5622e --- /dev/null +++ b/segmentation-3.0-b32-f16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7c76a1fc31b7a4d739f9112bf8ac55f4b5caad583d6ac9e47de680e775c9ade5 +size 243 diff --git a/segmentation-3.0-b32-f16.mlmodelc/coremldata.bin b/segmentation-3.0-b32-f16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..7184df841f572c6fdde3215ed785affe23098cdf --- /dev/null +++ b/segmentation-3.0-b32-f16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ac710fb9bcd0310d0c40fd82f2350ca5287b18596da33470bf5185be148aad81 +size 439 diff --git a/segmentation-3.0-b32-f16.mlmodelc/model.mil b/segmentation-3.0-b32-f16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a418b91ac02029ebd8c5efc59d9fc712eaa2a34a --- /dev/null +++ b/segmentation-3.0-b32-f16.mlmodelc/model.mil @@ -0,0 +1,227 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor input) [FlexibleShapeInformation = tuple>>, tuple>>>>((("DefaultShapes", {{"input", [32, 1, 160000]}}), ("EnumeratedShapes", {{"047bedbd", {{"input", [24, 1, 160000]}}}, {"08383b0f", {{"input", [32, 1, 160000]}}}, {"146ea7a4", {{"input", [30, 1, 160000]}}}, {"14a6a9fa", {{"input", [27, 1, 160000]}}}, {"41d6af63", {{"input", [26, 1, 160000]}}}, {"4a349f6d", {{"input", [2, 1, 160000]}}}, {"4c2c6917", {{"input", [8, 1, 160000]}}}, {"4cb052b1", {{"input", [5, 1, 160000]}}}, {"4eab2425", {{"input", [23, 1, 160000]}}}, {"4f2b5bd2", {{"input", [14, 1, 160000]}}}, {"50b949f3", {{"input", [22, 1, 160000]}}}, {"5316ecea", {{"input", [1, 1, 160000]}}}, {"5d89881e", {{"input", [21, 1, 160000]}}}, {"693a1c76", {{"input", [19, 1, 160000]}}}, {"6ac4a6a4", {{"input", [29, 1, 160000]}}}, {"73f266d5", {{"input", [3, 1, 160000]}}}, {"73f43a1d", {{"input", [31, 1, 160000]}}}, {"7ee56056", {{"input", [18, 1, 160000]}}}, {"9035b52a", {{"input", [25, 1, 160000]}}}, {"94f7468c", {{"input", [20, 1, 160000]}}}, {"999a22b0", {{"input", [12, 1, 160000]}}}, {"9fad9511", {{"input", [4, 1, 160000]}}}, {"ab9dbd8c", {{"input", [9, 1, 160000]}}}, {"ae49a11c", {{"input", [16, 1, 160000]}}}, {"bf53b769", {{"input", [15, 1, 160000]}}}, {"c147bbba", {{"input", [11, 1, 160000]}}}, {"c32e6216", {{"input", [28, 1, 160000]}}}, {"d1a076a6", {{"input", [7, 1, 160000]}}}, {"dccf3050", {{"input", [17, 1, 160000]}}}, {"ef60c196", {{"input", [10, 1, 160000]}}}, {"fe5ae199", {{"input", [13, 1, 160000]}}}, {"ffc2aaa2", {{"input", [6, 1, 160000]}}}})))] { + fp32 var_9 = const()[name = string("op_9"), val = fp32(0x1.47ae14p-7)]; + string input_to_fp16_dtype_0 = const()[name = string("input_to_fp16_dtype_0"), val = string("fp16")]; + tensor sincnet_wav_norm1d_weight_to_fp16 = const()[name = string("sincnet_wav_norm1d_weight_to_fp16"), val = tensor([0x1.44p-7])]; + tensor sincnet_wav_norm1d_bias_to_fp16 = const()[name = string("sincnet_wav_norm1d_bias_to_fp16"), val = tensor([0x1.734p-5])]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_to_fp16 = cast(dtype = input_to_fp16_dtype_0, x = input)[name = string("cast_19")]; + tensor waveform_cast_fp16 = instance_norm(beta = sincnet_wav_norm1d_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_wav_norm1d_weight_to_fp16, x = input_to_fp16)[name = string("waveform_cast_fp16")]; + string outputs_pad_type_0 = const()[name = string("outputs_pad_type_0"), val = string("valid")]; + tensor outputs_strides_0 = const()[name = string("outputs_strides_0"), val = tensor([10])]; + tensor outputs_pad_0 = const()[name = string("outputs_pad_0"), val = tensor([0, 0])]; + tensor outputs_dilations_0 = const()[name = string("outputs_dilations_0"), val = tensor([1])]; + int32 outputs_groups_0 = const()[name = string("outputs_groups_0"), val = int32(1)]; + tensor filters_to_fp16 = const()[name = string("filters_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor outputs_cast_fp16 = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters_to_fp16, x = waveform_cast_fp16)[name = string("outputs_cast_fp16")]; + tensor input_1_cast_fp16 = abs(x = outputs_cast_fp16)[name = string("input_1_cast_fp16")]; + tensor var_119 = const()[name = string("op_119"), val = tensor([3])]; + tensor var_120 = const()[name = string("op_120"), val = tensor([3])]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([0, 0])]; + bool input_3_ceil_mode_0 = const()[name = string("input_3_ceil_mode_0"), val = bool(false)]; + tensor input_3_cast_fp16 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor sincnet_norm1d_0_weight_to_fp16 = const()[name = string("sincnet_norm1d_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40320)))]; + tensor sincnet_norm1d_0_bias_to_fp16 = const()[name = string("sincnet_norm1d_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40576)))]; + tensor input_5_cast_fp16 = instance_norm(beta = sincnet_norm1d_0_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_0_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor input_7_cast_fp16 = leaky_relu(alpha = var_9, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1])]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor sincnet_conv1d_1_weight_to_fp16 = const()[name = string("sincnet_conv1d_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40832)))]; + tensor sincnet_conv1d_1_bias_to_fp16 = const()[name = string("sincnet_conv1d_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88896)))]; + tensor input_9_cast_fp16 = conv(bias = sincnet_conv1d_1_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight_to_fp16, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor var_135 = const()[name = string("op_135"), val = tensor([3])]; + tensor var_136 = const()[name = string("op_136"), val = tensor([3])]; + string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("custom")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0])]; + bool input_11_ceil_mode_0 = const()[name = string("input_11_ceil_mode_0"), val = bool(false)]; + tensor input_11_cast_fp16 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor sincnet_norm1d_1_weight_to_fp16 = const()[name = string("sincnet_norm1d_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89088)))]; + tensor sincnet_norm1d_1_bias_to_fp16 = const()[name = string("sincnet_norm1d_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89280)))]; + tensor input_13_cast_fp16 = instance_norm(beta = sincnet_norm1d_1_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_1_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = leaky_relu(alpha = var_9, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")]; + tensor input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor([1])]; + tensor input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor([0, 0])]; + tensor input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor([1])]; + int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)]; + tensor sincnet_conv1d_2_weight_to_fp16 = const()[name = string("sincnet_conv1d_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89472)))]; + tensor sincnet_conv1d_2_bias_to_fp16 = const()[name = string("sincnet_conv1d_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125568)))]; + tensor input_17_cast_fp16 = conv(bias = sincnet_conv1d_2_bias_to_fp16, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight_to_fp16, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor var_151 = const()[name = string("op_151"), val = tensor([3])]; + tensor var_152 = const()[name = string("op_152"), val = tensor([3])]; + string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")]; + tensor input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor([0, 0])]; + bool input_19_ceil_mode_0 = const()[name = string("input_19_ceil_mode_0"), val = bool(false)]; + tensor input_19_cast_fp16 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor sincnet_norm1d_2_weight_to_fp16 = const()[name = string("sincnet_norm1d_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125760)))]; + tensor sincnet_norm1d_2_bias_to_fp16 = const()[name = string("sincnet_norm1d_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125952)))]; + tensor input_21_cast_fp16 = instance_norm(beta = sincnet_norm1d_2_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_2_weight_to_fp16, x = input_19_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor x_cast_fp16 = leaky_relu(alpha = var_9, x = input_21_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_163 = const()[name = string("op_163"), val = tensor([0, 2, 1])]; + int32 var_172 = const()[name = string("op_172"), val = int32(128)]; + int32 var_173 = const()[name = string("op_173"), val = int32(8)]; + tensor input_23_cast_fp16 = transpose(perm = var_163, x = x_cast_fp16)[name = string("transpose_6")]; + tensor var_207_shape_cast_fp16 = shape(x = input_23_cast_fp16)[name = string("op_207_shape_cast_fp16")]; + int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)]; + int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)]; + bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)]; + string var_207_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_207_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_0_indices_0_to_uint16 = const()[name = string("gather_0_indices_0_to_uint16"), val = uint16(0)]; + tensor var_207_shape_cast_fp16_to_int16 = cast(dtype = var_207_shape_cast_fp16_to_int16_dtype_0, x = var_207_shape_cast_fp16)[name = string("cast_18")]; + int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = gather_0_indices_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_207_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")]; + string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)]; + bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)]; + int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_17")]; + tensor concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_173, gather_0_cast_uint16_to_int32, var_172))[name = string("concat_0")]; + fp16 hx_1_value_0_to_fp16 = const()[name = string("hx_1_value_0_to_fp16"), val = fp16(0x0p+0)]; + tensor hx_1_cast_fp16 = fill(shape = concat_0, value = hx_1_value_0_to_fp16)[name = string("hx_1_cast_fp16")]; + tensor input_23_batch_first_transpose_perm_0 = const()[name = string("input_23_batch_first_transpose_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(4)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1, tensor split_0_cast_fp16_2, tensor split_0_cast_fp16_3 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = hx_1_cast_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(4)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1, tensor split_1_cast_fp16_2, tensor split_1_cast_fp16_3 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = hx_1_cast_fp16)[name = string("split_1_cast_fp16")]; + tensor split_10_split_sizes_0 = const()[name = string("split_10_split_sizes_0"), val = tensor([1, 1])]; + int32 split_10_axis_0 = const()[name = string("split_10_axis_0"), val = int32(0)]; + tensor split_10_cast_fp16_0, tensor split_10_cast_fp16_1 = split(axis = split_10_axis_0, split_sizes = split_10_split_sizes_0, x = split_0_cast_fp16_0)[name = string("split_10_cast_fp16")]; + int32 concat_10_axis_0 = const()[name = string("concat_10_axis_0"), val = int32(2)]; + bool concat_10_interleave_0 = const()[name = string("concat_10_interleave_0"), val = bool(false)]; + tensor concat_10_cast_fp16 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (split_10_cast_fp16_0, split_10_cast_fp16_1))[name = string("concat_10_cast_fp16")]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_0_lstm_h0_reshaped_axes_0, x = concat_10_cast_fp16)[name = string("input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16")]; + tensor split_11_split_sizes_0 = const()[name = string("split_11_split_sizes_0"), val = tensor([1, 1])]; + int32 split_11_axis_0 = const()[name = string("split_11_axis_0"), val = int32(0)]; + tensor split_11_cast_fp16_0, tensor split_11_cast_fp16_1 = split(axis = split_11_axis_0, split_sizes = split_11_split_sizes_0, x = split_1_cast_fp16_0)[name = string("split_11_cast_fp16")]; + int32 concat_11_axis_0 = const()[name = string("concat_11_axis_0"), val = int32(2)]; + bool concat_11_interleave_0 = const()[name = string("concat_11_interleave_0"), val = bool(false)]; + tensor concat_11_cast_fp16 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (split_11_cast_fp16_0, split_11_cast_fp16_1))[name = string("concat_11_cast_fp16")]; + tensor input_25_lstm_layer_0_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_0_lstm_c0_reshaped_axes_0, x = concat_11_cast_fp16)[name = string("input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16")]; + string input_25_lstm_layer_0_direction_0 = const()[name = string("input_25_lstm_layer_0_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_0_output_sequence_0 = const()[name = string("input_25_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_0_cell_activation_0 = const()[name = string("input_25_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_0_activation_0 = const()[name = string("input_25_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_6_to_fp16 = const()[name = string("concat_6_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126144)))]; + tensor concat_7_to_fp16 = const()[name = string("concat_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187648)))]; + tensor add_0_to_fp16 = const()[name = string("add_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318784)))]; + tensor concat_8_to_fp16 = const()[name = string("concat_8_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319872)))]; + tensor concat_9_to_fp16 = const()[name = string("concat_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381376)))]; + tensor add_1_to_fp16 = const()[name = string("add_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512512)))]; + tensor input_23_batch_first_transpose_cast_fp16 = transpose(perm = input_23_batch_first_transpose_perm_0, x = input_23_cast_fp16)[name = string("transpose_5")]; + tensor input_25_lstm_layer_0_cast_fp16_0, tensor input_25_lstm_layer_0_cast_fp16_1, tensor input_25_lstm_layer_0_cast_fp16_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_7_to_fp16, weight_hh_back = concat_9_to_fp16, weight_ih = concat_6_to_fp16, weight_ih_back = concat_8_to_fp16, x = input_23_batch_first_transpose_cast_fp16)[name = string("input_25_lstm_layer_0_cast_fp16")]; + tensor split_20_split_sizes_0 = const()[name = string("split_20_split_sizes_0"), val = tensor([1, 1])]; + int32 split_20_axis_0 = const()[name = string("split_20_axis_0"), val = int32(0)]; + tensor split_20_cast_fp16_0, tensor split_20_cast_fp16_1 = split(axis = split_20_axis_0, split_sizes = split_20_split_sizes_0, x = split_0_cast_fp16_1)[name = string("split_20_cast_fp16")]; + int32 concat_20_axis_0 = const()[name = string("concat_20_axis_0"), val = int32(2)]; + bool concat_20_interleave_0 = const()[name = string("concat_20_interleave_0"), val = bool(false)]; + tensor concat_20_cast_fp16 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (split_20_cast_fp16_0, split_20_cast_fp16_1))[name = string("concat_20_cast_fp16")]; + tensor input_25_lstm_layer_1_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_1_lstm_h0_reshaped_axes_0, x = concat_20_cast_fp16)[name = string("input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16")]; + tensor split_21_split_sizes_0 = const()[name = string("split_21_split_sizes_0"), val = tensor([1, 1])]; + int32 split_21_axis_0 = const()[name = string("split_21_axis_0"), val = int32(0)]; + tensor split_21_cast_fp16_0, tensor split_21_cast_fp16_1 = split(axis = split_21_axis_0, split_sizes = split_21_split_sizes_0, x = split_1_cast_fp16_1)[name = string("split_21_cast_fp16")]; + int32 concat_21_axis_0 = const()[name = string("concat_21_axis_0"), val = int32(2)]; + bool concat_21_interleave_0 = const()[name = string("concat_21_interleave_0"), val = bool(false)]; + tensor concat_21_cast_fp16 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (split_21_cast_fp16_0, split_21_cast_fp16_1))[name = string("concat_21_cast_fp16")]; + tensor input_25_lstm_layer_1_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_1_lstm_c0_reshaped_axes_0, x = concat_21_cast_fp16)[name = string("input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16")]; + string input_25_lstm_layer_1_direction_0 = const()[name = string("input_25_lstm_layer_1_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_1_output_sequence_0 = const()[name = string("input_25_lstm_layer_1_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_1_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_1_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_1_cell_activation_0 = const()[name = string("input_25_lstm_layer_1_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_1_activation_0 = const()[name = string("input_25_lstm_layer_1_activation_0"), val = string("tanh")]; + tensor concat_16_to_fp16 = const()[name = string("concat_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513600)))]; + tensor concat_17_to_fp16 = const()[name = string("concat_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775808)))]; + tensor add_2_to_fp16 = const()[name = string("add_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906944)))]; + tensor concat_18_to_fp16 = const()[name = string("concat_18_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908032)))]; + tensor concat_19_to_fp16 = const()[name = string("concat_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1170240)))]; + tensor add_3_to_fp16 = const()[name = string("add_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1301376)))]; + tensor input_25_lstm_layer_1_cast_fp16_0, tensor input_25_lstm_layer_1_cast_fp16_1, tensor input_25_lstm_layer_1_cast_fp16_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2_to_fp16, bias_back = add_3_to_fp16, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16, initial_h = input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_17_to_fp16, weight_hh_back = concat_19_to_fp16, weight_ih = concat_16_to_fp16, weight_ih_back = concat_18_to_fp16, x = input_25_lstm_layer_0_cast_fp16_0)[name = string("input_25_lstm_layer_1_cast_fp16")]; + tensor split_30_split_sizes_0 = const()[name = string("split_30_split_sizes_0"), val = tensor([1, 1])]; + int32 split_30_axis_0 = const()[name = string("split_30_axis_0"), val = int32(0)]; + tensor split_30_cast_fp16_0, tensor split_30_cast_fp16_1 = split(axis = split_30_axis_0, split_sizes = split_30_split_sizes_0, x = split_0_cast_fp16_2)[name = string("split_30_cast_fp16")]; + int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(2)]; + bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)]; + tensor concat_30_cast_fp16 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (split_30_cast_fp16_0, split_30_cast_fp16_1))[name = string("concat_30_cast_fp16")]; + tensor input_25_lstm_layer_2_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_2_lstm_h0_reshaped_axes_0, x = concat_30_cast_fp16)[name = string("input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16")]; + tensor split_31_split_sizes_0 = const()[name = string("split_31_split_sizes_0"), val = tensor([1, 1])]; + int32 split_31_axis_0 = const()[name = string("split_31_axis_0"), val = int32(0)]; + tensor split_31_cast_fp16_0, tensor split_31_cast_fp16_1 = split(axis = split_31_axis_0, split_sizes = split_31_split_sizes_0, x = split_1_cast_fp16_2)[name = string("split_31_cast_fp16")]; + int32 concat_31_axis_0 = const()[name = string("concat_31_axis_0"), val = int32(2)]; + bool concat_31_interleave_0 = const()[name = string("concat_31_interleave_0"), val = bool(false)]; + tensor concat_31_cast_fp16 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (split_31_cast_fp16_0, split_31_cast_fp16_1))[name = string("concat_31_cast_fp16")]; + tensor input_25_lstm_layer_2_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_2_lstm_c0_reshaped_axes_0, x = concat_31_cast_fp16)[name = string("input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16")]; + string input_25_lstm_layer_2_direction_0 = const()[name = string("input_25_lstm_layer_2_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_2_output_sequence_0 = const()[name = string("input_25_lstm_layer_2_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_2_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_2_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_2_cell_activation_0 = const()[name = string("input_25_lstm_layer_2_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_2_activation_0 = const()[name = string("input_25_lstm_layer_2_activation_0"), val = string("tanh")]; + tensor concat_26_to_fp16 = const()[name = string("concat_26_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1302464)))]; + tensor concat_27_to_fp16 = const()[name = string("concat_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1564672)))]; + tensor add_4_to_fp16 = const()[name = string("add_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1695808)))]; + tensor concat_28_to_fp16 = const()[name = string("concat_28_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1696896)))]; + tensor concat_29_to_fp16 = const()[name = string("concat_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1959104)))]; + tensor add_5_to_fp16 = const()[name = string("add_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2090240)))]; + tensor input_25_lstm_layer_2_cast_fp16_0, tensor input_25_lstm_layer_2_cast_fp16_1, tensor input_25_lstm_layer_2_cast_fp16_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4_to_fp16, bias_back = add_5_to_fp16, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16, initial_h = input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_27_to_fp16, weight_hh_back = concat_29_to_fp16, weight_ih = concat_26_to_fp16, weight_ih_back = concat_28_to_fp16, x = input_25_lstm_layer_1_cast_fp16_0)[name = string("input_25_lstm_layer_2_cast_fp16")]; + tensor split_40_split_sizes_0 = const()[name = string("split_40_split_sizes_0"), val = tensor([1, 1])]; + int32 split_40_axis_0 = const()[name = string("split_40_axis_0"), val = int32(0)]; + tensor split_40_cast_fp16_0, tensor split_40_cast_fp16_1 = split(axis = split_40_axis_0, split_sizes = split_40_split_sizes_0, x = split_0_cast_fp16_3)[name = string("split_40_cast_fp16")]; + int32 concat_40_axis_0 = const()[name = string("concat_40_axis_0"), val = int32(2)]; + bool concat_40_interleave_0 = const()[name = string("concat_40_interleave_0"), val = bool(false)]; + tensor concat_40_cast_fp16 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (split_40_cast_fp16_0, split_40_cast_fp16_1))[name = string("concat_40_cast_fp16")]; + tensor input_25_batch_first_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_batch_first_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_batch_first_lstm_h0_reshaped_axes_0, x = concat_40_cast_fp16)[name = string("input_25_batch_first_lstm_h0_reshaped_cast_fp16")]; + tensor split_41_split_sizes_0 = const()[name = string("split_41_split_sizes_0"), val = tensor([1, 1])]; + int32 split_41_axis_0 = const()[name = string("split_41_axis_0"), val = int32(0)]; + tensor split_41_cast_fp16_0, tensor split_41_cast_fp16_1 = split(axis = split_41_axis_0, split_sizes = split_41_split_sizes_0, x = split_1_cast_fp16_3)[name = string("split_41_cast_fp16")]; + int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(2)]; + bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)]; + tensor concat_41_cast_fp16 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (split_41_cast_fp16_0, split_41_cast_fp16_1))[name = string("concat_41_cast_fp16")]; + tensor input_25_batch_first_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_batch_first_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_batch_first_lstm_c0_reshaped_axes_0, x = concat_41_cast_fp16)[name = string("input_25_batch_first_lstm_c0_reshaped_cast_fp16")]; + string input_25_batch_first_direction_0 = const()[name = string("input_25_batch_first_direction_0"), val = string("bidirectional")]; + bool input_25_batch_first_output_sequence_0 = const()[name = string("input_25_batch_first_output_sequence_0"), val = bool(true)]; + string input_25_batch_first_recurrent_activation_0 = const()[name = string("input_25_batch_first_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_batch_first_cell_activation_0 = const()[name = string("input_25_batch_first_cell_activation_0"), val = string("tanh")]; + string input_25_batch_first_activation_0 = const()[name = string("input_25_batch_first_activation_0"), val = string("tanh")]; + tensor concat_36_to_fp16 = const()[name = string("concat_36_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2091328)))]; + tensor concat_37_to_fp16 = const()[name = string("concat_37_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2353536)))]; + tensor add_6_to_fp16 = const()[name = string("add_6_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2484672)))]; + tensor concat_38_to_fp16 = const()[name = string("concat_38_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2485760)))]; + tensor concat_39_to_fp16 = const()[name = string("concat_39_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2747968)))]; + tensor add_7_to_fp16 = const()[name = string("add_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2879104)))]; + tensor input_25_batch_first_cast_fp16_0, tensor input_25_batch_first_cast_fp16_1, tensor input_25_batch_first_cast_fp16_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6_to_fp16, bias_back = add_7_to_fp16, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_batch_first_lstm_c0_reshaped_cast_fp16, initial_h = input_25_batch_first_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_37_to_fp16, weight_hh_back = concat_39_to_fp16, weight_ih = concat_36_to_fp16, weight_ih_back = concat_38_to_fp16, x = input_25_lstm_layer_2_cast_fp16_0)[name = string("input_25_batch_first_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([1, 0, 2])]; + tensor linear_0_weight_to_fp16 = const()[name = string("linear_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2880192)))]; + tensor linear_0_bias_to_fp16 = const()[name = string("linear_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2945792)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = input_25_batch_first_cast_fp16_0)[name = string("transpose_4")]; + tensor linear_0_cast_fp16 = linear(bias = linear_0_bias_to_fp16, weight = linear_0_weight_to_fp16, x = input_25_cast_fp16)[name = string("linear_0_cast_fp16")]; + fp32 var_220 = const()[name = string("op_220"), val = fp32(0x1.47ae14p-7)]; + tensor input_29_cast_fp16 = leaky_relu(alpha = var_220, x = linear_0_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor linear_1_weight_to_fp16 = const()[name = string("linear_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2946112)))]; + tensor linear_1_bias_to_fp16 = const()[name = string("linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2978944)))]; + tensor linear_1_cast_fp16 = linear(bias = linear_1_bias_to_fp16, weight = linear_1_weight_to_fp16, x = input_29_cast_fp16)[name = string("linear_1_cast_fp16")]; + fp32 var_225 = const()[name = string("op_225"), val = fp32(0x1.47ae14p-7)]; + tensor input_33_cast_fp16 = leaky_relu(alpha = var_225, x = linear_1_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor classifier_weight_to_fp16 = const()[name = string("classifier_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2979264)))]; + tensor classifier_bias_to_fp16 = const()[name = string("classifier_bias_to_fp16"), val = tensor([-0x1.01p+0, 0x1.67cp-2, 0x1.3d8p-1, 0x1.c8cp-2, -0x1.444p-2, -0x1.59p-1, -0x1.8fcp-2])]; + tensor linear_2_cast_fp16 = linear(bias = classifier_bias_to_fp16, weight = classifier_weight_to_fp16, x = input_33_cast_fp16)[name = string("linear_2_cast_fp16")]; + int32 var_231 = const()[name = string("op_231"), val = int32(-1)]; + tensor var_232_softmax_cast_fp16 = softmax(axis = var_231, x = linear_2_cast_fp16)[name = string("op_232_softmax_cast_fp16")]; + fp32 var_232_epsilon_0 = const()[name = string("op_232_epsilon_0"), val = fp32(0x1p-149)]; + tensor var_232_cast_fp16 = log(epsilon = var_232_epsilon_0, x = var_232_softmax_cast_fp16)[name = string("op_232_cast_fp16")]; + string var_232_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_232_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor output = cast(dtype = var_232_cast_fp16_to_fp32_dtype_0, x = var_232_cast_fp16)[name = string("cast_16")]; + } -> (output); +} \ No newline at end of file diff --git a/segmentation-3.0-b32-f16.mlmodelc/weights/weight.bin b/segmentation-3.0-b32-f16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..9226de6c7233bd392a439a63861af54c274e6f8d --- /dev/null +++ b/segmentation-3.0-b32-f16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0026d3483c74bc989fdd1649c5765ca5395235a6d140a698a2d87b95cddf56ae +size 2981120 diff --git a/segmentation-3.0-b32-w8a16.mlmodelc/analytics/coremldata.bin b/segmentation-3.0-b32-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..827a9792cce2a1bcffc65665657565d60ebc6304 --- /dev/null +++ b/segmentation-3.0-b32-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4fcdcd90f3129cc3eee5a970ad9851d0c735930b5ddf10ef73d917fd0517c9ec +size 243 diff --git a/segmentation-3.0-b32-w8a16.mlmodelc/coremldata.bin b/segmentation-3.0-b32-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ffdecc76a4c0f4341a034f04bd2cc48337eb0123 --- /dev/null +++ b/segmentation-3.0-b32-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:df181aeef8a195decfec56b1ff7283ddc163ef0a0e260c68af87927868573f37 +size 664 diff --git a/segmentation-3.0-b32-w8a16.mlmodelc/model.mil b/segmentation-3.0-b32-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..b80af75addb65f0eb950d721b30a372c6cf443fb --- /dev/null +++ b/segmentation-3.0-b32-w8a16.mlmodelc/model.mil @@ -0,0 +1,219 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor input) [FlexibleShapeInformation = tuple>>, tuple>>>>((("DefaultShapes", {{"input", [32, 1, 160000]}}), ("EnumeratedShapes", {{"047bedbd", {{"input", [24, 1, 160000]}}}, {"08383b0f", {{"input", [32, 1, 160000]}}}, {"146ea7a4", {{"input", [30, 1, 160000]}}}, {"14a6a9fa", {{"input", [27, 1, 160000]}}}, {"41d6af63", {{"input", [26, 1, 160000]}}}, {"4a349f6d", {{"input", [2, 1, 160000]}}}, {"4c2c6917", {{"input", [8, 1, 160000]}}}, {"4cb052b1", {{"input", [5, 1, 160000]}}}, {"4eab2425", {{"input", [23, 1, 160000]}}}, {"4f2b5bd2", {{"input", [14, 1, 160000]}}}, {"50b949f3", {{"input", [22, 1, 160000]}}}, {"5316ecea", {{"input", [1, 1, 160000]}}}, {"5d89881e", {{"input", [21, 1, 160000]}}}, {"693a1c76", {{"input", [19, 1, 160000]}}}, {"6ac4a6a4", {{"input", [29, 1, 160000]}}}, {"73f266d5", {{"input", [3, 1, 160000]}}}, {"73f43a1d", {{"input", [31, 1, 160000]}}}, {"7ee56056", {{"input", [18, 1, 160000]}}}, {"9035b52a", {{"input", [25, 1, 160000]}}}, {"94f7468c", {{"input", [20, 1, 160000]}}}, {"999a22b0", {{"input", [12, 1, 160000]}}}, {"9fad9511", {{"input", [4, 1, 160000]}}}, {"ab9dbd8c", {{"input", [9, 1, 160000]}}}, {"ae49a11c", {{"input", [16, 1, 160000]}}}, {"bf53b769", {{"input", [15, 1, 160000]}}}, {"c147bbba", {{"input", [11, 1, 160000]}}}, {"c32e6216", {{"input", [28, 1, 160000]}}}, {"d1a076a6", {{"input", [7, 1, 160000]}}}, {"dccf3050", {{"input", [17, 1, 160000]}}}, {"ef60c196", {{"input", [10, 1, 160000]}}}, {"fe5ae199", {{"input", [13, 1, 160000]}}}, {"ffc2aaa2", {{"input", [6, 1, 160000]}}}})))] { + tensor sincnet_wav_norm1d_bias = const()[name = string("sincnet_wav_norm1d_bias"), val = tensor([0x1.73505ep-5])]; + tensor sincnet_wav_norm1d_weight = const()[name = string("sincnet_wav_norm1d_weight"), val = tensor([0x1.43f862p-7])]; + tensor sincnet_norm1d_0_bias = const()[name = string("sincnet_norm1d_0_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor sincnet_norm1d_0_weight = const()[name = string("sincnet_norm1d_0_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448)))]; + tensor sincnet_conv1d_1_bias = const()[name = string("sincnet_conv1d_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(832)))]; + tensor sincnet_conv1d_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25216))))[name = string("sincnet_conv1d_1_weight_quantized")]; + tensor sincnet_norm1d_1_bias = const()[name = string("sincnet_norm1d_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25536)))]; + tensor sincnet_norm1d_1_weight = const()[name = string("sincnet_norm1d_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25856)))]; + tensor sincnet_conv1d_2_bias = const()[name = string("sincnet_conv1d_2_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26176)))]; + tensor sincnet_conv1d_2_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44608))))[name = string("sincnet_conv1d_2_weight_quantized")]; + tensor sincnet_norm1d_2_bias = const()[name = string("sincnet_norm1d_2_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44928)))]; + tensor sincnet_norm1d_2_weight = const()[name = string("sincnet_norm1d_2_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45248)))]; + tensor linear_0_bias = const()[name = string("linear_0_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45568)))]; + tensor linear_0_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78976))))[name = string("linear_0_weight_quantized")]; + tensor linear_1_bias = const()[name = string("linear_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79552)))]; + tensor linear_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96576))))[name = string("linear_1_weight_quantized")]; + tensor classifier_bias = const()[name = string("classifier_bias"), val = tensor([-0x1.00e888p+0, 0x1.67cb52p-2, 0x1.3d87fp-1, 0x1.c8aa8p-2, -0x1.445f5ep-2, -0x1.591274p-1, -0x1.8fb70ep-2])]; + tensor classifier_weight = const()[name = string("classifier_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97152)))]; + fp32 var_9 = const()[name = string("op_9"), val = fp32(0x1.47ae14p-7)]; + fp32 var_24 = const()[name = string("op_24"), val = fp32(0x1.4f8b58p-17)]; + tensor waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = string("waveform")]; + tensor filters_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120960))))[name = string("filters_quantized")]; + string outputs_pad_type_0 = const()[name = string("outputs_pad_type_0"), val = string("valid")]; + tensor outputs_strides_0 = const()[name = string("outputs_strides_0"), val = tensor([10])]; + tensor outputs_pad_0 = const()[name = string("outputs_pad_0"), val = tensor([0, 0])]; + tensor outputs_dilations_0 = const()[name = string("outputs_dilations_0"), val = tensor([1])]; + int32 outputs_groups_0 = const()[name = string("outputs_groups_0"), val = int32(1)]; + tensor outputs = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters_quantized, x = waveform)[name = string("outputs")]; + tensor input_1 = abs(x = outputs)[name = string("input_1")]; + tensor var_119 = const()[name = string("op_119"), val = tensor([3])]; + tensor var_120 = const()[name = string("op_120"), val = tensor([3])]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([0, 0])]; + bool input_3_ceil_mode_0 = const()[name = string("input_3_ceil_mode_0"), val = bool(false)]; + tensor input_3 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1)[name = string("input_3")]; + tensor input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = string("input_5")]; + tensor input_7 = leaky_relu(alpha = var_9, x = input_5)[name = string("input_7")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1])]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor input_9 = conv(bias = sincnet_conv1d_1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight_quantized, x = input_7)[name = string("input_9")]; + tensor var_135 = const()[name = string("op_135"), val = tensor([3])]; + tensor var_136 = const()[name = string("op_136"), val = tensor([3])]; + string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("custom")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0])]; + bool input_11_ceil_mode_0 = const()[name = string("input_11_ceil_mode_0"), val = bool(false)]; + tensor input_11 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9)[name = string("input_11")]; + tensor input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = string("input_13")]; + tensor input_15 = leaky_relu(alpha = var_9, x = input_13)[name = string("input_15")]; + string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")]; + tensor input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor([1])]; + tensor input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor([0, 0])]; + tensor input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor([1])]; + int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)]; + tensor input_17 = conv(bias = sincnet_conv1d_2_bias, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight_quantized, x = input_15)[name = string("input_17")]; + tensor var_151 = const()[name = string("op_151"), val = tensor([3])]; + tensor var_152 = const()[name = string("op_152"), val = tensor([3])]; + string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")]; + tensor input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor([0, 0])]; + bool input_19_ceil_mode_0 = const()[name = string("input_19_ceil_mode_0"), val = bool(false)]; + tensor input_19 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17)[name = string("input_19")]; + tensor input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = string("input_21")]; + tensor x = leaky_relu(alpha = var_9, x = input_21)[name = string("x")]; + tensor var_163 = const()[name = string("op_163"), val = tensor([0, 2, 1])]; + int32 var_172 = const()[name = string("op_172"), val = int32(128)]; + int32 var_173 = const()[name = string("op_173"), val = int32(8)]; + tensor input_23 = transpose(perm = var_163, x = x)[name = string("transpose_2")]; + tensor var_207_shape = shape(x = input_23)[name = string("op_207_shape")]; + int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)]; + bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)]; + int32 select_0 = const()[name = string("select_0"), val = int32(0)]; + int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)]; + int32 gather_0 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = select_0, validate_indices = gather_0_validate_indices_0, x = var_207_shape)[name = string("gather_0")]; + int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)]; + bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)]; + tensor concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_173, gather_0, var_172))[name = string("concat_0")]; + fp32 hx_1_value_0 = const()[name = string("hx_1_value_0"), val = fp32(0x0p+0)]; + tensor hx_1 = fill(shape = concat_0, value = hx_1_value_0)[name = string("hx_1")]; + tensor input_23_batch_first_transpose_perm_0 = const()[name = string("input_23_batch_first_transpose_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(4)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + tensor split_0_0, tensor split_0_1, tensor split_0_2, tensor split_0_3 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = hx_1)[name = string("split_0")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(4)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + tensor split_1_0, tensor split_1_1, tensor split_1_2, tensor split_1_3 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = hx_1)[name = string("split_1")]; + tensor add_0 = const()[name = string("add_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121344)))]; + tensor add_1 = const()[name = string("add_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123456)))]; + tensor concat_6_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156352))))[name = string("concat_6_quantized")]; + tensor concat_7_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224064))))[name = string("concat_7_quantized")]; + tensor concat_8_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256960))))[name = string("concat_8_quantized")]; + tensor concat_9_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324672))))[name = string("concat_9_quantized")]; + tensor split_10_split_sizes_0 = const()[name = string("split_10_split_sizes_0"), val = tensor([1, 1])]; + int32 split_10_axis_0 = const()[name = string("split_10_axis_0"), val = int32(0)]; + tensor split_10_0, tensor split_10_1 = split(axis = split_10_axis_0, split_sizes = split_10_split_sizes_0, x = split_0_0)[name = string("split_10")]; + int32 concat_10_axis_0 = const()[name = string("concat_10_axis_0"), val = int32(2)]; + bool concat_10_interleave_0 = const()[name = string("concat_10_interleave_0"), val = bool(false)]; + tensor concat_10 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (split_10_0, split_10_1))[name = string("concat_10")]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_h0_reshaped_axes_0, x = concat_10)[name = string("input_25_lstm_layer_0_lstm_h0_reshaped")]; + tensor split_11_split_sizes_0 = const()[name = string("split_11_split_sizes_0"), val = tensor([1, 1])]; + int32 split_11_axis_0 = const()[name = string("split_11_axis_0"), val = int32(0)]; + tensor split_11_0, tensor split_11_1 = split(axis = split_11_axis_0, split_sizes = split_11_split_sizes_0, x = split_1_0)[name = string("split_11")]; + int32 concat_11_axis_0 = const()[name = string("concat_11_axis_0"), val = int32(2)]; + bool concat_11_interleave_0 = const()[name = string("concat_11_interleave_0"), val = bool(false)]; + tensor concat_11 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (split_11_0, split_11_1))[name = string("concat_11")]; + tensor input_25_lstm_layer_0_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_0_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_c0_reshaped_axes_0, x = concat_11)[name = string("input_25_lstm_layer_0_lstm_c0_reshaped")]; + string input_25_lstm_layer_0_direction_0 = const()[name = string("input_25_lstm_layer_0_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_0_output_sequence_0 = const()[name = string("input_25_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_0_cell_activation_0 = const()[name = string("input_25_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_0_activation_0 = const()[name = string("input_25_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor input_23_batch_first_transpose = transpose(perm = input_23_batch_first_transpose_perm_0, x = input_23)[name = string("transpose_1")]; + tensor input_25_lstm_layer_0_0, tensor input_25_lstm_layer_0_1, tensor input_25_lstm_layer_0_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_c0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_7_quantized, weight_hh_back = concat_9_quantized, weight_ih = concat_6_quantized, weight_ih_back = concat_8_quantized, x = input_23_batch_first_transpose)[name = string("input_25_lstm_layer_0")]; + tensor add_2 = const()[name = string("add_2"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326784)))]; + tensor add_3 = const()[name = string("add_3"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328896)))]; + tensor concat_16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462144))))[name = string("concat_16_quantized")]; + tensor concat_17_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(464256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529856))))[name = string("concat_17_quantized")]; + tensor concat_18_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(663104))))[name = string("concat_18_quantized")]; + tensor concat_19_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(665216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(730816))))[name = string("concat_19_quantized")]; + tensor split_20_split_sizes_0 = const()[name = string("split_20_split_sizes_0"), val = tensor([1, 1])]; + int32 split_20_axis_0 = const()[name = string("split_20_axis_0"), val = int32(0)]; + tensor split_20_0, tensor split_20_1 = split(axis = split_20_axis_0, split_sizes = split_20_split_sizes_0, x = split_0_1)[name = string("split_20")]; + int32 concat_20_axis_0 = const()[name = string("concat_20_axis_0"), val = int32(2)]; + bool concat_20_interleave_0 = const()[name = string("concat_20_interleave_0"), val = bool(false)]; + tensor concat_20 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (split_20_0, split_20_1))[name = string("concat_20")]; + tensor input_25_lstm_layer_1_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_1_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_1_lstm_h0_reshaped_axes_0, x = concat_20)[name = string("input_25_lstm_layer_1_lstm_h0_reshaped")]; + tensor split_21_split_sizes_0 = const()[name = string("split_21_split_sizes_0"), val = tensor([1, 1])]; + int32 split_21_axis_0 = const()[name = string("split_21_axis_0"), val = int32(0)]; + tensor split_21_0, tensor split_21_1 = split(axis = split_21_axis_0, split_sizes = split_21_split_sizes_0, x = split_1_1)[name = string("split_21")]; + int32 concat_21_axis_0 = const()[name = string("concat_21_axis_0"), val = int32(2)]; + bool concat_21_interleave_0 = const()[name = string("concat_21_interleave_0"), val = bool(false)]; + tensor concat_21 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (split_21_0, split_21_1))[name = string("concat_21")]; + tensor input_25_lstm_layer_1_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_1_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_1_lstm_c0_reshaped_axes_0, x = concat_21)[name = string("input_25_lstm_layer_1_lstm_c0_reshaped")]; + string input_25_lstm_layer_1_direction_0 = const()[name = string("input_25_lstm_layer_1_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_1_output_sequence_0 = const()[name = string("input_25_lstm_layer_1_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_1_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_1_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_1_cell_activation_0 = const()[name = string("input_25_lstm_layer_1_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_1_activation_0 = const()[name = string("input_25_lstm_layer_1_activation_0"), val = string("tanh")]; + tensor input_25_lstm_layer_1_0, tensor input_25_lstm_layer_1_1, tensor input_25_lstm_layer_1_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_1_lstm_c0_reshaped, initial_h = input_25_lstm_layer_1_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_17_quantized, weight_hh_back = concat_19_quantized, weight_ih = concat_16_quantized, weight_ih_back = concat_18_quantized, x = input_25_lstm_layer_0_0)[name = string("input_25_lstm_layer_1")]; + tensor add_4 = const()[name = string("add_4"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(732928)))]; + tensor add_5 = const()[name = string("add_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(735040)))]; + tensor concat_26_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(737152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(868288))))[name = string("concat_26_quantized")]; + tensor concat_27_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(870400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936000))))[name = string("concat_27_quantized")]; + tensor concat_28_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(938112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069248))))[name = string("concat_28_quantized")]; + tensor concat_29_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1071360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136960))))[name = string("concat_29_quantized")]; + tensor split_30_split_sizes_0 = const()[name = string("split_30_split_sizes_0"), val = tensor([1, 1])]; + int32 split_30_axis_0 = const()[name = string("split_30_axis_0"), val = int32(0)]; + tensor split_30_0, tensor split_30_1 = split(axis = split_30_axis_0, split_sizes = split_30_split_sizes_0, x = split_0_2)[name = string("split_30")]; + int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(2)]; + bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)]; + tensor concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (split_30_0, split_30_1))[name = string("concat_30")]; + tensor input_25_lstm_layer_2_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_2_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_2_lstm_h0_reshaped_axes_0, x = concat_30)[name = string("input_25_lstm_layer_2_lstm_h0_reshaped")]; + tensor split_31_split_sizes_0 = const()[name = string("split_31_split_sizes_0"), val = tensor([1, 1])]; + int32 split_31_axis_0 = const()[name = string("split_31_axis_0"), val = int32(0)]; + tensor split_31_0, tensor split_31_1 = split(axis = split_31_axis_0, split_sizes = split_31_split_sizes_0, x = split_1_2)[name = string("split_31")]; + int32 concat_31_axis_0 = const()[name = string("concat_31_axis_0"), val = int32(2)]; + bool concat_31_interleave_0 = const()[name = string("concat_31_interleave_0"), val = bool(false)]; + tensor concat_31 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (split_31_0, split_31_1))[name = string("concat_31")]; + tensor input_25_lstm_layer_2_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_2_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_2_lstm_c0_reshaped_axes_0, x = concat_31)[name = string("input_25_lstm_layer_2_lstm_c0_reshaped")]; + string input_25_lstm_layer_2_direction_0 = const()[name = string("input_25_lstm_layer_2_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_2_output_sequence_0 = const()[name = string("input_25_lstm_layer_2_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_2_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_2_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_2_cell_activation_0 = const()[name = string("input_25_lstm_layer_2_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_2_activation_0 = const()[name = string("input_25_lstm_layer_2_activation_0"), val = string("tanh")]; + tensor input_25_lstm_layer_2_0, tensor input_25_lstm_layer_2_1, tensor input_25_lstm_layer_2_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_2_lstm_c0_reshaped, initial_h = input_25_lstm_layer_2_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_27_quantized, weight_hh_back = concat_29_quantized, weight_ih = concat_26_quantized, weight_ih_back = concat_28_quantized, x = input_25_lstm_layer_1_0)[name = string("input_25_lstm_layer_2")]; + tensor add_6 = const()[name = string("add_6"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1139072)))]; + tensor add_7 = const()[name = string("add_7"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1141184)))]; + tensor concat_36_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1143296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274432))))[name = string("concat_36_quantized")]; + tensor concat_37_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1276544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1342144))))[name = string("concat_37_quantized")]; + tensor concat_38_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1344256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475392))))[name = string("concat_38_quantized")]; + tensor concat_39_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1477504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1543104))))[name = string("concat_39_quantized")]; + tensor split_40_split_sizes_0 = const()[name = string("split_40_split_sizes_0"), val = tensor([1, 1])]; + int32 split_40_axis_0 = const()[name = string("split_40_axis_0"), val = int32(0)]; + tensor split_40_0, tensor split_40_1 = split(axis = split_40_axis_0, split_sizes = split_40_split_sizes_0, x = split_0_3)[name = string("split_40")]; + int32 concat_40_axis_0 = const()[name = string("concat_40_axis_0"), val = int32(2)]; + bool concat_40_interleave_0 = const()[name = string("concat_40_interleave_0"), val = bool(false)]; + tensor concat_40 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (split_40_0, split_40_1))[name = string("concat_40")]; + tensor input_25_batch_first_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_batch_first_lstm_h0_reshaped = squeeze(axes = input_25_batch_first_lstm_h0_reshaped_axes_0, x = concat_40)[name = string("input_25_batch_first_lstm_h0_reshaped")]; + tensor split_41_split_sizes_0 = const()[name = string("split_41_split_sizes_0"), val = tensor([1, 1])]; + int32 split_41_axis_0 = const()[name = string("split_41_axis_0"), val = int32(0)]; + tensor split_41_0, tensor split_41_1 = split(axis = split_41_axis_0, split_sizes = split_41_split_sizes_0, x = split_1_3)[name = string("split_41")]; + int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(2)]; + bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)]; + tensor concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (split_41_0, split_41_1))[name = string("concat_41")]; + tensor input_25_batch_first_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_batch_first_lstm_c0_reshaped = squeeze(axes = input_25_batch_first_lstm_c0_reshaped_axes_0, x = concat_41)[name = string("input_25_batch_first_lstm_c0_reshaped")]; + string input_25_batch_first_direction_0 = const()[name = string("input_25_batch_first_direction_0"), val = string("bidirectional")]; + bool input_25_batch_first_output_sequence_0 = const()[name = string("input_25_batch_first_output_sequence_0"), val = bool(true)]; + string input_25_batch_first_recurrent_activation_0 = const()[name = string("input_25_batch_first_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_batch_first_cell_activation_0 = const()[name = string("input_25_batch_first_cell_activation_0"), val = string("tanh")]; + string input_25_batch_first_activation_0 = const()[name = string("input_25_batch_first_activation_0"), val = string("tanh")]; + tensor input_25_batch_first_0, tensor input_25_batch_first_1, tensor input_25_batch_first_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_batch_first_lstm_c0_reshaped, initial_h = input_25_batch_first_lstm_h0_reshaped, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_37_quantized, weight_hh_back = concat_39_quantized, weight_ih = concat_36_quantized, weight_ih_back = concat_38_quantized, x = input_25_lstm_layer_2_0)[name = string("input_25_batch_first")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([1, 0, 2])]; + tensor input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = string("transpose_0")]; + tensor input_27 = linear(bias = linear_0_bias, weight = linear_0_weight_quantized, x = input_25)[name = string("linear_0")]; + fp32 var_220 = const()[name = string("op_220"), val = fp32(0x1.47ae14p-7)]; + tensor input_29 = leaky_relu(alpha = var_220, x = input_27)[name = string("input_29")]; + tensor input_31 = linear(bias = linear_1_bias, weight = linear_1_weight_quantized, x = input_29)[name = string("linear_1")]; + fp32 var_225 = const()[name = string("op_225"), val = fp32(0x1.47ae14p-7)]; + tensor input_33 = leaky_relu(alpha = var_225, x = input_31)[name = string("input_33")]; + tensor input_1_1 = linear(bias = classifier_bias, weight = classifier_weight, x = input_33)[name = string("linear_2")]; + int32 var_231 = const()[name = string("op_231"), val = int32(-1)]; + tensor var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = string("op_232_softmax")]; + fp32 var_232_epsilon_0 = const()[name = string("op_232_epsilon_0"), val = fp32(0x1p-149)]; + tensor output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = string("op_232")]; + } -> (output); +} \ No newline at end of file diff --git a/segmentation-3.0-b32-w8a16.mlmodelc/weights/weight.bin b/segmentation-3.0-b32-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..f594726b766cd78b580c8102512c06d145659fab --- /dev/null +++ b/segmentation-3.0-b32-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b5c162f15773b0417411bafc50141870d504ca9a3a896211dae643d7787de87 +size 1545216 diff --git a/segmentation-3.0-b56-w8a16.mlmodelc/analytics/coremldata.bin b/segmentation-3.0-b56-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b0d2450adbc07148ad9239ce6c305adbb45ae9ac --- /dev/null +++ b/segmentation-3.0-b56-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a380f80839856a96f4dfe49a356b6a17a327fdce53e91da5e4497a1d5e549b7e +size 243 diff --git a/segmentation-3.0-b56-w8a16.mlmodelc/coremldata.bin b/segmentation-3.0-b56-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..1b3d351200a5d149484d14a3868be66eb140609d --- /dev/null +++ b/segmentation-3.0-b56-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ea7ff18bfaeed8988151710d07ea1ab1f59aa48427393e073ea2d88ea50a87e0 +size 375 diff --git a/segmentation-3.0-b56-w8a16.mlmodelc/model.mil b/segmentation-3.0-b56-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a4fbf2397935856cfbbc4be09b43f6f82ff99a61 --- /dev/null +++ b/segmentation-3.0-b56-w8a16.mlmodelc/model.mil @@ -0,0 +1,135 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor input) { + tensor sincnet_wav_norm1d_bias = const()[name = tensor("sincnet_wav_norm1d_bias"), val = tensor([0x1.73505ep-5])]; + tensor sincnet_wav_norm1d_weight = const()[name = tensor("sincnet_wav_norm1d_weight"), val = tensor([0x1.43f862p-7])]; + tensor sincnet_norm1d_0_bias = const()[name = tensor("sincnet_norm1d_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor sincnet_norm1d_0_weight = const()[name = tensor("sincnet_norm1d_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; + tensor sincnet_conv1d_1_bias = const()[name = tensor("sincnet_conv1d_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832)))]; + tensor sincnet_conv1d_1_weight_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("sincnet_conv1d_1_weight_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1152))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25344))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25216)))]; + tensor sincnet_norm1d_1_bias = const()[name = tensor("sincnet_norm1d_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25664)))]; + tensor sincnet_norm1d_1_weight = const()[name = tensor("sincnet_norm1d_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25984)))]; + tensor sincnet_conv1d_2_bias = const()[name = tensor("sincnet_conv1d_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26304)))]; + tensor sincnet_conv1d_2_weight_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("sincnet_conv1d_2_weight_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26624))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44864))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44736)))]; + tensor sincnet_norm1d_2_bias = const()[name = tensor("sincnet_norm1d_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45184)))]; + tensor sincnet_norm1d_2_weight = const()[name = tensor("sincnet_norm1d_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45504)))]; + tensor linear_0_bias = const()[name = tensor("linear_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45824)))]; + tensor linear_0_weight_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("linear_0_weight_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46400))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79424))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79232)))]; + tensor linear_1_bias = const()[name = tensor("linear_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80000)))]; + tensor linear_1_weight_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("linear_1_weight_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80576))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97024))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79232)))]; + tensor classifier_bias = const()[name = tensor("classifier_bias"), val = tensor([-0x1.00e888p+0, 0x1.67cb52p-2, 0x1.3d87fp-1, 0x1.c8aa8p-2, -0x1.445f5ep-2, -0x1.591274p-1, -0x1.8fb70ep-2])]; + tensor classifier_weight_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("classifier_weight_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97600))), scale = tensor([0x1.a91b44p-7, 0x1.26991cp-7, 0x1.8bd9b2p-7, 0x1.0eb8bp-7, 0x1.4a7844p-7, 0x1.3ccf28p-6, 0x1.5ebed6p-7]), zero_point = tensor([0, 0, 0, 0, 0, 0, 0])]; + tensor var_9 = const()[name = tensor("op_9"), val = tensor(0x1.47ae14p-7)]; + tensor var_24 = const()[name = tensor("op_24"), val = tensor(0x1.4f8b58p-17)]; + tensor waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = tensor("waveform")]; + tensor filters_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("filters_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98560))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118912))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118720)))]; + tensor outputs_pad_type_0 = const()[name = tensor("outputs_pad_type_0"), val = tensor("valid")]; + tensor outputs_strides_0 = const()[name = tensor("outputs_strides_0"), val = tensor([10])]; + tensor outputs_pad_0 = const()[name = tensor("outputs_pad_0"), val = tensor([0, 0])]; + tensor outputs_dilations_0 = const()[name = tensor("outputs_dilations_0"), val = tensor([1])]; + tensor outputs_groups_0 = const()[name = tensor("outputs_groups_0"), val = tensor(1)]; + tensor outputs = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters_quantized, x = waveform)[name = tensor("outputs")]; + tensor input_1 = abs(x = outputs)[name = tensor("input_1")]; + tensor var_119 = const()[name = tensor("op_119"), val = tensor([3])]; + tensor var_120 = const()[name = tensor("op_120"), val = tensor([3])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0])]; + tensor input_3_ceil_mode_0 = const()[name = tensor("input_3_ceil_mode_0"), val = tensor(false)]; + tensor input_3 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1)[name = tensor("input_3")]; + tensor input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = tensor("input_5")]; + tensor input_7 = leaky_relu(alpha = var_9, x = input_5)[name = tensor("input_7")]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("valid")]; + tensor input_9_strides_0 = const()[name = tensor("input_9_strides_0"), val = tensor([1])]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0])]; + tensor input_9_dilations_0 = const()[name = tensor("input_9_dilations_0"), val = tensor([1])]; + tensor input_9_groups_0 = const()[name = tensor("input_9_groups_0"), val = tensor(1)]; + tensor input_9 = conv(bias = sincnet_conv1d_1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight_quantized, x = input_7)[name = tensor("input_9")]; + tensor var_135 = const()[name = tensor("op_135"), val = tensor([3])]; + tensor var_136 = const()[name = tensor("op_136"), val = tensor([3])]; + tensor input_11_pad_type_0 = const()[name = tensor("input_11_pad_type_0"), val = tensor("custom")]; + tensor input_11_pad_0 = const()[name = tensor("input_11_pad_0"), val = tensor([0, 0])]; + tensor input_11_ceil_mode_0 = const()[name = tensor("input_11_ceil_mode_0"), val = tensor(false)]; + tensor input_11 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9)[name = tensor("input_11")]; + tensor input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = tensor("input_13")]; + tensor input_15 = leaky_relu(alpha = var_9, x = input_13)[name = tensor("input_15")]; + tensor input_17_pad_type_0 = const()[name = tensor("input_17_pad_type_0"), val = tensor("valid")]; + tensor input_17_strides_0 = const()[name = tensor("input_17_strides_0"), val = tensor([1])]; + tensor input_17_pad_0 = const()[name = tensor("input_17_pad_0"), val = tensor([0, 0])]; + tensor input_17_dilations_0 = const()[name = tensor("input_17_dilations_0"), val = tensor([1])]; + tensor input_17_groups_0 = const()[name = tensor("input_17_groups_0"), val = tensor(1)]; + tensor input_17 = conv(bias = sincnet_conv1d_2_bias, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight_quantized, x = input_15)[name = tensor("input_17")]; + tensor var_151 = const()[name = tensor("op_151"), val = tensor([3])]; + tensor var_152 = const()[name = tensor("op_152"), val = tensor([3])]; + tensor input_19_pad_type_0 = const()[name = tensor("input_19_pad_type_0"), val = tensor("custom")]; + tensor input_19_pad_0 = const()[name = tensor("input_19_pad_0"), val = tensor([0, 0])]; + tensor input_19_ceil_mode_0 = const()[name = tensor("input_19_ceil_mode_0"), val = tensor(false)]; + tensor input_19 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17)[name = tensor("input_19")]; + tensor input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = tensor("input_21")]; + tensor x = leaky_relu(alpha = var_9, x = input_21)[name = tensor("x")]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([2, 0, 1])]; + tensor add_0 = const()[name = tensor("add_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119296)))]; + tensor add_1 = const()[name = tensor("add_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121408)))]; + tensor concat_4_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_4_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123520))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154880))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_5_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_5_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156992))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222592))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_6_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_6_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224704))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(255488))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_7_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_7_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257600))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323200))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("input_25_lstm_layer_0_lstm_h0_reshaped_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325312))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(339840))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(339712)))]; + tensor input_25_lstm_layer_0_direction_0 = const()[name = tensor("input_25_lstm_layer_0_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_0_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_0_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_0_activation_0 = const()[name = tensor("input_25_lstm_layer_0_activation_0"), val = tensor("tanh")]; + tensor transpose_4 = transpose(perm = transpose_4_perm_0, x = x)[name = tensor("transpose_1")]; + tensor input_25_lstm_layer_0_0, tensor input_25_lstm_layer_0_1, tensor input_25_lstm_layer_0_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_5_quantized, weight_hh_back = concat_7_quantized, weight_ih = concat_4_quantized, weight_ih_back = concat_6_quantized, x = transpose_4)[name = tensor("input_25_lstm_layer_0")]; + tensor add_2 = const()[name = tensor("add_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(340160)))]; + tensor add_3 = const()[name = tensor("add_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(342272)))]; + tensor concat_14_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_14_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344384))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475520))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_15_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_15_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(477632))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543232))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545344))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(676480))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_17_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_17_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678592))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(744192))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor input_25_lstm_layer_1_direction_0 = const()[name = tensor("input_25_lstm_layer_1_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_1_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_1_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_1_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_1_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_1_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_1_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_1_activation_0 = const()[name = tensor("input_25_lstm_layer_1_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_1_0, tensor input_25_lstm_layer_1_1, tensor input_25_lstm_layer_1_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_15_quantized, weight_hh_back = concat_17_quantized, weight_ih = concat_14_quantized, weight_ih_back = concat_16_quantized, x = input_25_lstm_layer_0_0)[name = tensor("input_25_lstm_layer_1")]; + tensor add_4 = const()[name = tensor("add_4"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(746304)))]; + tensor add_5 = const()[name = tensor("add_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(748416)))]; + tensor concat_24_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_24_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(750528))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(881664))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_25_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_25_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(883776))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(949376))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_26_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_26_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(951488))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1082624))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_27_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_27_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1084736))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1150336))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor input_25_lstm_layer_2_direction_0 = const()[name = tensor("input_25_lstm_layer_2_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_2_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_2_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_2_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_2_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_2_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_2_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_2_activation_0 = const()[name = tensor("input_25_lstm_layer_2_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_2_0, tensor input_25_lstm_layer_2_1, tensor input_25_lstm_layer_2_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_25_quantized, weight_hh_back = concat_27_quantized, weight_ih = concat_24_quantized, weight_ih_back = concat_26_quantized, x = input_25_lstm_layer_1_0)[name = tensor("input_25_lstm_layer_2")]; + tensor add_6 = const()[name = tensor("add_6"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1152448)))]; + tensor add_7 = const()[name = tensor("add_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1154560)))]; + tensor concat_34_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_34_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1156672))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1287808))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_35_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_35_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1289920))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1355520))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_36_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_36_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1357632))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1488768))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_37_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_37_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1490880))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1556480))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor input_25_batch_first_direction_0 = const()[name = tensor("input_25_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor input_25_batch_first_output_sequence_0 = const()[name = tensor("input_25_batch_first_output_sequence_0"), val = tensor(true)]; + tensor input_25_batch_first_recurrent_activation_0 = const()[name = tensor("input_25_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_batch_first_cell_activation_0 = const()[name = tensor("input_25_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_batch_first_activation_0 = const()[name = tensor("input_25_batch_first_activation_0"), val = tensor("tanh")]; + tensor input_25_batch_first_0, tensor input_25_batch_first_1, tensor input_25_batch_first_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_35_quantized, weight_hh_back = concat_37_quantized, weight_ih = concat_34_quantized, weight_ih_back = concat_36_quantized, x = input_25_lstm_layer_2_0)[name = tensor("input_25_batch_first")]; + tensor input_25_perm_0 = const()[name = tensor("input_25_perm_0"), val = tensor([1, 0, 2])]; + tensor input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = tensor("transpose_0")]; + tensor input_27 = linear(bias = linear_0_bias, weight = linear_0_weight_quantized, x = input_25)[name = tensor("linear_0")]; + tensor var_220 = const()[name = tensor("op_220"), val = tensor(0x1.47ae14p-7)]; + tensor input_29 = leaky_relu(alpha = var_220, x = input_27)[name = tensor("input_29")]; + tensor input_31 = linear(bias = linear_1_bias, weight = linear_1_weight_quantized, x = input_29)[name = tensor("linear_1")]; + tensor var_225 = const()[name = tensor("op_225"), val = tensor(0x1.47ae14p-7)]; + tensor input_33 = leaky_relu(alpha = var_225, x = input_31)[name = tensor("input_33")]; + tensor input_1_1 = linear(bias = classifier_bias, weight = classifier_weight_quantized, x = input_33)[name = tensor("linear_2")]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor(-1)]; + tensor var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = tensor("op_232_softmax")]; + tensor var_232_epsilon_0 = const()[name = tensor("op_232_epsilon_0"), val = tensor(0x1p-149)]; + tensor output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = tensor("op_232")]; + } -> (output); +} \ No newline at end of file diff --git a/segmentation-3.0-b56-w8a16.mlmodelc/weights/weight.bin b/segmentation-3.0-b56-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..cce85c3383523ae2d50022d132c66f25865ce6a7 --- /dev/null +++ b/segmentation-3.0-b56-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c41c0bea2895bf0e51e21b9b9ac65946277bc74b8b3038131af0152740d0fbc4 +size 1558592 diff --git a/segmentation-3.0-b56.mlmodelc/analytics/coremldata.bin b/segmentation-3.0-b56.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..24254dd7333ed3ae663fe387f340bd87e809a052 --- /dev/null +++ b/segmentation-3.0-b56.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a628bf7c8408d287fc40dc93651dbbc635f46d96e670f35c234682086c7748fc +size 243 diff --git a/segmentation-3.0-b56.mlmodelc/coremldata.bin b/segmentation-3.0-b56.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..45e214c6c92225994ffae92034b132e5be5fcd2c --- /dev/null +++ b/segmentation-3.0-b56.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d0905cc7f92512035ceea03f69095195d7e62b65d6bb13a126d58d2f7792864c +size 150 diff --git a/segmentation-3.0-b56.mlmodelc/model.mil b/segmentation-3.0-b56.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..fa9b6f428ee52017e1c240eb870f3f15bcd3f776 --- /dev/null +++ b/segmentation-3.0-b56.mlmodelc/model.mil @@ -0,0 +1,135 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor input) { + tensor sincnet_wav_norm1d_bias = const()[name = tensor("sincnet_wav_norm1d_bias"), val = tensor([0x1.73505ep-5])]; + tensor sincnet_wav_norm1d_weight = const()[name = tensor("sincnet_wav_norm1d_weight"), val = tensor([0x1.43f862p-7])]; + tensor sincnet_norm1d_0_bias = const()[name = tensor("sincnet_norm1d_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor sincnet_norm1d_0_weight = const()[name = tensor("sincnet_norm1d_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; + tensor sincnet_conv1d_1_bias = const()[name = tensor("sincnet_conv1d_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832)))]; + tensor sincnet_conv1d_1_weight = const()[name = tensor("sincnet_conv1d_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1152)))]; + tensor sincnet_norm1d_1_bias = const()[name = tensor("sincnet_norm1d_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97216)))]; + tensor sincnet_norm1d_1_weight = const()[name = tensor("sincnet_norm1d_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97536)))]; + tensor sincnet_conv1d_2_bias = const()[name = tensor("sincnet_conv1d_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97856)))]; + tensor sincnet_conv1d_2_weight = const()[name = tensor("sincnet_conv1d_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98176)))]; + tensor sincnet_norm1d_2_bias = const()[name = tensor("sincnet_norm1d_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170240)))]; + tensor sincnet_norm1d_2_weight = const()[name = tensor("sincnet_norm1d_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170560)))]; + tensor linear_0_bias = const()[name = tensor("linear_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170880)))]; + tensor linear_0_weight = const()[name = tensor("linear_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171456)))]; + tensor linear_1_bias = const()[name = tensor("linear_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302592)))]; + tensor linear_1_weight = const()[name = tensor("linear_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303168)))]; + tensor classifier_bias = const()[name = tensor("classifier_bias"), val = tensor([-0x1.00e888p+0, 0x1.67cb52p-2, 0x1.3d87fp-1, 0x1.c8aa8p-2, -0x1.445f5ep-2, -0x1.591274p-1, -0x1.8fb70ep-2])]; + tensor classifier_weight = const()[name = tensor("classifier_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(368768)))]; + tensor var_9 = const()[name = tensor("op_9"), val = tensor(0x1.47ae14p-7)]; + tensor var_24 = const()[name = tensor("op_24"), val = tensor(0x1.4f8b58p-17)]; + tensor waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = tensor("waveform")]; + tensor filters = const()[name = tensor("filters"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372416)))]; + tensor outputs_pad_type_0 = const()[name = tensor("outputs_pad_type_0"), val = tensor("valid")]; + tensor outputs_strides_0 = const()[name = tensor("outputs_strides_0"), val = tensor([10])]; + tensor outputs_pad_0 = const()[name = tensor("outputs_pad_0"), val = tensor([0, 0])]; + tensor outputs_dilations_0 = const()[name = tensor("outputs_dilations_0"), val = tensor([1])]; + tensor outputs_groups_0 = const()[name = tensor("outputs_groups_0"), val = tensor(1)]; + tensor outputs = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters, x = waveform)[name = tensor("outputs")]; + tensor input_1 = abs(x = outputs)[name = tensor("input_1")]; + tensor var_119 = const()[name = tensor("op_119"), val = tensor([3])]; + tensor var_120 = const()[name = tensor("op_120"), val = tensor([3])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0])]; + tensor input_3_ceil_mode_0 = const()[name = tensor("input_3_ceil_mode_0"), val = tensor(false)]; + tensor input_3 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1)[name = tensor("input_3")]; + tensor input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = tensor("input_5")]; + tensor input_7 = leaky_relu(alpha = var_9, x = input_5)[name = tensor("input_7")]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("valid")]; + tensor input_9_strides_0 = const()[name = tensor("input_9_strides_0"), val = tensor([1])]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0])]; + tensor input_9_dilations_0 = const()[name = tensor("input_9_dilations_0"), val = tensor([1])]; + tensor input_9_groups_0 = const()[name = tensor("input_9_groups_0"), val = tensor(1)]; + tensor input_9 = conv(bias = sincnet_conv1d_1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight, x = input_7)[name = tensor("input_9")]; + tensor var_135 = const()[name = tensor("op_135"), val = tensor([3])]; + tensor var_136 = const()[name = tensor("op_136"), val = tensor([3])]; + tensor input_11_pad_type_0 = const()[name = tensor("input_11_pad_type_0"), val = tensor("custom")]; + tensor input_11_pad_0 = const()[name = tensor("input_11_pad_0"), val = tensor([0, 0])]; + tensor input_11_ceil_mode_0 = const()[name = tensor("input_11_ceil_mode_0"), val = tensor(false)]; + tensor input_11 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9)[name = tensor("input_11")]; + tensor input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = tensor("input_13")]; + tensor input_15 = leaky_relu(alpha = var_9, x = input_13)[name = tensor("input_15")]; + tensor input_17_pad_type_0 = const()[name = tensor("input_17_pad_type_0"), val = tensor("valid")]; + tensor input_17_strides_0 = const()[name = tensor("input_17_strides_0"), val = tensor([1])]; + tensor input_17_pad_0 = const()[name = tensor("input_17_pad_0"), val = tensor([0, 0])]; + tensor input_17_dilations_0 = const()[name = tensor("input_17_dilations_0"), val = tensor([1])]; + tensor input_17_groups_0 = const()[name = tensor("input_17_groups_0"), val = tensor(1)]; + tensor input_17 = conv(bias = sincnet_conv1d_2_bias, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight, x = input_15)[name = tensor("input_17")]; + tensor var_151 = const()[name = tensor("op_151"), val = tensor([3])]; + tensor var_152 = const()[name = tensor("op_152"), val = tensor([3])]; + tensor input_19_pad_type_0 = const()[name = tensor("input_19_pad_type_0"), val = tensor("custom")]; + tensor input_19_pad_0 = const()[name = tensor("input_19_pad_0"), val = tensor([0, 0])]; + tensor input_19_ceil_mode_0 = const()[name = tensor("input_19_ceil_mode_0"), val = tensor(false)]; + tensor input_19 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17)[name = tensor("input_19")]; + tensor input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = tensor("input_21")]; + tensor x = leaky_relu(alpha = var_9, x = input_21)[name = tensor("x")]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([2, 0, 1])]; + tensor add_0 = const()[name = tensor("add_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(452800)))]; + tensor add_1 = const()[name = tensor("add_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454912)))]; + tensor concat_4 = const()[name = tensor("concat_4"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(457024)))]; + tensor concat_5 = const()[name = tensor("concat_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(579968)))]; + tensor concat_6 = const()[name = tensor("concat_6"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(842176)))]; + tensor concat_7 = const()[name = tensor("concat_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(965120)))]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped = const()[name = tensor("input_25_lstm_layer_0_lstm_h0_reshaped"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227328)))]; + tensor input_25_lstm_layer_0_direction_0 = const()[name = tensor("input_25_lstm_layer_0_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_0_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_0_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_0_activation_0 = const()[name = tensor("input_25_lstm_layer_0_activation_0"), val = tensor("tanh")]; + tensor transpose_4 = transpose(perm = transpose_4_perm_0, x = x)[name = tensor("transpose_6")]; + tensor input_25_lstm_layer_0_0, tensor input_25_lstm_layer_0_1, tensor input_25_lstm_layer_0_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_5, weight_hh_back = concat_7, weight_ih = concat_4, weight_ih_back = concat_6, x = transpose_4)[name = tensor("input_25_lstm_layer_0")]; + tensor add_2 = const()[name = tensor("add_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1284736)))]; + tensor add_3 = const()[name = tensor("add_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1286848)))]; + tensor concat_14 = const()[name = tensor("concat_14"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1288960)))]; + tensor concat_15 = const()[name = tensor("concat_15"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1813312)))]; + tensor concat_16 = const()[name = tensor("concat_16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2075520)))]; + tensor concat_17 = const()[name = tensor("concat_17"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2599872)))]; + tensor input_25_lstm_layer_1_direction_0 = const()[name = tensor("input_25_lstm_layer_1_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_1_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_1_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_1_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_1_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_1_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_1_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_1_activation_0 = const()[name = tensor("input_25_lstm_layer_1_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_1_0, tensor input_25_lstm_layer_1_1, tensor input_25_lstm_layer_1_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_15, weight_hh_back = concat_17, weight_ih = concat_14, weight_ih_back = concat_16, x = input_25_lstm_layer_0_0)[name = tensor("input_25_lstm_layer_1")]; + tensor add_4 = const()[name = tensor("add_4"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2862080)))]; + tensor add_5 = const()[name = tensor("add_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2864192)))]; + tensor concat_24 = const()[name = tensor("concat_24"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2866304)))]; + tensor concat_25 = const()[name = tensor("concat_25"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3390656)))]; + tensor concat_26 = const()[name = tensor("concat_26"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3652864)))]; + tensor concat_27 = const()[name = tensor("concat_27"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4177216)))]; + tensor input_25_lstm_layer_2_direction_0 = const()[name = tensor("input_25_lstm_layer_2_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_2_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_2_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_2_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_2_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_2_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_2_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_2_activation_0 = const()[name = tensor("input_25_lstm_layer_2_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_2_0, tensor input_25_lstm_layer_2_1, tensor input_25_lstm_layer_2_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_25, weight_hh_back = concat_27, weight_ih = concat_24, weight_ih_back = concat_26, x = input_25_lstm_layer_1_0)[name = tensor("input_25_lstm_layer_2")]; + tensor add_6 = const()[name = tensor("add_6"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4439424)))]; + tensor add_7 = const()[name = tensor("add_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4441536)))]; + tensor concat_34 = const()[name = tensor("concat_34"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4443648)))]; + tensor concat_35 = const()[name = tensor("concat_35"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4968000)))]; + tensor concat_36 = const()[name = tensor("concat_36"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5230208)))]; + tensor concat_37 = const()[name = tensor("concat_37"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5754560)))]; + tensor input_25_batch_first_direction_0 = const()[name = tensor("input_25_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor input_25_batch_first_output_sequence_0 = const()[name = tensor("input_25_batch_first_output_sequence_0"), val = tensor(true)]; + tensor input_25_batch_first_recurrent_activation_0 = const()[name = tensor("input_25_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_batch_first_cell_activation_0 = const()[name = tensor("input_25_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_batch_first_activation_0 = const()[name = tensor("input_25_batch_first_activation_0"), val = tensor("tanh")]; + tensor input_25_batch_first_0, tensor input_25_batch_first_1, tensor input_25_batch_first_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_35, weight_hh_back = concat_37, weight_ih = concat_34, weight_ih_back = concat_36, x = input_25_lstm_layer_2_0)[name = tensor("input_25_batch_first")]; + tensor input_25_perm_0 = const()[name = tensor("input_25_perm_0"), val = tensor([1, 0, 2])]; + tensor input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = tensor("transpose_5")]; + tensor input_27 = linear(bias = linear_0_bias, weight = linear_0_weight, x = input_25)[name = tensor("linear_0")]; + tensor var_220 = const()[name = tensor("op_220"), val = tensor(0x1.47ae14p-7)]; + tensor input_29 = leaky_relu(alpha = var_220, x = input_27)[name = tensor("input_29")]; + tensor input_31 = linear(bias = linear_1_bias, weight = linear_1_weight, x = input_29)[name = tensor("linear_1")]; + tensor var_225 = const()[name = tensor("op_225"), val = tensor(0x1.47ae14p-7)]; + tensor input_33 = leaky_relu(alpha = var_225, x = input_31)[name = tensor("input_33")]; + tensor input_1_1 = linear(bias = classifier_bias, weight = classifier_weight, x = input_33)[name = tensor("linear_2")]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor(-1)]; + tensor var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = tensor("op_232_softmax")]; + tensor var_232_epsilon_0 = const()[name = tensor("op_232_epsilon_0"), val = tensor(0x1p-149)]; + tensor output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = tensor("op_232")]; + } -> (output); +} \ No newline at end of file diff --git a/segmentation-3.0-b56.mlmodelc/weights/weight.bin b/segmentation-3.0-b56.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..6df245c7229c08c2536df6b4f0a18c4967fdb8bc --- /dev/null +++ b/segmentation-3.0-b56.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:adac89b733012f15aa553174b983961eb9259b4ddd1c75114b6b061b066b33f3 +size 6016768 diff --git a/segmentation-3.0-b64-w8a16.mlmodelc/analytics/coremldata.bin b/segmentation-3.0-b64-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..9ebbc2f7bc7363923b5550187398aa0bcceb3066 --- /dev/null +++ b/segmentation-3.0-b64-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d0f9ce1852053e6d154db0df44e11080f23ecb3f8fcc734afb81135d13d6ac7 +size 243 diff --git a/segmentation-3.0-b64-w8a16.mlmodelc/coremldata.bin b/segmentation-3.0-b64-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ecc2b94feaf89d1ce274f6b5f13ecaada722ab28 --- /dev/null +++ b/segmentation-3.0-b64-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:938cdf934614434dc438fab37d4281b0f4cd58677e245c341560f813440f9d5e +size 374 diff --git a/segmentation-3.0-b64-w8a16.mlmodelc/model.mil b/segmentation-3.0-b64-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..6df6b4f5b0eb888c98c6e635eed33774c658d894 --- /dev/null +++ b/segmentation-3.0-b64-w8a16.mlmodelc/model.mil @@ -0,0 +1,135 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor input) { + tensor sincnet_wav_norm1d_bias = const()[name = tensor("sincnet_wav_norm1d_bias"), val = tensor([0x1.73505ep-5])]; + tensor sincnet_wav_norm1d_weight = const()[name = tensor("sincnet_wav_norm1d_weight"), val = tensor([0x1.43f862p-7])]; + tensor sincnet_norm1d_0_bias = const()[name = tensor("sincnet_norm1d_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor sincnet_norm1d_0_weight = const()[name = tensor("sincnet_norm1d_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; + tensor sincnet_conv1d_1_bias = const()[name = tensor("sincnet_conv1d_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832)))]; + tensor sincnet_conv1d_1_weight_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("sincnet_conv1d_1_weight_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1152))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25344))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25216)))]; + tensor sincnet_norm1d_1_bias = const()[name = tensor("sincnet_norm1d_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25664)))]; + tensor sincnet_norm1d_1_weight = const()[name = tensor("sincnet_norm1d_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25984)))]; + tensor sincnet_conv1d_2_bias = const()[name = tensor("sincnet_conv1d_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26304)))]; + tensor sincnet_conv1d_2_weight_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("sincnet_conv1d_2_weight_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26624))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44864))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44736)))]; + tensor sincnet_norm1d_2_bias = const()[name = tensor("sincnet_norm1d_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45184)))]; + tensor sincnet_norm1d_2_weight = const()[name = tensor("sincnet_norm1d_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45504)))]; + tensor linear_0_bias = const()[name = tensor("linear_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45824)))]; + tensor linear_0_weight_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("linear_0_weight_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46400))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79424))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79232)))]; + tensor linear_1_bias = const()[name = tensor("linear_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80000)))]; + tensor linear_1_weight_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("linear_1_weight_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80576))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97024))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79232)))]; + tensor classifier_bias = const()[name = tensor("classifier_bias"), val = tensor([-0x1.00e888p+0, 0x1.67cb52p-2, 0x1.3d87fp-1, 0x1.c8aa8p-2, -0x1.445f5ep-2, -0x1.591274p-1, -0x1.8fb70ep-2])]; + tensor classifier_weight_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("classifier_weight_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97600))), scale = tensor([0x1.a91b44p-7, 0x1.26991cp-7, 0x1.8bd9b2p-7, 0x1.0eb8bp-7, 0x1.4a7844p-7, 0x1.3ccf28p-6, 0x1.5ebed6p-7]), zero_point = tensor([0, 0, 0, 0, 0, 0, 0])]; + tensor var_9 = const()[name = tensor("op_9"), val = tensor(0x1.47ae14p-7)]; + tensor var_24 = const()[name = tensor("op_24"), val = tensor(0x1.4f8b58p-17)]; + tensor waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = tensor("waveform")]; + tensor filters_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("filters_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98560))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118912))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118720)))]; + tensor outputs_pad_type_0 = const()[name = tensor("outputs_pad_type_0"), val = tensor("valid")]; + tensor outputs_strides_0 = const()[name = tensor("outputs_strides_0"), val = tensor([10])]; + tensor outputs_pad_0 = const()[name = tensor("outputs_pad_0"), val = tensor([0, 0])]; + tensor outputs_dilations_0 = const()[name = tensor("outputs_dilations_0"), val = tensor([1])]; + tensor outputs_groups_0 = const()[name = tensor("outputs_groups_0"), val = tensor(1)]; + tensor outputs = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters_quantized, x = waveform)[name = tensor("outputs")]; + tensor input_1 = abs(x = outputs)[name = tensor("input_1")]; + tensor var_119 = const()[name = tensor("op_119"), val = tensor([3])]; + tensor var_120 = const()[name = tensor("op_120"), val = tensor([3])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0])]; + tensor input_3_ceil_mode_0 = const()[name = tensor("input_3_ceil_mode_0"), val = tensor(false)]; + tensor input_3 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1)[name = tensor("input_3")]; + tensor input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = tensor("input_5")]; + tensor input_7 = leaky_relu(alpha = var_9, x = input_5)[name = tensor("input_7")]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("valid")]; + tensor input_9_strides_0 = const()[name = tensor("input_9_strides_0"), val = tensor([1])]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0])]; + tensor input_9_dilations_0 = const()[name = tensor("input_9_dilations_0"), val = tensor([1])]; + tensor input_9_groups_0 = const()[name = tensor("input_9_groups_0"), val = tensor(1)]; + tensor input_9 = conv(bias = sincnet_conv1d_1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight_quantized, x = input_7)[name = tensor("input_9")]; + tensor var_135 = const()[name = tensor("op_135"), val = tensor([3])]; + tensor var_136 = const()[name = tensor("op_136"), val = tensor([3])]; + tensor input_11_pad_type_0 = const()[name = tensor("input_11_pad_type_0"), val = tensor("custom")]; + tensor input_11_pad_0 = const()[name = tensor("input_11_pad_0"), val = tensor([0, 0])]; + tensor input_11_ceil_mode_0 = const()[name = tensor("input_11_ceil_mode_0"), val = tensor(false)]; + tensor input_11 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9)[name = tensor("input_11")]; + tensor input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = tensor("input_13")]; + tensor input_15 = leaky_relu(alpha = var_9, x = input_13)[name = tensor("input_15")]; + tensor input_17_pad_type_0 = const()[name = tensor("input_17_pad_type_0"), val = tensor("valid")]; + tensor input_17_strides_0 = const()[name = tensor("input_17_strides_0"), val = tensor([1])]; + tensor input_17_pad_0 = const()[name = tensor("input_17_pad_0"), val = tensor([0, 0])]; + tensor input_17_dilations_0 = const()[name = tensor("input_17_dilations_0"), val = tensor([1])]; + tensor input_17_groups_0 = const()[name = tensor("input_17_groups_0"), val = tensor(1)]; + tensor input_17 = conv(bias = sincnet_conv1d_2_bias, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight_quantized, x = input_15)[name = tensor("input_17")]; + tensor var_151 = const()[name = tensor("op_151"), val = tensor([3])]; + tensor var_152 = const()[name = tensor("op_152"), val = tensor([3])]; + tensor input_19_pad_type_0 = const()[name = tensor("input_19_pad_type_0"), val = tensor("custom")]; + tensor input_19_pad_0 = const()[name = tensor("input_19_pad_0"), val = tensor([0, 0])]; + tensor input_19_ceil_mode_0 = const()[name = tensor("input_19_ceil_mode_0"), val = tensor(false)]; + tensor input_19 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17)[name = tensor("input_19")]; + tensor input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = tensor("input_21")]; + tensor x = leaky_relu(alpha = var_9, x = input_21)[name = tensor("x")]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([2, 0, 1])]; + tensor add_0 = const()[name = tensor("add_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119296)))]; + tensor add_1 = const()[name = tensor("add_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121408)))]; + tensor concat_4_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_4_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123520))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154880))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_5_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_5_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156992))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222592))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_6_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_6_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(224704))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(255488))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_7_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_7_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257600))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323200))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("input_25_lstm_layer_0_lstm_h0_reshaped_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325312))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341888))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(341760)))]; + tensor input_25_lstm_layer_0_direction_0 = const()[name = tensor("input_25_lstm_layer_0_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_0_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_0_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_0_activation_0 = const()[name = tensor("input_25_lstm_layer_0_activation_0"), val = tensor("tanh")]; + tensor transpose_4 = transpose(perm = transpose_4_perm_0, x = x)[name = tensor("transpose_1")]; + tensor input_25_lstm_layer_0_0, tensor input_25_lstm_layer_0_1, tensor input_25_lstm_layer_0_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_5_quantized, weight_hh_back = concat_7_quantized, weight_ih = concat_4_quantized, weight_ih_back = concat_6_quantized, x = transpose_4)[name = tensor("input_25_lstm_layer_0")]; + tensor add_2 = const()[name = tensor("add_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(342208)))]; + tensor add_3 = const()[name = tensor("add_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344320)))]; + tensor concat_14_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_14_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346432))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(477568))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_15_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_15_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(479680))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545280))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_16_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_16_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(547392))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678528))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_17_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_17_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(680640))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(746240))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor input_25_lstm_layer_1_direction_0 = const()[name = tensor("input_25_lstm_layer_1_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_1_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_1_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_1_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_1_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_1_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_1_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_1_activation_0 = const()[name = tensor("input_25_lstm_layer_1_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_1_0, tensor input_25_lstm_layer_1_1, tensor input_25_lstm_layer_1_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_15_quantized, weight_hh_back = concat_17_quantized, weight_ih = concat_14_quantized, weight_ih_back = concat_16_quantized, x = input_25_lstm_layer_0_0)[name = tensor("input_25_lstm_layer_1")]; + tensor add_4 = const()[name = tensor("add_4"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(748352)))]; + tensor add_5 = const()[name = tensor("add_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(750464)))]; + tensor concat_24_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_24_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752576))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(883712))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_25_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_25_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(885824))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(951424))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_26_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_26_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(953536))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1084672))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_27_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_27_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1086784))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1152384))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor input_25_lstm_layer_2_direction_0 = const()[name = tensor("input_25_lstm_layer_2_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_2_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_2_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_2_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_2_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_2_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_2_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_2_activation_0 = const()[name = tensor("input_25_lstm_layer_2_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_2_0, tensor input_25_lstm_layer_2_1, tensor input_25_lstm_layer_2_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_25_quantized, weight_hh_back = concat_27_quantized, weight_ih = concat_24_quantized, weight_ih_back = concat_26_quantized, x = input_25_lstm_layer_1_0)[name = tensor("input_25_lstm_layer_2")]; + tensor add_6 = const()[name = tensor("add_6"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1154496)))]; + tensor add_7 = const()[name = tensor("add_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1156608)))]; + tensor concat_34_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_34_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158720))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1289856))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_35_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_35_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1291968))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1357568))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_36_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_36_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1359680))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1490816))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor concat_37_quantized = constexpr_affine_dequantize()[axis = tensor(0), name = tensor("concat_37_quantized"), quantized_data = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1492928))), scale = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1558528))), zero_point = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154304)))]; + tensor input_25_batch_first_direction_0 = const()[name = tensor("input_25_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor input_25_batch_first_output_sequence_0 = const()[name = tensor("input_25_batch_first_output_sequence_0"), val = tensor(true)]; + tensor input_25_batch_first_recurrent_activation_0 = const()[name = tensor("input_25_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_batch_first_cell_activation_0 = const()[name = tensor("input_25_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_batch_first_activation_0 = const()[name = tensor("input_25_batch_first_activation_0"), val = tensor("tanh")]; + tensor input_25_batch_first_0, tensor input_25_batch_first_1, tensor input_25_batch_first_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_35_quantized, weight_hh_back = concat_37_quantized, weight_ih = concat_34_quantized, weight_ih_back = concat_36_quantized, x = input_25_lstm_layer_2_0)[name = tensor("input_25_batch_first")]; + tensor input_25_perm_0 = const()[name = tensor("input_25_perm_0"), val = tensor([1, 0, 2])]; + tensor input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = tensor("transpose_0")]; + tensor input_27 = linear(bias = linear_0_bias, weight = linear_0_weight_quantized, x = input_25)[name = tensor("linear_0")]; + tensor var_220 = const()[name = tensor("op_220"), val = tensor(0x1.47ae14p-7)]; + tensor input_29 = leaky_relu(alpha = var_220, x = input_27)[name = tensor("input_29")]; + tensor input_31 = linear(bias = linear_1_bias, weight = linear_1_weight_quantized, x = input_29)[name = tensor("linear_1")]; + tensor var_225 = const()[name = tensor("op_225"), val = tensor(0x1.47ae14p-7)]; + tensor input_33 = leaky_relu(alpha = var_225, x = input_31)[name = tensor("input_33")]; + tensor input_1_1 = linear(bias = classifier_bias, weight = classifier_weight_quantized, x = input_33)[name = tensor("linear_2")]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor(-1)]; + tensor var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = tensor("op_232_softmax")]; + tensor var_232_epsilon_0 = const()[name = tensor("op_232_epsilon_0"), val = tensor(0x1p-149)]; + tensor output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = tensor("op_232")]; + } -> (output); +} \ No newline at end of file diff --git a/segmentation-3.0-b64-w8a16.mlmodelc/weights/weight.bin b/segmentation-3.0-b64-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..5e5071f4f5faf7b180d422bfa0b4ca589f623379 --- /dev/null +++ b/segmentation-3.0-b64-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a21a9bed1435cd97ea0e1d68ac67d82b53c47b51d149fa67682639c117420341 +size 1560640 diff --git a/segmentation-3.0-b64.mlmodelc/analytics/coremldata.bin b/segmentation-3.0-b64.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..11da8f8d61938f1dd7e55646618bb0b3c162cff4 --- /dev/null +++ b/segmentation-3.0-b64.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7497fe39c383a49061eae9ebb3052a59360752ee96deeee46ae701f9afbd6e4d +size 243 diff --git a/segmentation-3.0-b64.mlmodelc/coremldata.bin b/segmentation-3.0-b64.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..c811eb9b4c465b6421c6540f5b9b24d2af6c7406 --- /dev/null +++ b/segmentation-3.0-b64.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0460ffe22d22a0ae0dc86c0cc22d94c074f29bbd6be1434707a66b5e154796c8 +size 150 diff --git a/segmentation-3.0-b64.mlmodelc/model.mil b/segmentation-3.0-b64.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..6a47924e127ddcd52b05e3d1b82645ca876580af --- /dev/null +++ b/segmentation-3.0-b64.mlmodelc/model.mil @@ -0,0 +1,135 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor input) { + tensor sincnet_wav_norm1d_bias = const()[name = tensor("sincnet_wav_norm1d_bias"), val = tensor([0x1.73505ep-5])]; + tensor sincnet_wav_norm1d_weight = const()[name = tensor("sincnet_wav_norm1d_weight"), val = tensor([0x1.43f862p-7])]; + tensor sincnet_norm1d_0_bias = const()[name = tensor("sincnet_norm1d_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor sincnet_norm1d_0_weight = const()[name = tensor("sincnet_norm1d_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; + tensor sincnet_conv1d_1_bias = const()[name = tensor("sincnet_conv1d_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832)))]; + tensor sincnet_conv1d_1_weight = const()[name = tensor("sincnet_conv1d_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1152)))]; + tensor sincnet_norm1d_1_bias = const()[name = tensor("sincnet_norm1d_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97216)))]; + tensor sincnet_norm1d_1_weight = const()[name = tensor("sincnet_norm1d_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97536)))]; + tensor sincnet_conv1d_2_bias = const()[name = tensor("sincnet_conv1d_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97856)))]; + tensor sincnet_conv1d_2_weight = const()[name = tensor("sincnet_conv1d_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98176)))]; + tensor sincnet_norm1d_2_bias = const()[name = tensor("sincnet_norm1d_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170240)))]; + tensor sincnet_norm1d_2_weight = const()[name = tensor("sincnet_norm1d_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170560)))]; + tensor linear_0_bias = const()[name = tensor("linear_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170880)))]; + tensor linear_0_weight = const()[name = tensor("linear_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171456)))]; + tensor linear_1_bias = const()[name = tensor("linear_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302592)))]; + tensor linear_1_weight = const()[name = tensor("linear_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303168)))]; + tensor classifier_bias = const()[name = tensor("classifier_bias"), val = tensor([-0x1.00e888p+0, 0x1.67cb52p-2, 0x1.3d87fp-1, 0x1.c8aa8p-2, -0x1.445f5ep-2, -0x1.591274p-1, -0x1.8fb70ep-2])]; + tensor classifier_weight = const()[name = tensor("classifier_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(368768)))]; + tensor var_9 = const()[name = tensor("op_9"), val = tensor(0x1.47ae14p-7)]; + tensor var_24 = const()[name = tensor("op_24"), val = tensor(0x1.4f8b58p-17)]; + tensor waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = tensor("waveform")]; + tensor filters = const()[name = tensor("filters"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372416)))]; + tensor outputs_pad_type_0 = const()[name = tensor("outputs_pad_type_0"), val = tensor("valid")]; + tensor outputs_strides_0 = const()[name = tensor("outputs_strides_0"), val = tensor([10])]; + tensor outputs_pad_0 = const()[name = tensor("outputs_pad_0"), val = tensor([0, 0])]; + tensor outputs_dilations_0 = const()[name = tensor("outputs_dilations_0"), val = tensor([1])]; + tensor outputs_groups_0 = const()[name = tensor("outputs_groups_0"), val = tensor(1)]; + tensor outputs = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters, x = waveform)[name = tensor("outputs")]; + tensor input_1 = abs(x = outputs)[name = tensor("input_1")]; + tensor var_119 = const()[name = tensor("op_119"), val = tensor([3])]; + tensor var_120 = const()[name = tensor("op_120"), val = tensor([3])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0])]; + tensor input_3_ceil_mode_0 = const()[name = tensor("input_3_ceil_mode_0"), val = tensor(false)]; + tensor input_3 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1)[name = tensor("input_3")]; + tensor input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = tensor("input_5")]; + tensor input_7 = leaky_relu(alpha = var_9, x = input_5)[name = tensor("input_7")]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("valid")]; + tensor input_9_strides_0 = const()[name = tensor("input_9_strides_0"), val = tensor([1])]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0])]; + tensor input_9_dilations_0 = const()[name = tensor("input_9_dilations_0"), val = tensor([1])]; + tensor input_9_groups_0 = const()[name = tensor("input_9_groups_0"), val = tensor(1)]; + tensor input_9 = conv(bias = sincnet_conv1d_1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight, x = input_7)[name = tensor("input_9")]; + tensor var_135 = const()[name = tensor("op_135"), val = tensor([3])]; + tensor var_136 = const()[name = tensor("op_136"), val = tensor([3])]; + tensor input_11_pad_type_0 = const()[name = tensor("input_11_pad_type_0"), val = tensor("custom")]; + tensor input_11_pad_0 = const()[name = tensor("input_11_pad_0"), val = tensor([0, 0])]; + tensor input_11_ceil_mode_0 = const()[name = tensor("input_11_ceil_mode_0"), val = tensor(false)]; + tensor input_11 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9)[name = tensor("input_11")]; + tensor input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = tensor("input_13")]; + tensor input_15 = leaky_relu(alpha = var_9, x = input_13)[name = tensor("input_15")]; + tensor input_17_pad_type_0 = const()[name = tensor("input_17_pad_type_0"), val = tensor("valid")]; + tensor input_17_strides_0 = const()[name = tensor("input_17_strides_0"), val = tensor([1])]; + tensor input_17_pad_0 = const()[name = tensor("input_17_pad_0"), val = tensor([0, 0])]; + tensor input_17_dilations_0 = const()[name = tensor("input_17_dilations_0"), val = tensor([1])]; + tensor input_17_groups_0 = const()[name = tensor("input_17_groups_0"), val = tensor(1)]; + tensor input_17 = conv(bias = sincnet_conv1d_2_bias, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight, x = input_15)[name = tensor("input_17")]; + tensor var_151 = const()[name = tensor("op_151"), val = tensor([3])]; + tensor var_152 = const()[name = tensor("op_152"), val = tensor([3])]; + tensor input_19_pad_type_0 = const()[name = tensor("input_19_pad_type_0"), val = tensor("custom")]; + tensor input_19_pad_0 = const()[name = tensor("input_19_pad_0"), val = tensor([0, 0])]; + tensor input_19_ceil_mode_0 = const()[name = tensor("input_19_ceil_mode_0"), val = tensor(false)]; + tensor input_19 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17)[name = tensor("input_19")]; + tensor input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = tensor("input_21")]; + tensor x = leaky_relu(alpha = var_9, x = input_21)[name = tensor("x")]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([2, 0, 1])]; + tensor add_0 = const()[name = tensor("add_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(452800)))]; + tensor add_1 = const()[name = tensor("add_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454912)))]; + tensor concat_4 = const()[name = tensor("concat_4"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(457024)))]; + tensor concat_5 = const()[name = tensor("concat_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(579968)))]; + tensor concat_6 = const()[name = tensor("concat_6"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(842176)))]; + tensor concat_7 = const()[name = tensor("concat_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(965120)))]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped = const()[name = tensor("input_25_lstm_layer_0_lstm_h0_reshaped"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227328)))]; + tensor input_25_lstm_layer_0_direction_0 = const()[name = tensor("input_25_lstm_layer_0_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_0_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_0_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_0_activation_0 = const()[name = tensor("input_25_lstm_layer_0_activation_0"), val = tensor("tanh")]; + tensor transpose_4 = transpose(perm = transpose_4_perm_0, x = x)[name = tensor("transpose_6")]; + tensor input_25_lstm_layer_0_0, tensor input_25_lstm_layer_0_1, tensor input_25_lstm_layer_0_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_5, weight_hh_back = concat_7, weight_ih = concat_4, weight_ih_back = concat_6, x = transpose_4)[name = tensor("input_25_lstm_layer_0")]; + tensor add_2 = const()[name = tensor("add_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1292928)))]; + tensor add_3 = const()[name = tensor("add_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1295040)))]; + tensor concat_14 = const()[name = tensor("concat_14"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1297152)))]; + tensor concat_15 = const()[name = tensor("concat_15"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1821504)))]; + tensor concat_16 = const()[name = tensor("concat_16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2083712)))]; + tensor concat_17 = const()[name = tensor("concat_17"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2608064)))]; + tensor input_25_lstm_layer_1_direction_0 = const()[name = tensor("input_25_lstm_layer_1_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_1_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_1_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_1_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_1_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_1_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_1_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_1_activation_0 = const()[name = tensor("input_25_lstm_layer_1_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_1_0, tensor input_25_lstm_layer_1_1, tensor input_25_lstm_layer_1_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_15, weight_hh_back = concat_17, weight_ih = concat_14, weight_ih_back = concat_16, x = input_25_lstm_layer_0_0)[name = tensor("input_25_lstm_layer_1")]; + tensor add_4 = const()[name = tensor("add_4"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2870272)))]; + tensor add_5 = const()[name = tensor("add_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2872384)))]; + tensor concat_24 = const()[name = tensor("concat_24"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2874496)))]; + tensor concat_25 = const()[name = tensor("concat_25"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3398848)))]; + tensor concat_26 = const()[name = tensor("concat_26"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3661056)))]; + tensor concat_27 = const()[name = tensor("concat_27"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4185408)))]; + tensor input_25_lstm_layer_2_direction_0 = const()[name = tensor("input_25_lstm_layer_2_direction_0"), val = tensor("bidirectional")]; + tensor input_25_lstm_layer_2_output_sequence_0 = const()[name = tensor("input_25_lstm_layer_2_output_sequence_0"), val = tensor(true)]; + tensor input_25_lstm_layer_2_recurrent_activation_0 = const()[name = tensor("input_25_lstm_layer_2_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_lstm_layer_2_cell_activation_0 = const()[name = tensor("input_25_lstm_layer_2_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_2_activation_0 = const()[name = tensor("input_25_lstm_layer_2_activation_0"), val = tensor("tanh")]; + tensor input_25_lstm_layer_2_0, tensor input_25_lstm_layer_2_1, tensor input_25_lstm_layer_2_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_25, weight_hh_back = concat_27, weight_ih = concat_24, weight_ih_back = concat_26, x = input_25_lstm_layer_1_0)[name = tensor("input_25_lstm_layer_2")]; + tensor add_6 = const()[name = tensor("add_6"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4447616)))]; + tensor add_7 = const()[name = tensor("add_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4449728)))]; + tensor concat_34 = const()[name = tensor("concat_34"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4451840)))]; + tensor concat_35 = const()[name = tensor("concat_35"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4976192)))]; + tensor concat_36 = const()[name = tensor("concat_36"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5238400)))]; + tensor concat_37 = const()[name = tensor("concat_37"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5762752)))]; + tensor input_25_batch_first_direction_0 = const()[name = tensor("input_25_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor input_25_batch_first_output_sequence_0 = const()[name = tensor("input_25_batch_first_output_sequence_0"), val = tensor(true)]; + tensor input_25_batch_first_recurrent_activation_0 = const()[name = tensor("input_25_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_25_batch_first_cell_activation_0 = const()[name = tensor("input_25_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor input_25_batch_first_activation_0 = const()[name = tensor("input_25_batch_first_activation_0"), val = tensor("tanh")]; + tensor input_25_batch_first_0, tensor input_25_batch_first_1, tensor input_25_batch_first_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_35, weight_hh_back = concat_37, weight_ih = concat_34, weight_ih_back = concat_36, x = input_25_lstm_layer_2_0)[name = tensor("input_25_batch_first")]; + tensor input_25_perm_0 = const()[name = tensor("input_25_perm_0"), val = tensor([1, 0, 2])]; + tensor input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = tensor("transpose_5")]; + tensor input_27 = linear(bias = linear_0_bias, weight = linear_0_weight, x = input_25)[name = tensor("linear_0")]; + tensor var_220 = const()[name = tensor("op_220"), val = tensor(0x1.47ae14p-7)]; + tensor input_29 = leaky_relu(alpha = var_220, x = input_27)[name = tensor("input_29")]; + tensor input_31 = linear(bias = linear_1_bias, weight = linear_1_weight, x = input_29)[name = tensor("linear_1")]; + tensor var_225 = const()[name = tensor("op_225"), val = tensor(0x1.47ae14p-7)]; + tensor input_33 = leaky_relu(alpha = var_225, x = input_31)[name = tensor("input_33")]; + tensor input_1_1 = linear(bias = classifier_bias, weight = classifier_weight, x = input_33)[name = tensor("linear_2")]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor(-1)]; + tensor var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = tensor("op_232_softmax")]; + tensor var_232_epsilon_0 = const()[name = tensor("op_232_epsilon_0"), val = tensor(0x1p-149)]; + tensor output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = tensor("op_232")]; + } -> (output); +} \ No newline at end of file diff --git a/segmentation-3.0-b64.mlmodelc/weights/weight.bin b/segmentation-3.0-b64.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..2f7df59a0bda56b5961aa6d5bef3fd0329d90375 --- /dev/null +++ b/segmentation-3.0-b64.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b8ef91e4a97b435f0f3a4bb66ca647ff53820edc4cb747c242413045a4aaa56 +size 6024960 diff --git a/segmentation-3.0-f16.mlmodelc/analytics/coremldata.bin b/segmentation-3.0-f16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b7fda399a137c77661df251007d9785c8ca5622e --- /dev/null +++ b/segmentation-3.0-f16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7c76a1fc31b7a4d739f9112bf8ac55f4b5caad583d6ac9e47de680e775c9ade5 +size 243 diff --git a/segmentation-3.0-f16.mlmodelc/coremldata.bin b/segmentation-3.0-f16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..7184df841f572c6fdde3215ed785affe23098cdf --- /dev/null +++ b/segmentation-3.0-f16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ac710fb9bcd0310d0c40fd82f2350ca5287b18596da33470bf5185be148aad81 +size 439 diff --git a/segmentation-3.0-f16.mlmodelc/model.mil b/segmentation-3.0-f16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a418b91ac02029ebd8c5efc59d9fc712eaa2a34a --- /dev/null +++ b/segmentation-3.0-f16.mlmodelc/model.mil @@ -0,0 +1,227 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor input) [FlexibleShapeInformation = tuple>>, tuple>>>>((("DefaultShapes", {{"input", [32, 1, 160000]}}), ("EnumeratedShapes", {{"047bedbd", {{"input", [24, 1, 160000]}}}, {"08383b0f", {{"input", [32, 1, 160000]}}}, {"146ea7a4", {{"input", [30, 1, 160000]}}}, {"14a6a9fa", {{"input", [27, 1, 160000]}}}, {"41d6af63", {{"input", [26, 1, 160000]}}}, {"4a349f6d", {{"input", [2, 1, 160000]}}}, {"4c2c6917", {{"input", [8, 1, 160000]}}}, {"4cb052b1", {{"input", [5, 1, 160000]}}}, {"4eab2425", {{"input", [23, 1, 160000]}}}, {"4f2b5bd2", {{"input", [14, 1, 160000]}}}, {"50b949f3", {{"input", [22, 1, 160000]}}}, {"5316ecea", {{"input", [1, 1, 160000]}}}, {"5d89881e", {{"input", [21, 1, 160000]}}}, {"693a1c76", {{"input", [19, 1, 160000]}}}, {"6ac4a6a4", {{"input", [29, 1, 160000]}}}, {"73f266d5", {{"input", [3, 1, 160000]}}}, {"73f43a1d", {{"input", [31, 1, 160000]}}}, {"7ee56056", {{"input", [18, 1, 160000]}}}, {"9035b52a", {{"input", [25, 1, 160000]}}}, {"94f7468c", {{"input", [20, 1, 160000]}}}, {"999a22b0", {{"input", [12, 1, 160000]}}}, {"9fad9511", {{"input", [4, 1, 160000]}}}, {"ab9dbd8c", {{"input", [9, 1, 160000]}}}, {"ae49a11c", {{"input", [16, 1, 160000]}}}, {"bf53b769", {{"input", [15, 1, 160000]}}}, {"c147bbba", {{"input", [11, 1, 160000]}}}, {"c32e6216", {{"input", [28, 1, 160000]}}}, {"d1a076a6", {{"input", [7, 1, 160000]}}}, {"dccf3050", {{"input", [17, 1, 160000]}}}, {"ef60c196", {{"input", [10, 1, 160000]}}}, {"fe5ae199", {{"input", [13, 1, 160000]}}}, {"ffc2aaa2", {{"input", [6, 1, 160000]}}}})))] { + fp32 var_9 = const()[name = string("op_9"), val = fp32(0x1.47ae14p-7)]; + string input_to_fp16_dtype_0 = const()[name = string("input_to_fp16_dtype_0"), val = string("fp16")]; + tensor sincnet_wav_norm1d_weight_to_fp16 = const()[name = string("sincnet_wav_norm1d_weight_to_fp16"), val = tensor([0x1.44p-7])]; + tensor sincnet_wav_norm1d_bias_to_fp16 = const()[name = string("sincnet_wav_norm1d_bias_to_fp16"), val = tensor([0x1.734p-5])]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_to_fp16 = cast(dtype = input_to_fp16_dtype_0, x = input)[name = string("cast_19")]; + tensor waveform_cast_fp16 = instance_norm(beta = sincnet_wav_norm1d_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_wav_norm1d_weight_to_fp16, x = input_to_fp16)[name = string("waveform_cast_fp16")]; + string outputs_pad_type_0 = const()[name = string("outputs_pad_type_0"), val = string("valid")]; + tensor outputs_strides_0 = const()[name = string("outputs_strides_0"), val = tensor([10])]; + tensor outputs_pad_0 = const()[name = string("outputs_pad_0"), val = tensor([0, 0])]; + tensor outputs_dilations_0 = const()[name = string("outputs_dilations_0"), val = tensor([1])]; + int32 outputs_groups_0 = const()[name = string("outputs_groups_0"), val = int32(1)]; + tensor filters_to_fp16 = const()[name = string("filters_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor outputs_cast_fp16 = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters_to_fp16, x = waveform_cast_fp16)[name = string("outputs_cast_fp16")]; + tensor input_1_cast_fp16 = abs(x = outputs_cast_fp16)[name = string("input_1_cast_fp16")]; + tensor var_119 = const()[name = string("op_119"), val = tensor([3])]; + tensor var_120 = const()[name = string("op_120"), val = tensor([3])]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([0, 0])]; + bool input_3_ceil_mode_0 = const()[name = string("input_3_ceil_mode_0"), val = bool(false)]; + tensor input_3_cast_fp16 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor sincnet_norm1d_0_weight_to_fp16 = const()[name = string("sincnet_norm1d_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40320)))]; + tensor sincnet_norm1d_0_bias_to_fp16 = const()[name = string("sincnet_norm1d_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40576)))]; + tensor input_5_cast_fp16 = instance_norm(beta = sincnet_norm1d_0_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_0_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor input_7_cast_fp16 = leaky_relu(alpha = var_9, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1])]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor sincnet_conv1d_1_weight_to_fp16 = const()[name = string("sincnet_conv1d_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40832)))]; + tensor sincnet_conv1d_1_bias_to_fp16 = const()[name = string("sincnet_conv1d_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88896)))]; + tensor input_9_cast_fp16 = conv(bias = sincnet_conv1d_1_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight_to_fp16, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor var_135 = const()[name = string("op_135"), val = tensor([3])]; + tensor var_136 = const()[name = string("op_136"), val = tensor([3])]; + string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("custom")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0])]; + bool input_11_ceil_mode_0 = const()[name = string("input_11_ceil_mode_0"), val = bool(false)]; + tensor input_11_cast_fp16 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor sincnet_norm1d_1_weight_to_fp16 = const()[name = string("sincnet_norm1d_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89088)))]; + tensor sincnet_norm1d_1_bias_to_fp16 = const()[name = string("sincnet_norm1d_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89280)))]; + tensor input_13_cast_fp16 = instance_norm(beta = sincnet_norm1d_1_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_1_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = leaky_relu(alpha = var_9, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")]; + tensor input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor([1])]; + tensor input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor([0, 0])]; + tensor input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor([1])]; + int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)]; + tensor sincnet_conv1d_2_weight_to_fp16 = const()[name = string("sincnet_conv1d_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89472)))]; + tensor sincnet_conv1d_2_bias_to_fp16 = const()[name = string("sincnet_conv1d_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125568)))]; + tensor input_17_cast_fp16 = conv(bias = sincnet_conv1d_2_bias_to_fp16, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight_to_fp16, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor var_151 = const()[name = string("op_151"), val = tensor([3])]; + tensor var_152 = const()[name = string("op_152"), val = tensor([3])]; + string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")]; + tensor input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor([0, 0])]; + bool input_19_ceil_mode_0 = const()[name = string("input_19_ceil_mode_0"), val = bool(false)]; + tensor input_19_cast_fp16 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor sincnet_norm1d_2_weight_to_fp16 = const()[name = string("sincnet_norm1d_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125760)))]; + tensor sincnet_norm1d_2_bias_to_fp16 = const()[name = string("sincnet_norm1d_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125952)))]; + tensor input_21_cast_fp16 = instance_norm(beta = sincnet_norm1d_2_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_2_weight_to_fp16, x = input_19_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor x_cast_fp16 = leaky_relu(alpha = var_9, x = input_21_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_163 = const()[name = string("op_163"), val = tensor([0, 2, 1])]; + int32 var_172 = const()[name = string("op_172"), val = int32(128)]; + int32 var_173 = const()[name = string("op_173"), val = int32(8)]; + tensor input_23_cast_fp16 = transpose(perm = var_163, x = x_cast_fp16)[name = string("transpose_6")]; + tensor var_207_shape_cast_fp16 = shape(x = input_23_cast_fp16)[name = string("op_207_shape_cast_fp16")]; + int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)]; + int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)]; + bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)]; + string var_207_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_207_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_0_indices_0_to_uint16 = const()[name = string("gather_0_indices_0_to_uint16"), val = uint16(0)]; + tensor var_207_shape_cast_fp16_to_int16 = cast(dtype = var_207_shape_cast_fp16_to_int16_dtype_0, x = var_207_shape_cast_fp16)[name = string("cast_18")]; + int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = gather_0_indices_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_207_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")]; + string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)]; + bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)]; + int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_17")]; + tensor concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_173, gather_0_cast_uint16_to_int32, var_172))[name = string("concat_0")]; + fp16 hx_1_value_0_to_fp16 = const()[name = string("hx_1_value_0_to_fp16"), val = fp16(0x0p+0)]; + tensor hx_1_cast_fp16 = fill(shape = concat_0, value = hx_1_value_0_to_fp16)[name = string("hx_1_cast_fp16")]; + tensor input_23_batch_first_transpose_perm_0 = const()[name = string("input_23_batch_first_transpose_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(4)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + tensor split_0_cast_fp16_0, tensor split_0_cast_fp16_1, tensor split_0_cast_fp16_2, tensor split_0_cast_fp16_3 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = hx_1_cast_fp16)[name = string("split_0_cast_fp16")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(4)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + tensor split_1_cast_fp16_0, tensor split_1_cast_fp16_1, tensor split_1_cast_fp16_2, tensor split_1_cast_fp16_3 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = hx_1_cast_fp16)[name = string("split_1_cast_fp16")]; + tensor split_10_split_sizes_0 = const()[name = string("split_10_split_sizes_0"), val = tensor([1, 1])]; + int32 split_10_axis_0 = const()[name = string("split_10_axis_0"), val = int32(0)]; + tensor split_10_cast_fp16_0, tensor split_10_cast_fp16_1 = split(axis = split_10_axis_0, split_sizes = split_10_split_sizes_0, x = split_0_cast_fp16_0)[name = string("split_10_cast_fp16")]; + int32 concat_10_axis_0 = const()[name = string("concat_10_axis_0"), val = int32(2)]; + bool concat_10_interleave_0 = const()[name = string("concat_10_interleave_0"), val = bool(false)]; + tensor concat_10_cast_fp16 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (split_10_cast_fp16_0, split_10_cast_fp16_1))[name = string("concat_10_cast_fp16")]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_0_lstm_h0_reshaped_axes_0, x = concat_10_cast_fp16)[name = string("input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16")]; + tensor split_11_split_sizes_0 = const()[name = string("split_11_split_sizes_0"), val = tensor([1, 1])]; + int32 split_11_axis_0 = const()[name = string("split_11_axis_0"), val = int32(0)]; + tensor split_11_cast_fp16_0, tensor split_11_cast_fp16_1 = split(axis = split_11_axis_0, split_sizes = split_11_split_sizes_0, x = split_1_cast_fp16_0)[name = string("split_11_cast_fp16")]; + int32 concat_11_axis_0 = const()[name = string("concat_11_axis_0"), val = int32(2)]; + bool concat_11_interleave_0 = const()[name = string("concat_11_interleave_0"), val = bool(false)]; + tensor concat_11_cast_fp16 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (split_11_cast_fp16_0, split_11_cast_fp16_1))[name = string("concat_11_cast_fp16")]; + tensor input_25_lstm_layer_0_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_0_lstm_c0_reshaped_axes_0, x = concat_11_cast_fp16)[name = string("input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16")]; + string input_25_lstm_layer_0_direction_0 = const()[name = string("input_25_lstm_layer_0_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_0_output_sequence_0 = const()[name = string("input_25_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_0_cell_activation_0 = const()[name = string("input_25_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_0_activation_0 = const()[name = string("input_25_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor concat_6_to_fp16 = const()[name = string("concat_6_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126144)))]; + tensor concat_7_to_fp16 = const()[name = string("concat_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187648)))]; + tensor add_0_to_fp16 = const()[name = string("add_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318784)))]; + tensor concat_8_to_fp16 = const()[name = string("concat_8_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319872)))]; + tensor concat_9_to_fp16 = const()[name = string("concat_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381376)))]; + tensor add_1_to_fp16 = const()[name = string("add_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512512)))]; + tensor input_23_batch_first_transpose_cast_fp16 = transpose(perm = input_23_batch_first_transpose_perm_0, x = input_23_cast_fp16)[name = string("transpose_5")]; + tensor input_25_lstm_layer_0_cast_fp16_0, tensor input_25_lstm_layer_0_cast_fp16_1, tensor input_25_lstm_layer_0_cast_fp16_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_7_to_fp16, weight_hh_back = concat_9_to_fp16, weight_ih = concat_6_to_fp16, weight_ih_back = concat_8_to_fp16, x = input_23_batch_first_transpose_cast_fp16)[name = string("input_25_lstm_layer_0_cast_fp16")]; + tensor split_20_split_sizes_0 = const()[name = string("split_20_split_sizes_0"), val = tensor([1, 1])]; + int32 split_20_axis_0 = const()[name = string("split_20_axis_0"), val = int32(0)]; + tensor split_20_cast_fp16_0, tensor split_20_cast_fp16_1 = split(axis = split_20_axis_0, split_sizes = split_20_split_sizes_0, x = split_0_cast_fp16_1)[name = string("split_20_cast_fp16")]; + int32 concat_20_axis_0 = const()[name = string("concat_20_axis_0"), val = int32(2)]; + bool concat_20_interleave_0 = const()[name = string("concat_20_interleave_0"), val = bool(false)]; + tensor concat_20_cast_fp16 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (split_20_cast_fp16_0, split_20_cast_fp16_1))[name = string("concat_20_cast_fp16")]; + tensor input_25_lstm_layer_1_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_1_lstm_h0_reshaped_axes_0, x = concat_20_cast_fp16)[name = string("input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16")]; + tensor split_21_split_sizes_0 = const()[name = string("split_21_split_sizes_0"), val = tensor([1, 1])]; + int32 split_21_axis_0 = const()[name = string("split_21_axis_0"), val = int32(0)]; + tensor split_21_cast_fp16_0, tensor split_21_cast_fp16_1 = split(axis = split_21_axis_0, split_sizes = split_21_split_sizes_0, x = split_1_cast_fp16_1)[name = string("split_21_cast_fp16")]; + int32 concat_21_axis_0 = const()[name = string("concat_21_axis_0"), val = int32(2)]; + bool concat_21_interleave_0 = const()[name = string("concat_21_interleave_0"), val = bool(false)]; + tensor concat_21_cast_fp16 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (split_21_cast_fp16_0, split_21_cast_fp16_1))[name = string("concat_21_cast_fp16")]; + tensor input_25_lstm_layer_1_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_1_lstm_c0_reshaped_axes_0, x = concat_21_cast_fp16)[name = string("input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16")]; + string input_25_lstm_layer_1_direction_0 = const()[name = string("input_25_lstm_layer_1_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_1_output_sequence_0 = const()[name = string("input_25_lstm_layer_1_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_1_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_1_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_1_cell_activation_0 = const()[name = string("input_25_lstm_layer_1_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_1_activation_0 = const()[name = string("input_25_lstm_layer_1_activation_0"), val = string("tanh")]; + tensor concat_16_to_fp16 = const()[name = string("concat_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513600)))]; + tensor concat_17_to_fp16 = const()[name = string("concat_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775808)))]; + tensor add_2_to_fp16 = const()[name = string("add_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906944)))]; + tensor concat_18_to_fp16 = const()[name = string("concat_18_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908032)))]; + tensor concat_19_to_fp16 = const()[name = string("concat_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1170240)))]; + tensor add_3_to_fp16 = const()[name = string("add_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1301376)))]; + tensor input_25_lstm_layer_1_cast_fp16_0, tensor input_25_lstm_layer_1_cast_fp16_1, tensor input_25_lstm_layer_1_cast_fp16_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2_to_fp16, bias_back = add_3_to_fp16, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16, initial_h = input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_17_to_fp16, weight_hh_back = concat_19_to_fp16, weight_ih = concat_16_to_fp16, weight_ih_back = concat_18_to_fp16, x = input_25_lstm_layer_0_cast_fp16_0)[name = string("input_25_lstm_layer_1_cast_fp16")]; + tensor split_30_split_sizes_0 = const()[name = string("split_30_split_sizes_0"), val = tensor([1, 1])]; + int32 split_30_axis_0 = const()[name = string("split_30_axis_0"), val = int32(0)]; + tensor split_30_cast_fp16_0, tensor split_30_cast_fp16_1 = split(axis = split_30_axis_0, split_sizes = split_30_split_sizes_0, x = split_0_cast_fp16_2)[name = string("split_30_cast_fp16")]; + int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(2)]; + bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)]; + tensor concat_30_cast_fp16 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (split_30_cast_fp16_0, split_30_cast_fp16_1))[name = string("concat_30_cast_fp16")]; + tensor input_25_lstm_layer_2_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_2_lstm_h0_reshaped_axes_0, x = concat_30_cast_fp16)[name = string("input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16")]; + tensor split_31_split_sizes_0 = const()[name = string("split_31_split_sizes_0"), val = tensor([1, 1])]; + int32 split_31_axis_0 = const()[name = string("split_31_axis_0"), val = int32(0)]; + tensor split_31_cast_fp16_0, tensor split_31_cast_fp16_1 = split(axis = split_31_axis_0, split_sizes = split_31_split_sizes_0, x = split_1_cast_fp16_2)[name = string("split_31_cast_fp16")]; + int32 concat_31_axis_0 = const()[name = string("concat_31_axis_0"), val = int32(2)]; + bool concat_31_interleave_0 = const()[name = string("concat_31_interleave_0"), val = bool(false)]; + tensor concat_31_cast_fp16 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (split_31_cast_fp16_0, split_31_cast_fp16_1))[name = string("concat_31_cast_fp16")]; + tensor input_25_lstm_layer_2_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_2_lstm_c0_reshaped_axes_0, x = concat_31_cast_fp16)[name = string("input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16")]; + string input_25_lstm_layer_2_direction_0 = const()[name = string("input_25_lstm_layer_2_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_2_output_sequence_0 = const()[name = string("input_25_lstm_layer_2_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_2_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_2_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_2_cell_activation_0 = const()[name = string("input_25_lstm_layer_2_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_2_activation_0 = const()[name = string("input_25_lstm_layer_2_activation_0"), val = string("tanh")]; + tensor concat_26_to_fp16 = const()[name = string("concat_26_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1302464)))]; + tensor concat_27_to_fp16 = const()[name = string("concat_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1564672)))]; + tensor add_4_to_fp16 = const()[name = string("add_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1695808)))]; + tensor concat_28_to_fp16 = const()[name = string("concat_28_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1696896)))]; + tensor concat_29_to_fp16 = const()[name = string("concat_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1959104)))]; + tensor add_5_to_fp16 = const()[name = string("add_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2090240)))]; + tensor input_25_lstm_layer_2_cast_fp16_0, tensor input_25_lstm_layer_2_cast_fp16_1, tensor input_25_lstm_layer_2_cast_fp16_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4_to_fp16, bias_back = add_5_to_fp16, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16, initial_h = input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_27_to_fp16, weight_hh_back = concat_29_to_fp16, weight_ih = concat_26_to_fp16, weight_ih_back = concat_28_to_fp16, x = input_25_lstm_layer_1_cast_fp16_0)[name = string("input_25_lstm_layer_2_cast_fp16")]; + tensor split_40_split_sizes_0 = const()[name = string("split_40_split_sizes_0"), val = tensor([1, 1])]; + int32 split_40_axis_0 = const()[name = string("split_40_axis_0"), val = int32(0)]; + tensor split_40_cast_fp16_0, tensor split_40_cast_fp16_1 = split(axis = split_40_axis_0, split_sizes = split_40_split_sizes_0, x = split_0_cast_fp16_3)[name = string("split_40_cast_fp16")]; + int32 concat_40_axis_0 = const()[name = string("concat_40_axis_0"), val = int32(2)]; + bool concat_40_interleave_0 = const()[name = string("concat_40_interleave_0"), val = bool(false)]; + tensor concat_40_cast_fp16 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (split_40_cast_fp16_0, split_40_cast_fp16_1))[name = string("concat_40_cast_fp16")]; + tensor input_25_batch_first_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_batch_first_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_batch_first_lstm_h0_reshaped_axes_0, x = concat_40_cast_fp16)[name = string("input_25_batch_first_lstm_h0_reshaped_cast_fp16")]; + tensor split_41_split_sizes_0 = const()[name = string("split_41_split_sizes_0"), val = tensor([1, 1])]; + int32 split_41_axis_0 = const()[name = string("split_41_axis_0"), val = int32(0)]; + tensor split_41_cast_fp16_0, tensor split_41_cast_fp16_1 = split(axis = split_41_axis_0, split_sizes = split_41_split_sizes_0, x = split_1_cast_fp16_3)[name = string("split_41_cast_fp16")]; + int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(2)]; + bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)]; + tensor concat_41_cast_fp16 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (split_41_cast_fp16_0, split_41_cast_fp16_1))[name = string("concat_41_cast_fp16")]; + tensor input_25_batch_first_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_batch_first_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_batch_first_lstm_c0_reshaped_axes_0, x = concat_41_cast_fp16)[name = string("input_25_batch_first_lstm_c0_reshaped_cast_fp16")]; + string input_25_batch_first_direction_0 = const()[name = string("input_25_batch_first_direction_0"), val = string("bidirectional")]; + bool input_25_batch_first_output_sequence_0 = const()[name = string("input_25_batch_first_output_sequence_0"), val = bool(true)]; + string input_25_batch_first_recurrent_activation_0 = const()[name = string("input_25_batch_first_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_batch_first_cell_activation_0 = const()[name = string("input_25_batch_first_cell_activation_0"), val = string("tanh")]; + string input_25_batch_first_activation_0 = const()[name = string("input_25_batch_first_activation_0"), val = string("tanh")]; + tensor concat_36_to_fp16 = const()[name = string("concat_36_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2091328)))]; + tensor concat_37_to_fp16 = const()[name = string("concat_37_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2353536)))]; + tensor add_6_to_fp16 = const()[name = string("add_6_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2484672)))]; + tensor concat_38_to_fp16 = const()[name = string("concat_38_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2485760)))]; + tensor concat_39_to_fp16 = const()[name = string("concat_39_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2747968)))]; + tensor add_7_to_fp16 = const()[name = string("add_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2879104)))]; + tensor input_25_batch_first_cast_fp16_0, tensor input_25_batch_first_cast_fp16_1, tensor input_25_batch_first_cast_fp16_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6_to_fp16, bias_back = add_7_to_fp16, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_batch_first_lstm_c0_reshaped_cast_fp16, initial_h = input_25_batch_first_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_37_to_fp16, weight_hh_back = concat_39_to_fp16, weight_ih = concat_36_to_fp16, weight_ih_back = concat_38_to_fp16, x = input_25_lstm_layer_2_cast_fp16_0)[name = string("input_25_batch_first_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([1, 0, 2])]; + tensor linear_0_weight_to_fp16 = const()[name = string("linear_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2880192)))]; + tensor linear_0_bias_to_fp16 = const()[name = string("linear_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2945792)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = input_25_batch_first_cast_fp16_0)[name = string("transpose_4")]; + tensor linear_0_cast_fp16 = linear(bias = linear_0_bias_to_fp16, weight = linear_0_weight_to_fp16, x = input_25_cast_fp16)[name = string("linear_0_cast_fp16")]; + fp32 var_220 = const()[name = string("op_220"), val = fp32(0x1.47ae14p-7)]; + tensor input_29_cast_fp16 = leaky_relu(alpha = var_220, x = linear_0_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor linear_1_weight_to_fp16 = const()[name = string("linear_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2946112)))]; + tensor linear_1_bias_to_fp16 = const()[name = string("linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2978944)))]; + tensor linear_1_cast_fp16 = linear(bias = linear_1_bias_to_fp16, weight = linear_1_weight_to_fp16, x = input_29_cast_fp16)[name = string("linear_1_cast_fp16")]; + fp32 var_225 = const()[name = string("op_225"), val = fp32(0x1.47ae14p-7)]; + tensor input_33_cast_fp16 = leaky_relu(alpha = var_225, x = linear_1_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor classifier_weight_to_fp16 = const()[name = string("classifier_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2979264)))]; + tensor classifier_bias_to_fp16 = const()[name = string("classifier_bias_to_fp16"), val = tensor([-0x1.01p+0, 0x1.67cp-2, 0x1.3d8p-1, 0x1.c8cp-2, -0x1.444p-2, -0x1.59p-1, -0x1.8fcp-2])]; + tensor linear_2_cast_fp16 = linear(bias = classifier_bias_to_fp16, weight = classifier_weight_to_fp16, x = input_33_cast_fp16)[name = string("linear_2_cast_fp16")]; + int32 var_231 = const()[name = string("op_231"), val = int32(-1)]; + tensor var_232_softmax_cast_fp16 = softmax(axis = var_231, x = linear_2_cast_fp16)[name = string("op_232_softmax_cast_fp16")]; + fp32 var_232_epsilon_0 = const()[name = string("op_232_epsilon_0"), val = fp32(0x1p-149)]; + tensor var_232_cast_fp16 = log(epsilon = var_232_epsilon_0, x = var_232_softmax_cast_fp16)[name = string("op_232_cast_fp16")]; + string var_232_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_232_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor output = cast(dtype = var_232_cast_fp16_to_fp32_dtype_0, x = var_232_cast_fp16)[name = string("cast_16")]; + } -> (output); +} \ No newline at end of file diff --git a/segmentation-3.0-f16.mlmodelc/weights/weight.bin b/segmentation-3.0-f16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..9226de6c7233bd392a439a63861af54c274e6f8d --- /dev/null +++ b/segmentation-3.0-f16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0026d3483c74bc989fdd1649c5765ca5395235a6d140a698a2d87b95cddf56ae +size 2981120 diff --git a/segmentation-3.0-w8a16.mlmodelc/analytics/coremldata.bin b/segmentation-3.0-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..827a9792cce2a1bcffc65665657565d60ebc6304 --- /dev/null +++ b/segmentation-3.0-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4fcdcd90f3129cc3eee5a970ad9851d0c735930b5ddf10ef73d917fd0517c9ec +size 243 diff --git a/segmentation-3.0-w8a16.mlmodelc/coremldata.bin b/segmentation-3.0-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ffdecc76a4c0f4341a034f04bd2cc48337eb0123 --- /dev/null +++ b/segmentation-3.0-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:df181aeef8a195decfec56b1ff7283ddc163ef0a0e260c68af87927868573f37 +size 664 diff --git a/segmentation-3.0-w8a16.mlmodelc/model.mil b/segmentation-3.0-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..b80af75addb65f0eb950d721b30a372c6cf443fb --- /dev/null +++ b/segmentation-3.0-w8a16.mlmodelc/model.mil @@ -0,0 +1,219 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor input) [FlexibleShapeInformation = tuple>>, tuple>>>>((("DefaultShapes", {{"input", [32, 1, 160000]}}), ("EnumeratedShapes", {{"047bedbd", {{"input", [24, 1, 160000]}}}, {"08383b0f", {{"input", [32, 1, 160000]}}}, {"146ea7a4", {{"input", [30, 1, 160000]}}}, {"14a6a9fa", {{"input", [27, 1, 160000]}}}, {"41d6af63", {{"input", [26, 1, 160000]}}}, {"4a349f6d", {{"input", [2, 1, 160000]}}}, {"4c2c6917", {{"input", [8, 1, 160000]}}}, {"4cb052b1", {{"input", [5, 1, 160000]}}}, {"4eab2425", {{"input", [23, 1, 160000]}}}, {"4f2b5bd2", {{"input", [14, 1, 160000]}}}, {"50b949f3", {{"input", [22, 1, 160000]}}}, {"5316ecea", {{"input", [1, 1, 160000]}}}, {"5d89881e", {{"input", [21, 1, 160000]}}}, {"693a1c76", {{"input", [19, 1, 160000]}}}, {"6ac4a6a4", {{"input", [29, 1, 160000]}}}, {"73f266d5", {{"input", [3, 1, 160000]}}}, {"73f43a1d", {{"input", [31, 1, 160000]}}}, {"7ee56056", {{"input", [18, 1, 160000]}}}, {"9035b52a", {{"input", [25, 1, 160000]}}}, {"94f7468c", {{"input", [20, 1, 160000]}}}, {"999a22b0", {{"input", [12, 1, 160000]}}}, {"9fad9511", {{"input", [4, 1, 160000]}}}, {"ab9dbd8c", {{"input", [9, 1, 160000]}}}, {"ae49a11c", {{"input", [16, 1, 160000]}}}, {"bf53b769", {{"input", [15, 1, 160000]}}}, {"c147bbba", {{"input", [11, 1, 160000]}}}, {"c32e6216", {{"input", [28, 1, 160000]}}}, {"d1a076a6", {{"input", [7, 1, 160000]}}}, {"dccf3050", {{"input", [17, 1, 160000]}}}, {"ef60c196", {{"input", [10, 1, 160000]}}}, {"fe5ae199", {{"input", [13, 1, 160000]}}}, {"ffc2aaa2", {{"input", [6, 1, 160000]}}}})))] { + tensor sincnet_wav_norm1d_bias = const()[name = string("sincnet_wav_norm1d_bias"), val = tensor([0x1.73505ep-5])]; + tensor sincnet_wav_norm1d_weight = const()[name = string("sincnet_wav_norm1d_weight"), val = tensor([0x1.43f862p-7])]; + tensor sincnet_norm1d_0_bias = const()[name = string("sincnet_norm1d_0_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor sincnet_norm1d_0_weight = const()[name = string("sincnet_norm1d_0_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448)))]; + tensor sincnet_conv1d_1_bias = const()[name = string("sincnet_conv1d_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(832)))]; + tensor sincnet_conv1d_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25216))))[name = string("sincnet_conv1d_1_weight_quantized")]; + tensor sincnet_norm1d_1_bias = const()[name = string("sincnet_norm1d_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25536)))]; + tensor sincnet_norm1d_1_weight = const()[name = string("sincnet_norm1d_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25856)))]; + tensor sincnet_conv1d_2_bias = const()[name = string("sincnet_conv1d_2_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26176)))]; + tensor sincnet_conv1d_2_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44608))))[name = string("sincnet_conv1d_2_weight_quantized")]; + tensor sincnet_norm1d_2_bias = const()[name = string("sincnet_norm1d_2_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44928)))]; + tensor sincnet_norm1d_2_weight = const()[name = string("sincnet_norm1d_2_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45248)))]; + tensor linear_0_bias = const()[name = string("linear_0_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45568)))]; + tensor linear_0_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78976))))[name = string("linear_0_weight_quantized")]; + tensor linear_1_bias = const()[name = string("linear_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79552)))]; + tensor linear_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96576))))[name = string("linear_1_weight_quantized")]; + tensor classifier_bias = const()[name = string("classifier_bias"), val = tensor([-0x1.00e888p+0, 0x1.67cb52p-2, 0x1.3d87fp-1, 0x1.c8aa8p-2, -0x1.445f5ep-2, -0x1.591274p-1, -0x1.8fb70ep-2])]; + tensor classifier_weight = const()[name = string("classifier_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97152)))]; + fp32 var_9 = const()[name = string("op_9"), val = fp32(0x1.47ae14p-7)]; + fp32 var_24 = const()[name = string("op_24"), val = fp32(0x1.4f8b58p-17)]; + tensor waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = string("waveform")]; + tensor filters_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120960))))[name = string("filters_quantized")]; + string outputs_pad_type_0 = const()[name = string("outputs_pad_type_0"), val = string("valid")]; + tensor outputs_strides_0 = const()[name = string("outputs_strides_0"), val = tensor([10])]; + tensor outputs_pad_0 = const()[name = string("outputs_pad_0"), val = tensor([0, 0])]; + tensor outputs_dilations_0 = const()[name = string("outputs_dilations_0"), val = tensor([1])]; + int32 outputs_groups_0 = const()[name = string("outputs_groups_0"), val = int32(1)]; + tensor outputs = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters_quantized, x = waveform)[name = string("outputs")]; + tensor input_1 = abs(x = outputs)[name = string("input_1")]; + tensor var_119 = const()[name = string("op_119"), val = tensor([3])]; + tensor var_120 = const()[name = string("op_120"), val = tensor([3])]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([0, 0])]; + bool input_3_ceil_mode_0 = const()[name = string("input_3_ceil_mode_0"), val = bool(false)]; + tensor input_3 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1)[name = string("input_3")]; + tensor input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = string("input_5")]; + tensor input_7 = leaky_relu(alpha = var_9, x = input_5)[name = string("input_7")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1])]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([0, 0])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor input_9 = conv(bias = sincnet_conv1d_1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight_quantized, x = input_7)[name = string("input_9")]; + tensor var_135 = const()[name = string("op_135"), val = tensor([3])]; + tensor var_136 = const()[name = string("op_136"), val = tensor([3])]; + string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("custom")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0])]; + bool input_11_ceil_mode_0 = const()[name = string("input_11_ceil_mode_0"), val = bool(false)]; + tensor input_11 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9)[name = string("input_11")]; + tensor input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = string("input_13")]; + tensor input_15 = leaky_relu(alpha = var_9, x = input_13)[name = string("input_15")]; + string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")]; + tensor input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor([1])]; + tensor input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor([0, 0])]; + tensor input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor([1])]; + int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)]; + tensor input_17 = conv(bias = sincnet_conv1d_2_bias, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight_quantized, x = input_15)[name = string("input_17")]; + tensor var_151 = const()[name = string("op_151"), val = tensor([3])]; + tensor var_152 = const()[name = string("op_152"), val = tensor([3])]; + string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")]; + tensor input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor([0, 0])]; + bool input_19_ceil_mode_0 = const()[name = string("input_19_ceil_mode_0"), val = bool(false)]; + tensor input_19 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17)[name = string("input_19")]; + tensor input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = string("input_21")]; + tensor x = leaky_relu(alpha = var_9, x = input_21)[name = string("x")]; + tensor var_163 = const()[name = string("op_163"), val = tensor([0, 2, 1])]; + int32 var_172 = const()[name = string("op_172"), val = int32(128)]; + int32 var_173 = const()[name = string("op_173"), val = int32(8)]; + tensor input_23 = transpose(perm = var_163, x = x)[name = string("transpose_2")]; + tensor var_207_shape = shape(x = input_23)[name = string("op_207_shape")]; + int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)]; + bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)]; + int32 select_0 = const()[name = string("select_0"), val = int32(0)]; + int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)]; + int32 gather_0 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = select_0, validate_indices = gather_0_validate_indices_0, x = var_207_shape)[name = string("gather_0")]; + int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)]; + bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)]; + tensor concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_173, gather_0, var_172))[name = string("concat_0")]; + fp32 hx_1_value_0 = const()[name = string("hx_1_value_0"), val = fp32(0x0p+0)]; + tensor hx_1 = fill(shape = concat_0, value = hx_1_value_0)[name = string("hx_1")]; + tensor input_23_batch_first_transpose_perm_0 = const()[name = string("input_23_batch_first_transpose_perm_0"), val = tensor([1, 0, 2])]; + int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(4)]; + int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; + tensor split_0_0, tensor split_0_1, tensor split_0_2, tensor split_0_3 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = hx_1)[name = string("split_0")]; + int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(4)]; + int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; + tensor split_1_0, tensor split_1_1, tensor split_1_2, tensor split_1_3 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = hx_1)[name = string("split_1")]; + tensor add_0 = const()[name = string("add_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121344)))]; + tensor add_1 = const()[name = string("add_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123456)))]; + tensor concat_6_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156352))))[name = string("concat_6_quantized")]; + tensor concat_7_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224064))))[name = string("concat_7_quantized")]; + tensor concat_8_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256960))))[name = string("concat_8_quantized")]; + tensor concat_9_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324672))))[name = string("concat_9_quantized")]; + tensor split_10_split_sizes_0 = const()[name = string("split_10_split_sizes_0"), val = tensor([1, 1])]; + int32 split_10_axis_0 = const()[name = string("split_10_axis_0"), val = int32(0)]; + tensor split_10_0, tensor split_10_1 = split(axis = split_10_axis_0, split_sizes = split_10_split_sizes_0, x = split_0_0)[name = string("split_10")]; + int32 concat_10_axis_0 = const()[name = string("concat_10_axis_0"), val = int32(2)]; + bool concat_10_interleave_0 = const()[name = string("concat_10_interleave_0"), val = bool(false)]; + tensor concat_10 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (split_10_0, split_10_1))[name = string("concat_10")]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_0_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_h0_reshaped_axes_0, x = concat_10)[name = string("input_25_lstm_layer_0_lstm_h0_reshaped")]; + tensor split_11_split_sizes_0 = const()[name = string("split_11_split_sizes_0"), val = tensor([1, 1])]; + int32 split_11_axis_0 = const()[name = string("split_11_axis_0"), val = int32(0)]; + tensor split_11_0, tensor split_11_1 = split(axis = split_11_axis_0, split_sizes = split_11_split_sizes_0, x = split_1_0)[name = string("split_11")]; + int32 concat_11_axis_0 = const()[name = string("concat_11_axis_0"), val = int32(2)]; + bool concat_11_interleave_0 = const()[name = string("concat_11_interleave_0"), val = bool(false)]; + tensor concat_11 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (split_11_0, split_11_1))[name = string("concat_11")]; + tensor input_25_lstm_layer_0_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_0_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_c0_reshaped_axes_0, x = concat_11)[name = string("input_25_lstm_layer_0_lstm_c0_reshaped")]; + string input_25_lstm_layer_0_direction_0 = const()[name = string("input_25_lstm_layer_0_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_0_output_sequence_0 = const()[name = string("input_25_lstm_layer_0_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_0_cell_activation_0 = const()[name = string("input_25_lstm_layer_0_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_0_activation_0 = const()[name = string("input_25_lstm_layer_0_activation_0"), val = string("tanh")]; + tensor input_23_batch_first_transpose = transpose(perm = input_23_batch_first_transpose_perm_0, x = input_23)[name = string("transpose_1")]; + tensor input_25_lstm_layer_0_0, tensor input_25_lstm_layer_0_1, tensor input_25_lstm_layer_0_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_c0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_7_quantized, weight_hh_back = concat_9_quantized, weight_ih = concat_6_quantized, weight_ih_back = concat_8_quantized, x = input_23_batch_first_transpose)[name = string("input_25_lstm_layer_0")]; + tensor add_2 = const()[name = string("add_2"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326784)))]; + tensor add_3 = const()[name = string("add_3"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328896)))]; + tensor concat_16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462144))))[name = string("concat_16_quantized")]; + tensor concat_17_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(464256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529856))))[name = string("concat_17_quantized")]; + tensor concat_18_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(663104))))[name = string("concat_18_quantized")]; + tensor concat_19_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(665216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(730816))))[name = string("concat_19_quantized")]; + tensor split_20_split_sizes_0 = const()[name = string("split_20_split_sizes_0"), val = tensor([1, 1])]; + int32 split_20_axis_0 = const()[name = string("split_20_axis_0"), val = int32(0)]; + tensor split_20_0, tensor split_20_1 = split(axis = split_20_axis_0, split_sizes = split_20_split_sizes_0, x = split_0_1)[name = string("split_20")]; + int32 concat_20_axis_0 = const()[name = string("concat_20_axis_0"), val = int32(2)]; + bool concat_20_interleave_0 = const()[name = string("concat_20_interleave_0"), val = bool(false)]; + tensor concat_20 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (split_20_0, split_20_1))[name = string("concat_20")]; + tensor input_25_lstm_layer_1_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_1_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_1_lstm_h0_reshaped_axes_0, x = concat_20)[name = string("input_25_lstm_layer_1_lstm_h0_reshaped")]; + tensor split_21_split_sizes_0 = const()[name = string("split_21_split_sizes_0"), val = tensor([1, 1])]; + int32 split_21_axis_0 = const()[name = string("split_21_axis_0"), val = int32(0)]; + tensor split_21_0, tensor split_21_1 = split(axis = split_21_axis_0, split_sizes = split_21_split_sizes_0, x = split_1_1)[name = string("split_21")]; + int32 concat_21_axis_0 = const()[name = string("concat_21_axis_0"), val = int32(2)]; + bool concat_21_interleave_0 = const()[name = string("concat_21_interleave_0"), val = bool(false)]; + tensor concat_21 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (split_21_0, split_21_1))[name = string("concat_21")]; + tensor input_25_lstm_layer_1_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_1_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_1_lstm_c0_reshaped_axes_0, x = concat_21)[name = string("input_25_lstm_layer_1_lstm_c0_reshaped")]; + string input_25_lstm_layer_1_direction_0 = const()[name = string("input_25_lstm_layer_1_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_1_output_sequence_0 = const()[name = string("input_25_lstm_layer_1_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_1_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_1_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_1_cell_activation_0 = const()[name = string("input_25_lstm_layer_1_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_1_activation_0 = const()[name = string("input_25_lstm_layer_1_activation_0"), val = string("tanh")]; + tensor input_25_lstm_layer_1_0, tensor input_25_lstm_layer_1_1, tensor input_25_lstm_layer_1_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_1_lstm_c0_reshaped, initial_h = input_25_lstm_layer_1_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_17_quantized, weight_hh_back = concat_19_quantized, weight_ih = concat_16_quantized, weight_ih_back = concat_18_quantized, x = input_25_lstm_layer_0_0)[name = string("input_25_lstm_layer_1")]; + tensor add_4 = const()[name = string("add_4"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(732928)))]; + tensor add_5 = const()[name = string("add_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(735040)))]; + tensor concat_26_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(737152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(868288))))[name = string("concat_26_quantized")]; + tensor concat_27_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(870400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936000))))[name = string("concat_27_quantized")]; + tensor concat_28_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(938112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069248))))[name = string("concat_28_quantized")]; + tensor concat_29_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1071360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136960))))[name = string("concat_29_quantized")]; + tensor split_30_split_sizes_0 = const()[name = string("split_30_split_sizes_0"), val = tensor([1, 1])]; + int32 split_30_axis_0 = const()[name = string("split_30_axis_0"), val = int32(0)]; + tensor split_30_0, tensor split_30_1 = split(axis = split_30_axis_0, split_sizes = split_30_split_sizes_0, x = split_0_2)[name = string("split_30")]; + int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(2)]; + bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)]; + tensor concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (split_30_0, split_30_1))[name = string("concat_30")]; + tensor input_25_lstm_layer_2_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_2_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_2_lstm_h0_reshaped_axes_0, x = concat_30)[name = string("input_25_lstm_layer_2_lstm_h0_reshaped")]; + tensor split_31_split_sizes_0 = const()[name = string("split_31_split_sizes_0"), val = tensor([1, 1])]; + int32 split_31_axis_0 = const()[name = string("split_31_axis_0"), val = int32(0)]; + tensor split_31_0, tensor split_31_1 = split(axis = split_31_axis_0, split_sizes = split_31_split_sizes_0, x = split_1_2)[name = string("split_31")]; + int32 concat_31_axis_0 = const()[name = string("concat_31_axis_0"), val = int32(2)]; + bool concat_31_interleave_0 = const()[name = string("concat_31_interleave_0"), val = bool(false)]; + tensor concat_31 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (split_31_0, split_31_1))[name = string("concat_31")]; + tensor input_25_lstm_layer_2_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_lstm_layer_2_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_2_lstm_c0_reshaped_axes_0, x = concat_31)[name = string("input_25_lstm_layer_2_lstm_c0_reshaped")]; + string input_25_lstm_layer_2_direction_0 = const()[name = string("input_25_lstm_layer_2_direction_0"), val = string("bidirectional")]; + bool input_25_lstm_layer_2_output_sequence_0 = const()[name = string("input_25_lstm_layer_2_output_sequence_0"), val = bool(true)]; + string input_25_lstm_layer_2_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_2_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_lstm_layer_2_cell_activation_0 = const()[name = string("input_25_lstm_layer_2_cell_activation_0"), val = string("tanh")]; + string input_25_lstm_layer_2_activation_0 = const()[name = string("input_25_lstm_layer_2_activation_0"), val = string("tanh")]; + tensor input_25_lstm_layer_2_0, tensor input_25_lstm_layer_2_1, tensor input_25_lstm_layer_2_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_2_lstm_c0_reshaped, initial_h = input_25_lstm_layer_2_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_27_quantized, weight_hh_back = concat_29_quantized, weight_ih = concat_26_quantized, weight_ih_back = concat_28_quantized, x = input_25_lstm_layer_1_0)[name = string("input_25_lstm_layer_2")]; + tensor add_6 = const()[name = string("add_6"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1139072)))]; + tensor add_7 = const()[name = string("add_7"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1141184)))]; + tensor concat_36_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1143296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274432))))[name = string("concat_36_quantized")]; + tensor concat_37_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1276544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1342144))))[name = string("concat_37_quantized")]; + tensor concat_38_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1344256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475392))))[name = string("concat_38_quantized")]; + tensor concat_39_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1477504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1543104))))[name = string("concat_39_quantized")]; + tensor split_40_split_sizes_0 = const()[name = string("split_40_split_sizes_0"), val = tensor([1, 1])]; + int32 split_40_axis_0 = const()[name = string("split_40_axis_0"), val = int32(0)]; + tensor split_40_0, tensor split_40_1 = split(axis = split_40_axis_0, split_sizes = split_40_split_sizes_0, x = split_0_3)[name = string("split_40")]; + int32 concat_40_axis_0 = const()[name = string("concat_40_axis_0"), val = int32(2)]; + bool concat_40_interleave_0 = const()[name = string("concat_40_interleave_0"), val = bool(false)]; + tensor concat_40 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (split_40_0, split_40_1))[name = string("concat_40")]; + tensor input_25_batch_first_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_h0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_batch_first_lstm_h0_reshaped = squeeze(axes = input_25_batch_first_lstm_h0_reshaped_axes_0, x = concat_40)[name = string("input_25_batch_first_lstm_h0_reshaped")]; + tensor split_41_split_sizes_0 = const()[name = string("split_41_split_sizes_0"), val = tensor([1, 1])]; + int32 split_41_axis_0 = const()[name = string("split_41_axis_0"), val = int32(0)]; + tensor split_41_0, tensor split_41_1 = split(axis = split_41_axis_0, split_sizes = split_41_split_sizes_0, x = split_1_3)[name = string("split_41")]; + int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(2)]; + bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)]; + tensor concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (split_41_0, split_41_1))[name = string("concat_41")]; + tensor input_25_batch_first_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_c0_reshaped_axes_0"), val = tensor([0])]; + tensor input_25_batch_first_lstm_c0_reshaped = squeeze(axes = input_25_batch_first_lstm_c0_reshaped_axes_0, x = concat_41)[name = string("input_25_batch_first_lstm_c0_reshaped")]; + string input_25_batch_first_direction_0 = const()[name = string("input_25_batch_first_direction_0"), val = string("bidirectional")]; + bool input_25_batch_first_output_sequence_0 = const()[name = string("input_25_batch_first_output_sequence_0"), val = bool(true)]; + string input_25_batch_first_recurrent_activation_0 = const()[name = string("input_25_batch_first_recurrent_activation_0"), val = string("sigmoid")]; + string input_25_batch_first_cell_activation_0 = const()[name = string("input_25_batch_first_cell_activation_0"), val = string("tanh")]; + string input_25_batch_first_activation_0 = const()[name = string("input_25_batch_first_activation_0"), val = string("tanh")]; + tensor input_25_batch_first_0, tensor input_25_batch_first_1, tensor input_25_batch_first_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_batch_first_lstm_c0_reshaped, initial_h = input_25_batch_first_lstm_h0_reshaped, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_37_quantized, weight_hh_back = concat_39_quantized, weight_ih = concat_36_quantized, weight_ih_back = concat_38_quantized, x = input_25_lstm_layer_2_0)[name = string("input_25_batch_first")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([1, 0, 2])]; + tensor input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = string("transpose_0")]; + tensor input_27 = linear(bias = linear_0_bias, weight = linear_0_weight_quantized, x = input_25)[name = string("linear_0")]; + fp32 var_220 = const()[name = string("op_220"), val = fp32(0x1.47ae14p-7)]; + tensor input_29 = leaky_relu(alpha = var_220, x = input_27)[name = string("input_29")]; + tensor input_31 = linear(bias = linear_1_bias, weight = linear_1_weight_quantized, x = input_29)[name = string("linear_1")]; + fp32 var_225 = const()[name = string("op_225"), val = fp32(0x1.47ae14p-7)]; + tensor input_33 = leaky_relu(alpha = var_225, x = input_31)[name = string("input_33")]; + tensor input_1_1 = linear(bias = classifier_bias, weight = classifier_weight, x = input_33)[name = string("linear_2")]; + int32 var_231 = const()[name = string("op_231"), val = int32(-1)]; + tensor var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = string("op_232_softmax")]; + fp32 var_232_epsilon_0 = const()[name = string("op_232_epsilon_0"), val = fp32(0x1p-149)]; + tensor output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = string("op_232")]; + } -> (output); +} \ No newline at end of file diff --git a/segmentation-3.0-w8a16.mlmodelc/weights/weight.bin b/segmentation-3.0-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..f594726b766cd78b580c8102512c06d145659fab --- /dev/null +++ b/segmentation-3.0-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b5c162f15773b0417411bafc50141870d504ca9a3a896211dae643d7787de87 +size 1545216 diff --git a/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..f63e5dd1e8e2950dfc776daee891d55faea22188 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:03ddd5dfec7426073ede96c100536550d5384fef9a560671cb7960da85405f8b +size 243 diff --git a/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..71384da419516b72f675d2043634ba07fc936fb9 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d7b0a30f1b8342a359c6350597d1475533144e1c6816f5da7574558d40a1356b +size 403 diff --git a/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..660bd5912e2dbd6540368366b342ba414355c193 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 116, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 116, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6733440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624384))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..03358c348fdfdb203048e4ff256f51a5112a28db --- /dev/null +++ b/wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:01c4bb6f197e6ec961e8eafd6ba1b72a84b06a00dd14d41a97033ab3e3498720 +size 7625840 diff --git a/wespeaker-chunk-emb-s12-w116.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s12-w116.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2ec9ef186d304bb606b2ff2df2c61c28ff23630e --- /dev/null +++ b/wespeaker-chunk-emb-s12-w116.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:68234b4f7e8b542bd61d973184fc0c750af22085f8e5eb2d8be85514951da818 +size 243 diff --git a/wespeaker-chunk-emb-s12-w116.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s12-w116.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..4788a5a368b293fd61e73afeedad142bf84813d5 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w116.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd2d8e8773a78800cd638b44748de4d7e1860f3b2727e49b199def03fd01f6d2 +size 172 diff --git a/wespeaker-chunk-emb-s12-w116.mlmodelc/model.mil b/wespeaker-chunk-emb-s12-w116.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..2d416d1bfaf88dff0a35791b81dd25d7b0b83c3f --- /dev/null +++ b/wespeaker-chunk-emb-s12-w116.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 116, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 116, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26583232)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s12-w116.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s12-w116.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..433afad1c6d5ed0f5e7fc12d7a8f13a65a296f8e --- /dev/null +++ b/wespeaker-chunk-emb-s12-w116.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:369991f2f7eff6dd7e89dd804fff843e4e1a576b40fda757f819db0907cf2688 +size 30146816 diff --git a/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..faec245abc8173d82cd71a2b41d44fb35ca703b9 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e98c5e13fcf6a69fa084da0009f0db2e01eb0ee907a74f34907353e03055cb76 +size 243 diff --git a/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..faf4edfedacd9dd7c515c0813e64570caa4086e5 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d6eabaffe6d680567150c3b25dff3ff50c41b561e6cf87d6b92a3df41055e7a0 +size 401 diff --git a/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..cf3664d31211e77b7eabd60418d41fec66bf84b1 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 22, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 22, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6686400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6855424))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..84cff46493272c9b220920a64e2dcf47c2691167 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:103738e256ffefb91b41703e38b8d665ed7995027a355e7c11b774eb8c598167 +size 6855752 diff --git a/wespeaker-chunk-emb-s12-w22.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s12-w22.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..11fda718a226e98c4998a54979be8c5d138ece90 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w22.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aafa741516f6315fbf1aa462506532b29ade3735288894546ae6416fb3c43ce9 +size 243 diff --git a/wespeaker-chunk-emb-s12-w22.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s12-w22.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..8f5d276a5fa5766fc9740b29503b77d89b4c4d05 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w22.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b9d52f76d5063a5afa2617f9f116d871feb43465d04d794807782e1da7a39fb8 +size 170 diff --git a/wespeaker-chunk-emb-s12-w22.mlmodelc/model.mil b/wespeaker-chunk-emb-s12-w22.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..f16e0e628b63e20d112575abaf65d7559a0a9f37 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w22.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 22, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 22, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26536192)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s12-w22.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s12-w22.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..bc527d6ae69dda619183456f98b6e7dd38fcc216 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w22.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:760c74421515a9407815321f45096a6b3c347fc79e8af69177a69700d7689acc +size 27212096 diff --git a/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..71a53c5b189bf11e8f6bfb27389aa04a4d151812 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6310ac4007076f47144f9ecd7ec849b5248e1b2b71a47b555b8292950f3cdbd1 +size 243 diff --git a/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..4905171ad558911505b9ae691547e1ec0ebb59df --- /dev/null +++ b/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bbc00ebd185ab7dde80382eafd745661b7e21097052016eaeecaa3b5e5f63c18 +size 401 diff --git a/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..34c2317867aac7e0ec529f5e5744bd6efc5d03f8 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 37, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 37, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6693952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6978176))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..aa6d55bf8b434618fb3bb7e6c5fa29f6ace58eee --- /dev/null +++ b/wespeaker-chunk-emb-s12-w37-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1fe3a54042773c62f687b178d92c46f78cf1d7ad3ee1d24b2412cfeb934f36a5 +size 6978684 diff --git a/wespeaker-chunk-emb-s12-w37.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s12-w37.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0c11c3e06c53599268c0840be6c476e15bb1df48 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w37.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2b90333ff02abd2deac8f5681908b07c8e4b288f2cbe92ecd2b7b220f8c184f +size 243 diff --git a/wespeaker-chunk-emb-s12-w37.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s12-w37.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..1cb947a3264c8f208c883cf831c22e5a65e28f76 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w37.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ad9d4580fa5349282e6d02f2c471c459045fe9c90f79242565c3e55ad6b80bb +size 170 diff --git a/wespeaker-chunk-emb-s12-w37.mlmodelc/model.mil b/wespeaker-chunk-emb-s12-w37.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..e1d59943a160dfe7d3ed33fa081d829e875b21ab --- /dev/null +++ b/wespeaker-chunk-emb-s12-w37.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 37, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 37, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26543744)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s12-w37.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s12-w37.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..4b5aebf8a141cfe5f6f2906597022a9a423df7fb --- /dev/null +++ b/wespeaker-chunk-emb-s12-w37.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cab4592f768a3490f709dd95e0405fa85b9c1d63c26d47cba73394841a7b5d07 +size 27680448 diff --git a/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..6f3a1d45cdcf5f0b8eaacfca6ac9f4e32c8466aa --- /dev/null +++ b/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47551a8aed34f66ae4660afa2cae75c6f068a7ddd6e9c9e387d424965b8ab334 +size 243 diff --git a/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..835f365934b45313bbb41a382962476d55451ef1 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e8cae7d7f6a87ae86a1191d43700667f3e1ac256761438f9ed59eda7224c6b68 +size 403 diff --git a/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..5bcf6f862c6e99796e50ef795d01a0bcd7d6c1bb --- /dev/null +++ b/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 53, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 53, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6701952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7109056))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..e45a87e44802710fdc49b68a7f8b50e7ea9da2e1 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w53-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5cb8b229dfb70b6157cd04a865e36859bd43bcb55c3499b6ef531197570c64ac +size 7109756 diff --git a/wespeaker-chunk-emb-s12-w53.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s12-w53.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ae34c29f8a6e5974651c3891c3b1dca3be2eadc8 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w53.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f28514f1515e416f69935b2a391ccab7a1c9daab40899c447952be67045ab70 +size 243 diff --git a/wespeaker-chunk-emb-s12-w53.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s12-w53.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..4497e21740d42569f5fafa87d1a8200b1ed4d377 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w53.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8338f256e91562a68acaa7dd585f8975ee5ebf259471a0b8fbca0a02c3adb958 +size 172 diff --git a/wespeaker-chunk-emb-s12-w53.mlmodelc/model.mil b/wespeaker-chunk-emb-s12-w53.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..3307be486c440f95c200d2e0fc4c4337240c5e46 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w53.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 53, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 53, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26551744)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s12-w53.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s12-w53.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..17604b069115bddacbbe8e11035fb3f79b2130ce --- /dev/null +++ b/wespeaker-chunk-emb-s12-w53.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b8f5a65793ccd84603497c550af0b107f3227102a564831e20c81adaa831c414 +size 28179968 diff --git a/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b938e3061d89009b1617c5626f36836e956fdaa6 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee900746dc8da58d399005e8618dc494366662bf5f55678d694848e827a322ba +size 243 diff --git a/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..f52e04af9b112f030f80868b49192bbb51ad789a --- /dev/null +++ b/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:139827d9cb2c49c162cf53dddff0e4ec2273f433a9a895119a693d4826bd7ac5 +size 403 diff --git a/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..aa287c4cf88db98f168ffe5b5fce27968bef90ba --- /dev/null +++ b/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 84, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 84, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6717440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7362624))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..512375245b7299cfa016c5eae6676521c4b0b94e --- /dev/null +++ b/wespeaker-chunk-emb-s12-w84-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f8f6fd41810359840b861335fc03ad479c3cf02517556031c31ae3b062a8a91a +size 7363696 diff --git a/wespeaker-chunk-emb-s12-w84.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s12-w84.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..e529368bfe6b1d39d7876440eafd5c2bf63a0480 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w84.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2baaac92ab37ced0a14006989e23748cbedfd58d3a3858da4ae0c77f3649a47a +size 243 diff --git a/wespeaker-chunk-emb-s12-w84.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s12-w84.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..886bce0c70dd39795e941682d5d9fa4432ac2ee2 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w84.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d0347bea7234fb8a60519a05c6a150b5523fb86f0a50340f3dd30036e0ac1e7e +size 172 diff --git a/wespeaker-chunk-emb-s12-w84.mlmodelc/model.mil b/wespeaker-chunk-emb-s12-w84.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..21aebdb73ce85eeaa567ec102bacc35e91f67fbf --- /dev/null +++ b/wespeaker-chunk-emb-s12-w84.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 84, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 84, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26567232)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s12-w84.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s12-w84.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..7f65829555503d9c8899a496a582c5d1519025f7 --- /dev/null +++ b/wespeaker-chunk-emb-s12-w84.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2fe670276b392230ae0fad72c4fbce5b6bd6fd0002905e6f352b65afde0e7cf +size 29147776 diff --git a/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..e8bc514f39e2afc9c629abea08ab1199c45066b6 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb6ced3428a286f5e5b3f3950c5208b6795e63bb558228873fdb3ffc6f8d6897 +size 243 diff --git a/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..90bffdbcd8c84467453fabc81e33b74f0654befe --- /dev/null +++ b/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29eab81e0de0138d5d00ffd2ba1272231b9c5d2e711bf693af88303c48d59a53 +size 403 diff --git a/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..808d8e7d98d3b97c3b2180de66e49c64e12584bb --- /dev/null +++ b/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 106, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 106, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6728448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7542592))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..0ec653c4f4b30a3f09dcb701996e7b8fef651a94 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w106-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a34a0d794f6985db61c0af8cc3e550255828aa23fc083fe6e8dc0a26524b01e6 +size 7543928 diff --git a/wespeaker-chunk-emb-s13-w106.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s13-w106.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0e0b14573d4d94be1c12bc7a630702cc641ff0b1 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w106.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d9e87e7bab26220512e7a3aca2933c10c676a520c4a602990786ca1c91f57582 +size 243 diff --git a/wespeaker-chunk-emb-s13-w106.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s13-w106.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..38f71bbdc1753e12fadf53a58c051e5977ca678f --- /dev/null +++ b/wespeaker-chunk-emb-s13-w106.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0a99481d1d92fa68137e33ce07d6b742609c90e31542b21c9c68e935ecf8eb73 +size 172 diff --git a/wespeaker-chunk-emb-s13-w106.mlmodelc/model.mil b/wespeaker-chunk-emb-s13-w106.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..214c025dfcd9a2cbf9e8857ab4be0bf65a315f5f --- /dev/null +++ b/wespeaker-chunk-emb-s13-w106.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 106, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 106, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26578240)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s13-w106.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s13-w106.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..4c382dd4e8eb3941cc326e695a7abe92bd785f8c --- /dev/null +++ b/wespeaker-chunk-emb-s13-w106.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:87801dcc035c5c451c82c240a237248e6f708b18ea4b39e244e23f4a317544fb +size 29834624 diff --git a/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ab226289f68d327b5c791ef9c97fa23d1883f6ee --- /dev/null +++ b/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b01ca950730f6c578426e891762bdf22c2da25676220ec6d4336ed729b947593 +size 243 diff --git a/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..1974a0821099000b5e4338387f8b4e3f50a1684b --- /dev/null +++ b/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d3346752822700e39e6d01aa6fc3c663036e5c693e54f3b59086ece53e2cd9b5 +size 401 diff --git a/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..02224f1e9f68895ded38bc62ece46f1b349435dd --- /dev/null +++ b/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 20, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 20, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6685440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6839104))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..83a8bbe55486ad23cb5863f1c3dc736f31c0e954 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w20-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4936e7d029a2b78ba2ab29c5bd8780b68781b0aa497a530bb4baa8bd4a710b1a +size 6839408 diff --git a/wespeaker-chunk-emb-s13-w20.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s13-w20.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..eb21378d31d0fe129fa5711e3ac598c84f427645 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w20.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b1fff971a0c8f4d985db709d98ae2f9d95e238e7d7d90a2870bdc3e21cf3ee32 +size 243 diff --git a/wespeaker-chunk-emb-s13-w20.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s13-w20.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0264e7dd3d7b825b2a82a03c0b387436d9046b0d --- /dev/null +++ b/wespeaker-chunk-emb-s13-w20.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d823d1250aaec463f26c0850205fa9f558a43f44081dcf0c3c2206114d4859a1 +size 170 diff --git a/wespeaker-chunk-emb-s13-w20.mlmodelc/model.mil b/wespeaker-chunk-emb-s13-w20.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..63466c9beaf82ec83b951feeafe23f08bb4497db --- /dev/null +++ b/wespeaker-chunk-emb-s13-w20.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 20, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 20, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26535232)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s13-w20.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s13-w20.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..76d392f7f3c49815d88780ccf40da0a5a17cde11 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w20.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9bd9414ba921fe8980cdc477c86abd1e946300ec6fdecf400d11db777dd10780 +size 27149696 diff --git a/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..9fca8d327a6d63d340c89be8335ed095b339adcd --- /dev/null +++ b/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce717c494d0d77795a04da0ae0b76ef09c8b45bb67ea463a1ca97dcff9ec838b +size 243 diff --git a/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..05feacdbc825aa96648c21358cb051b8e3b3e20f --- /dev/null +++ b/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:33561273c1d699fc61c7e6c385e68cee71f68ad1afa862ffacdbc2bf8ec2e2cb +size 401 diff --git a/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..6bd8cd562402032b4ad8f2d3c6baca5bb056df89 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 34, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 34, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6692416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6953600))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..af1d4fac7ce30e63dd0ce0d86933c623a45d3fe3 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w34-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d317139a6b5928dcebdaf440a8ababa03a50e7297dbb9ef5ce1a2c34c2251d99 +size 6954072 diff --git a/wespeaker-chunk-emb-s13-w34.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s13-w34.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..34d0752ccf5b807c3d8505c08bcff5e3e6419e3a --- /dev/null +++ b/wespeaker-chunk-emb-s13-w34.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fcfd86be7a5dddcc7e29640746c5cc6abe0b9ca4213299a9ac2e5a888b887c22 +size 243 diff --git a/wespeaker-chunk-emb-s13-w34.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s13-w34.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..3155dbb9e68e76f629f3cc697dc0af981893d860 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w34.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ebf9ccd94de0ae35209d7067485b710cbfc15cc10c1df0bee53b4c700e508cf +size 170 diff --git a/wespeaker-chunk-emb-s13-w34.mlmodelc/model.mil b/wespeaker-chunk-emb-s13-w34.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..3ca830a6bb62cb27fc5fcf1a7acd2b7137aeadb4 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w34.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 34, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 34, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26542208)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s13-w34.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s13-w34.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..20409fd921b7204e23de15b83d0ddfe6e2bf3002 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w34.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35abb387a90c733c46b0bd0f0d638797aadbfcce572635de3c307eb08a46f6b9 +size 27586752 diff --git a/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2e81136eba1c003b77926f0af24bb8d58b23ac87 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1ac1bf4b3e3f765902904cd8ee1c777da7614390afc4aa6f8de9be8b6afe5e4a +size 243 diff --git a/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b461a417a61d3128c7ddeb0be499ff5c3ae8ba63 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3029b16b274f5c2379dbfa9b7709a44e5a38da1ce5121bed14c3f12f7e6d270b +size 403 diff --git a/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..5c20992a8d629fef1f1e545be7fe54c14a72e0c5 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 49, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 49, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6699904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7076288))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..e9498c704d2ff1dd6e3be4e0fa6add3dcc088dab --- /dev/null +++ b/wespeaker-chunk-emb-s13-w49-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:54fedf8bb0c85b3823caa2e92ee1388afccd06262dec5a53adaaf539c20c55aa +size 7076940 diff --git a/wespeaker-chunk-emb-s13-w49.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s13-w49.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..420f46dd276a966e5f5885652072438744ba9b6a --- /dev/null +++ b/wespeaker-chunk-emb-s13-w49.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5032c18b857c435d8a3d9e9a5cac052d91195c256cef10212dc765804f787e6f +size 243 diff --git a/wespeaker-chunk-emb-s13-w49.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s13-w49.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..6a375b703ca341b0889322bbc507f7ced0e973b7 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w49.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1c0671d3625263bb597ce6184a1bed1d5fb9a3c28e112c7011ba896e97263db3 +size 172 diff --git a/wespeaker-chunk-emb-s13-w49.mlmodelc/model.mil b/wespeaker-chunk-emb-s13-w49.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..e4c3844995ee2e87fc9bd69a8c0124eecb8a478b --- /dev/null +++ b/wespeaker-chunk-emb-s13-w49.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 49, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 49, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26549696)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s13-w49.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s13-w49.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..c049aa4bf9d77c46ba17c2fab32a8f8b365d761e --- /dev/null +++ b/wespeaker-chunk-emb-s13-w49.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e34c72c27614f76b29e5811d118530972c54bbb4cd36873a4f473bd86f97fcbd +size 28055040 diff --git a/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..c7eb46a4d9cb24696f19b678d0f6213c9afb7a36 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e4ca98d712ed979b25881c6e0421b509f132d24ae0c604a87bda72db7621a60c +size 243 diff --git a/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..8998971940bd259ba6dba5703ed6db1c4689606d --- /dev/null +++ b/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:09ec03244447bef5e4907031462d16c30fa9e45c69d177a436131cac7c700b31 +size 403 diff --git a/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..abdc13f6157b95386722610412f3f3ff572cc9be --- /dev/null +++ b/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 77, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 77, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6713920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7305344))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..e0b04ebb01fa6a2634ff173d944d3e23e13f2303 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w77-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3d245c9e2345d9312219fd73cbc91c625e8f116dd25031f103fd433770e9d07e +size 7306332 diff --git a/wespeaker-chunk-emb-s13-w77.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s13-w77.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2279d068fd589365da682e6e7b7692ddb8bb40a1 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w77.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a74b87cf8cf485e963a97d17d38e7772a9d0212665bac3e404d1021734d3d983 +size 243 diff --git a/wespeaker-chunk-emb-s13-w77.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s13-w77.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..d5dc85c76ae952ea1d3b583bcad5b78d437f299c --- /dev/null +++ b/wespeaker-chunk-emb-s13-w77.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a95652bbe8122695e4684315523930f8c6e8163ea8b9d9324ae9c65835650301 +size 172 diff --git a/wespeaker-chunk-emb-s13-w77.mlmodelc/model.mil b/wespeaker-chunk-emb-s13-w77.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..22fbcc73e6406a84f17ba78c9b4dc67940f5e4a2 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w77.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 77, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 77, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26563712)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s13-w77.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s13-w77.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..19aa1d36d0183d06b5f4ae2485d5582bdb7e6322 --- /dev/null +++ b/wespeaker-chunk-emb-s13-w77.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23f810841f8431d258db2dccd517c6086e63ef7879df119b25fe1f528adb8011 +size 28929216 diff --git a/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..06187c6bcb19f5c0fcc66d21584a749f91e9c4e2 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f5b3d1dac7ea346578282fb2026ddb50d18509c428a1ead9738f3b4f587212ac +size 243 diff --git a/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b4588d724d25b8e11363e402a880a2e70cad1cc9 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cae8ac2b5a0a80e81010f8038caf5bf0630be71fb8338b0414e659dfd57bb698 +size 400 diff --git a/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..d5431e01bcff27686e241cde5008b4b5ca5804e1 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 16, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 16, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6683392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6806336))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..7c93aa1730f13e2ae07075c8bdeedda639360173 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w16-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ae7f162baffa23e4fa2e3ffee3c009cd3b689c2c4d00ffd9043df2cd2dc3675 +size 6806592 diff --git a/wespeaker-chunk-emb-s16-w16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2373b8f46fd9744a959172eb7d243db9d4d6234e --- /dev/null +++ b/wespeaker-chunk-emb-s16-w16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd79814ab8b0333f1afdd2e49b2ce22e0ca06d18b8169200d18043812f1a783f +size 243 diff --git a/wespeaker-chunk-emb-s16-w16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a64af506ced09c0f05c768a32fd8615f62d05b96 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:db1f370bbf800933b8fefdad69a36955125931c0f114c7f75cf09bd2d5239734 +size 170 diff --git a/wespeaker-chunk-emb-s16-w16.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..da892fece227e277367c5cac8472c26f44d73547 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 16, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 16, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26533184)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..cd14ce0baaf543e9b943cb806de91971a6533718 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0568f81968d194c9ebb90ce1df5c6ee37376b4dd65372f6d18f64e469d2e8a2e +size 27024768 diff --git a/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..5447a96826cbb1a0ea2234789887cdf645981bb9 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:36c84ca943c41fa357414224e56451e7a8afd0cbf2a69a842d56560d3bf2b034 +size 243 diff --git a/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..dca717eb75b6ae36cc86b81916c8d91d96e4dbfb --- /dev/null +++ b/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:18c165bfddc7e7dfe1d6c0d3e81cd53720a88e1d6ee75096576c30889c417f7d +size 400 diff --git a/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..30b0df52b9b7889834f760ffed5a66c1a0735817 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 24, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 24, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6687424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6871808))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..301861cf37c0ae858f6867450ed1c72b9f0046be --- /dev/null +++ b/wespeaker-chunk-emb-s16-w24-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0890f59701c823ce10eca3ad5a476865fbf401344e929d65081f772ef998c09b +size 6872160 diff --git a/wespeaker-chunk-emb-s16-w24.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w24.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a60e9ed071850c46f4a505bef8d4b14c2802615e --- /dev/null +++ b/wespeaker-chunk-emb-s16-w24.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b8e0b67ad41a73c963779bff06754a4cc1f9d58b3767f71dc5b52c852424bb74 +size 243 diff --git a/wespeaker-chunk-emb-s16-w24.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w24.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..92a992403873d8e522c3d4eafa4ba9abdca6b8d3 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w24.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9fdc123fe5a2e585ffd4a7ed477b9a8394bcd839482145158dadc9fcafc6e763 +size 170 diff --git a/wespeaker-chunk-emb-s16-w24.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w24.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..e8def6780ce7bf4f7736b73f53e6f4a2f12fc096 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w24.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 24, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 24, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26537216)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w24.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w24.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..ca98657020e9920043126b4a3581ff7190df22fd --- /dev/null +++ b/wespeaker-chunk-emb-s16-w24.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5f0dd040aa589459353b8127660f4fbde1dbb92cf05021c0a0aa4c2acd5375a +size 27274560 diff --git a/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..cb5abb456ef76c4f7938d080d4cf7ce2d7933652 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c3a30064ab55cb651fa4ada3d660c8995e6abfddfff9d5a5c133c91c1bf4f697 +size 243 diff --git a/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ab812be8ab515b3773fe2fbe0386c789b12c4488 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:efed461a1dafba0b3a06a2e0004c0fc9c227ad088e46d56543574ce5ce97b07c +size 400 diff --git a/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..c5c277c565c44df2e56160315e2dd787c1c0e409 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 32, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 32, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6691392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6937216))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..8b46d2b699fad682b1822fbe9ec7f5f1c7540d3e --- /dev/null +++ b/wespeaker-chunk-emb-s16-w32-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9581af6e143cb5d933bfdb932937adaf9d1c56c127a14b4d492b64ee634e7645 +size 6937664 diff --git a/wespeaker-chunk-emb-s16-w32.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w32.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..f56f3314f3199a8562b833c836497792ecf429ef --- /dev/null +++ b/wespeaker-chunk-emb-s16-w32.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5be2cb6b18026a44fb5ceb60915069d5a9a5872b8cc6f38f6b8e1a8f28c9da12 +size 243 diff --git a/wespeaker-chunk-emb-s16-w32.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w32.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2b6dc819b23bd43e2cc56bc6934fc6dffa7a11e4 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w32.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:91aedf36dbe5a15d4e5044bdcc969ea60d1a2a13085eec9a449a4c6ca62f54a3 +size 170 diff --git a/wespeaker-chunk-emb-s16-w32.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w32.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..ccd266cec6f000ba0b4ce7168a724bafa89bab32 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w32.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 32, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 32, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26541184)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w32.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w32.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..bd03e60d87eb63fb3c9c8a7bf152bd17670df3a8 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w32.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a4fc4bfb16563c6c843ddd21c0865c6aac7d0d77a991b33e79ba45f299ba35e +size 27524288 diff --git a/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..336ef2b3369c812b2686dc45ab09d8642dd5d7bc --- /dev/null +++ b/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b1d7cb5bbeca6852899e6840c312fd305dc7bb2d513fcb5659a35bcba3236dc9 +size 243 diff --git a/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..73d99377ad58d6a10038bec19efadefd1aafe531 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8946e72a629326e9395ab47ee2155ed9d918f5c4119ab1be3c1df30f932c6a46 +size 400 diff --git a/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..d7bf5ddee4953e7f274a324ffea8d5ad69347827 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 40, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 40, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6695424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7002688))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a53e5b768dfa274deed13a155cdce2b999679ce4 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w40-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c124b6df081e869496f33e64084a7b09cffed385d0d951da8c6829d1a2dd7a20 +size 7003232 diff --git a/wespeaker-chunk-emb-s16-w40.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w40.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..8bc056f251a0db82f4c7f33e9221ee844450234d --- /dev/null +++ b/wespeaker-chunk-emb-s16-w40.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0cf0fab6a6f2323f931b06f7b0ee0170710314cc522a6654600ceb7d583baa62 +size 243 diff --git a/wespeaker-chunk-emb-s16-w40.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w40.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..f40be75042a135a1a308e3524ade9792a6cc253a --- /dev/null +++ b/wespeaker-chunk-emb-s16-w40.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1fac1b58c59cba684f3dc3b77a4aba3ced8ab5752e56c7dcc865b7fada7aff1f +size 170 diff --git a/wespeaker-chunk-emb-s16-w40.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w40.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..ea463eb12d45a82ce848241c1633b956b7e35294 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w40.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 40, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 40, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26545216)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w40.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w40.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..5e03a58fd666204b1fcd4c03b312607c43ff4348 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w40.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3729a68e467ffbfd8df4ad1fa2c0097765a971a30a948e15a8661fbc218ce0cd +size 27774080 diff --git a/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0839cd8118db705db09913a0f0b4f7c2001f7bcd --- /dev/null +++ b/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c70944cf9cb30e29183e0dd98834a60bc595e679600541cd14dc9420d9f8891e +size 243 diff --git a/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..156a740150d09e6699227e445b42f7baef96144a --- /dev/null +++ b/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb61883bac591c4818de9bd5083e4e6de9efe3b7f7feecefb8694ed54b94a6bd +size 402 diff --git a/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..4e64a78a51a94da41fd20b8904e829210ff87374 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 56, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 56, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6703424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7133568))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..0c151d5ca834f79bdd3b93e69bfc91ca7baa50d7 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w56-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ec925661d7ada3f01aa57d54780453aa0a1339120765162148b268d5336312af +size 7134304 diff --git a/wespeaker-chunk-emb-s16-w56.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w56.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..55b06fa61579276d44f966491e0d47de08ba7736 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w56.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:672ce9247d166df35420effe4e9f0ad64c8a43d14b33ae54fce3c956a7885eb5 +size 243 diff --git a/wespeaker-chunk-emb-s16-w56.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w56.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..c6a0493d069a415379634df7312dc93d56b122ee --- /dev/null +++ b/wespeaker-chunk-emb-s16-w56.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9e9a711c4424219faba775d37c7dce3988fa4cc8874bda05328f824abb9e1b3 +size 172 diff --git a/wespeaker-chunk-emb-s16-w56.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w56.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..8248602bb60c43652cfd93d86ab08dfbc5fc4c8f --- /dev/null +++ b/wespeaker-chunk-emb-s16-w56.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 56, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 56, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26553216)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w56.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w56.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..ec9bb8167b6da272dafb17ec41da9c3457f81edc --- /dev/null +++ b/wespeaker-chunk-emb-s16-w56.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:846fb6c1a7b5a8768d31656c6bd3625dbbd9fb9bfd801fb26fb75fad087525b9 +size 28273600 diff --git a/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..77417cf2695ba43eb41603fb2406470df9a5b9c6 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eab6580a00b27ef1d7fe7bc2ad62463c25b6e04aa56bc0308857bdc9c005eec7 +size 243 diff --git a/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a70a9f1ee025a4d60c914530332ac88427877872 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97c33ee50823bef11c182c5a4d13ee1e3434ec1071f6a05d60ff244cce56fa9c +size 402 diff --git a/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..5537856db25ea4f45e73592304c90e7fb6498458 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 72, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 72, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6711424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7264448))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..cba1c2aa06457768849032deaefa39441c08b3b8 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w72-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:acd7e0d220c383f5d0c151cc34b188f558da935de02c7f0aaeba92a36a52d311 +size 7265376 diff --git a/wespeaker-chunk-emb-s16-w72.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s16-w72.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..9743acaa744bbcc1f14f7c6d0ab2cc3ced8f2135 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w72.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1388064c87a1b8ace67ad7c3dfb92d1d597241b1e47ce24898cb5aae079690fd +size 243 diff --git a/wespeaker-chunk-emb-s16-w72.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s16-w72.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0e19620a4c41e2f5fa81ba3863f3335eb49ca583 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w72.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ac5f2d51de02127b57c83448c1f2457dcdc69b2e71d9a0340a9c163e315e0187 +size 172 diff --git a/wespeaker-chunk-emb-s16-w72.mlmodelc/model.mil b/wespeaker-chunk-emb-s16-w72.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..658048cb7b9c505efbf4a7747e65d10e2173c8a4 --- /dev/null +++ b/wespeaker-chunk-emb-s16-w72.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 72, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 72, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26561216)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s16-w72.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s16-w72.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..63d64b1c68dbc168dfd374d682abb53f9259c7fa --- /dev/null +++ b/wespeaker-chunk-emb-s16-w72.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4d9a31dc34a35557a692acfb3cd335befd4581a093b44a24ebe0ec96b025c3c +size 28773120 diff --git a/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..71f6e8a05d78d90e7d63fd293d2895c692b58f8e --- /dev/null +++ b/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:57062c136f127e17b4feb80ffdad47b497a2a3330460160a9536581db5078442 +size 243 diff --git a/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..c592bc8789a872d2c0bd9299583ee4d3e98e53e9 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:001e7c7b388c3384265b4517c15dd25b7758680b481ff6130216c4c36dc5ee38 +size 401 diff --git a/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..5225bbad8267d31e76530c31cfdbc30187c62cdb --- /dev/null +++ b/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 11, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 11, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6680896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6765440))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..c42d9eee67aaff5766d9abecf389cc78b41c4b7b --- /dev/null +++ b/wespeaker-chunk-emb-s25-w11-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e61f27e1d6dad43e0d7bc101fb7f3422595d14614a307890be0ff94f61d15f9 +size 6765636 diff --git a/wespeaker-chunk-emb-s25-w11.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w11.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..bc7128c53d64b9e4e49f5c19d64336315633ef95 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w11.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f5087aa7393e4fa08d20577ed4b051f53d415f7133ebe3716d0752cdf33b9462 +size 243 diff --git a/wespeaker-chunk-emb-s25-w11.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w11.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..dfca64a2e0aa93e804a0db1654b79dc3dfb4c2dd --- /dev/null +++ b/wespeaker-chunk-emb-s25-w11.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:071a1200c0011d1f1cad36fc8c0bbe8d630c06fe6301cae2e010eea3ad9040b4 +size 170 diff --git a/wespeaker-chunk-emb-s25-w11.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w11.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..165bb0adb1bc84022c20888718d21cb4c2d579e3 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w11.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 11, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 11, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26530688)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w11.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w11.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..6cef103b289bc35640f5602780e0f519be4258c2 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w11.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a83bb0f97351b6d517d3aabdc5685a780f3e3b49eed73d2df56c5d955e675e05 +size 26868672 diff --git a/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..27f5c0a8a9138294b1e432f3e9f9c0851eed0468 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:da81ef3a0923a80d08761390f8b236254411a21bee1e27cf4fadf2fbe9aa8a05 +size 243 diff --git a/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..03a346aeeef5eb0cb96f0250e68a9956754651d4 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8ba99b17e54a48edac6cabdf74b73749a9340dc70b00bc2e11c20f7f402949f5 +size 401 diff --git a/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..cac0d15d009f90cc05e8dd86bcf2a701cc301643 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 16, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 16, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6683392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6806336))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..71f3d4ce131852534a6fa5a3347cd08e4ac0ad08 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w16-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d7fd24d64f29869013943cfe43682d18abec0c0cf1aa26bf6b934e164d3c07f +size 6806592 diff --git a/wespeaker-chunk-emb-s25-w16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..7df71c222401d62757173dce8cc07effe517c3a6 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6f092d86cede555bdb5f4cdd05b73732bdaf3c00df0ae4f0bd5681a18991135b +size 243 diff --git a/wespeaker-chunk-emb-s25-w16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..83c1fd8806260f22c2cec47df10f8857743fb6a1 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfc4d5e9417e66ace1c2027881ea40d6985af5fedb2dac3ef06fb296923117bc +size 170 diff --git a/wespeaker-chunk-emb-s25-w16.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..9da881f1b8d6eed6ab6e7cd711d71b9de53706e1 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 16, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 16, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26533184)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..ed3956ad2e4e350507f88a8a933fc4ab443c8fe7 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9eed6929c3e0c0a3f3451886f41f87640f2563d8719f9eb14b55158cfc9c905 +size 27024768 diff --git a/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2e5e7dd75f72295044812379f43d3b2898e70057 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92decea8b90b1477cc533bdd9b03f12afb9b354150bd36cf7effc4ec503f70a4 +size 243 diff --git a/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..e37f78e5aff9d00d310178796bde9e00da35b008 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:403312cb2caf167cc2f1160a7fa2c87f1eec064a8ce05fd38f8cc230899f452e +size 401 diff --git a/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..8d066f9be7e41600926758528c4b31f4e3f39077 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 21, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 21, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6685952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6847296))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..fdbc4b75baae0cffe9d04ba827dd5da126d17219 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w21-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:997734665fd1d92727326ac49cc624b5b672890be5d0e90a22539f61767496c8 +size 6847612 diff --git a/wespeaker-chunk-emb-s25-w21.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w21.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..fb5cd090dc381b1d3cf09f935f60367740052b6a --- /dev/null +++ b/wespeaker-chunk-emb-s25-w21.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:485a48fca1726656b9cd0170a1b5af42abf41d858a4adb17c1c0442cf72bc49d +size 243 diff --git a/wespeaker-chunk-emb-s25-w21.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w21.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..25ed32a318311a96a04b9a260b10c3275741b9ad --- /dev/null +++ b/wespeaker-chunk-emb-s25-w21.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a35fd78a1f904a702a762f11af4126eff05cec81310d49109a2976ba89fc81d0 +size 170 diff --git a/wespeaker-chunk-emb-s25-w21.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w21.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..20874f8ef2b9879e82dbe8d773bc1387fb33e581 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w21.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 21, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 21, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26535744)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w21.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w21.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..2d6b975e2cda1dddb11129124dcd5f48a80e5fa4 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w21.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3711423e2f7f27f805ba35e6fb5f3f7c4630750a5bdff4b7700e27c0726255b7 +size 27180928 diff --git a/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..d2010bba52a586353df7ed3041fdbc1be66f9164 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:db4b75b5a77ee6e02ce3f9ec5b5868d266ea01775325c43dd734c162ec696150 +size 243 diff --git a/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0b16c04546a48d11a8ed7c0634c91ec1a4788a0b --- /dev/null +++ b/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b96ead69a7e06f4f0b1cc710118d83fad3c4e1fc7b4da05504823e404a4676ac +size 401 diff --git a/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..2229b416aee11a479156464c3b4b166b40f0d9fb --- /dev/null +++ b/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 26, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 26, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6688448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6888192))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..8ad1f030ede2c97e5e066e2942a97b341b4645f7 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w26-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e74bbd87f86ce138ea25a6961bd010ffd416c25fdfbe2c4c8cd38a3ae453a82c +size 6888568 diff --git a/wespeaker-chunk-emb-s25-w26.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w26.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..7e1d0f5efb595cc3d41976b16912689c9a7526d5 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w26.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:34f76d13ec834023770c3d406a07aa632145edd55e06a61e68f23fd117094e2d +size 243 diff --git a/wespeaker-chunk-emb-s25-w26.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w26.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..c5ff451b2d63642c98074e96dfa02abdd9bee307 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w26.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd9767d24d64b3d06afe4d6ce1e9d6f981bf03418bb03573af92f5d23aef8acb +size 170 diff --git a/wespeaker-chunk-emb-s25-w26.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w26.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..7f97c4fc8f1b14a818b4174f77b572de9607bdb7 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w26.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 26, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 26, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26538240)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w26.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w26.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..421999df9751825c9f72e6ec5eff224827273dcd --- /dev/null +++ b/wespeaker-chunk-emb-s25-w26.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:59ac11c34310a15222978b380fd925a876a2d154dbbd75ab5a350427e8670c2b +size 27337024 diff --git a/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a19522ecbac9f2ed635350df5a452bf13390a77d --- /dev/null +++ b/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0519c1ca709349173e24011889b9ac4488f1ff568f17b5de4922db2066f1578d +size 243 diff --git a/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..024d29374496870560a87d5c56f8ea5dec23a9b6 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e69d783ef624d4a323040060b6ed07039fd9493afa7fe835500168987b0d0c91 +size 401 diff --git a/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a6295c187bdd749a6a8e74b7ccd9188d35163494 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 36, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 36, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6693440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6969984))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..b95b5efd06333c471642e19f5a942cfced5ff467 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w36-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:159a1d58af64a4aefb3d6ff98b3644f06d386470740071c8b431d46cbf3c3166 +size 6970480 diff --git a/wespeaker-chunk-emb-s25-w36.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w36.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..1affdd34dd17e160581d8b5ab9da46e1438a0530 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w36.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:02e23c120c4b5c6d8e45c2f4f8687883676e77b87727767fcc335ebdcda673db +size 243 diff --git a/wespeaker-chunk-emb-s25-w36.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w36.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..664b3ce5184fc47e0f32d66e7e13a8598ced4829 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w36.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cae4f04dc7074f9f49e7e7f2ff118fcafdbc813cfcf7bf3f5394ac841f2cf4d3 +size 170 diff --git a/wespeaker-chunk-emb-s25-w36.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w36.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..d238c199ccb714763085bcbdcaf42c403eb067fc --- /dev/null +++ b/wespeaker-chunk-emb-s25-w36.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 36, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 36, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26543232)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w36.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w36.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..70dd1249ffaae0767320b8d74e1ac58495e3aaf7 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w36.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4605246b2cc8c17e3a0401ec87a2e0bdc339ce02c730ee93c44b78b4dc43d48e +size 27649216 diff --git a/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ec9adbd8941aba79a66bdac5dee64bc31225e17e --- /dev/null +++ b/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:975b71de2d73308ddf5387e3ad60a395816b7e741a3544fa8bd57fa7f01e2e8e +size 243 diff --git a/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..958e951d558bfb8e2a2bea38f89f91068e433cd1 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2b789fb8f9b259f353c196ec11ea55a4627abd120a4d9bc7e456d7dda0b2bb95 +size 403 diff --git a/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..acf7aacb9b3813c2ed7556e24be48f0c35f67575 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 46, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 46, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6698432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7051776))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..4a2c0a9f274d94be9c3e1bbea2adb35beee1a22b --- /dev/null +++ b/wespeaker-chunk-emb-s25-w46-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4dc0527b138fb770fd14e363007c5f8765ccba1ac151247393a9ead48702d313 +size 7052392 diff --git a/wespeaker-chunk-emb-s25-w46.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w46.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..dec07dca3146044a7e7f25cc18c05f46a4c59c08 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w46.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:643528b1d3bed6e9fd6de7637f5d3c592bf14e003dcc7f29b5f02f9c780d9561 +size 243 diff --git a/wespeaker-chunk-emb-s25-w46.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w46.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ba42402edaf07007b3e1080ef141b0f937716080 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w46.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b712ea62e81ff40ece1bc2679f9e3d05da62a2314bb9c6aa60bcab0c8fe377ec +size 172 diff --git a/wespeaker-chunk-emb-s25-w46.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w46.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..05bb07c86284b86d66392dca1062168b25fa6ecb --- /dev/null +++ b/wespeaker-chunk-emb-s25-w46.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 46, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 46, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26548224)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w46.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w46.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a38fb6424753e728f821755c60f7bf0c65a529ad --- /dev/null +++ b/wespeaker-chunk-emb-s25-w46.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4595af6214b9614523a25bc08cab3764e0f54a92ff7805ba1f6b5d84760b8391 +size 27961408 diff --git a/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..71dfc693874508c4618f93199406689054c491fd --- /dev/null +++ b/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:829e182a4a9675b83a8c1f33d3f568676f01eb07d79d92649f68a81672034981 +size 243 diff --git a/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b5740e0ddc91465d0ed419280947e891b44ab749 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aef3005963a044c083c00b918e3020aaf95fe05427332e1db5b6260f25ee3da3 +size 403 diff --git a/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..2e8d69a6ddaa2c1cdf4295801ddb078a0ba0c65f --- /dev/null +++ b/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 56, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 56, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6703424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7133568))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..562571b355cba167478cb13d5f1233b039fe6be8 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w56-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a6581225cea1cc2d5e25ce1a51b5a9d4dab8fbca24b8a1aecf0ce425248285e +size 7134304 diff --git a/wespeaker-chunk-emb-s25-w56.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-s25-w56.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2ca4e128650611956cbf1ef997d0c6e3cb49f307 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w56.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23152438a35711372caba1b9ac05b7de2d660dcddffc75ece5ffa72d23ac536b +size 243 diff --git a/wespeaker-chunk-emb-s25-w56.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-s25-w56.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..53cb576e3122b9508bc90aabd6a32a6950ccc6f0 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w56.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5fcb2c095d540e842cb200c9bf058e656f95379a51635e5afe79cb49f7e9fadd +size 172 diff --git a/wespeaker-chunk-emb-s25-w56.mlmodelc/model.mil b/wespeaker-chunk-emb-s25-w56.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a1906b7f5dcff5781bf4173ad0cc9d65374bf788 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w56.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 56, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 56, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26553216)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-s25-w56.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-s25-w56.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..47d7affae969e08ce352f1afdb62508c610911e7 --- /dev/null +++ b/wespeaker-chunk-emb-s25-w56.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab81aa2d7f432b0f319c5367edf2890b9d7ae37a5e49c491d0a8b9aefbea5d42 +size 28273600 diff --git a/wespeaker-chunk-emb-w11-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-w11-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..607ebf2e0fa187ad14490e06b920b7dc77c9629f --- /dev/null +++ b/wespeaker-chunk-emb-w11-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:25a68ef810b7b5bdc71ffa9ff4074bef1c9a152a76616296627522d870aca7bd +size 243 diff --git a/wespeaker-chunk-emb-w11-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-w11-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..60cca52001c4f5cf5658e0bbff68e8a7bb7ccb45 --- /dev/null +++ b/wespeaker-chunk-emb-w11-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e55ff6fafda596133bb1a2cc127ccd9f0bdd561b8a222b7899ecfd6698d0c4c +size 400 diff --git a/wespeaker-chunk-emb-w11-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-w11-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..5225bbad8267d31e76530c31cfdbc30187c62cdb --- /dev/null +++ b/wespeaker-chunk-emb-w11-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 11, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 11, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6680896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6765440))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-w11-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-w11-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..c42d9eee67aaff5766d9abecf389cc78b41c4b7b --- /dev/null +++ b/wespeaker-chunk-emb-w11-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e61f27e1d6dad43e0d7bc101fb7f3422595d14614a307890be0ff94f61d15f9 +size 6765636 diff --git a/wespeaker-chunk-emb-w11.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-w11.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..fb6fc77a4b9bf2682ca85672b1307ccca756e787 --- /dev/null +++ b/wespeaker-chunk-emb-w11.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4bd93e546e4b3e520a71ed57de8e607a5dcc87eb46c99d3cd2b8a432c5290e73 +size 243 diff --git a/wespeaker-chunk-emb-w11.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-w11.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..dfca64a2e0aa93e804a0db1654b79dc3dfb4c2dd --- /dev/null +++ b/wespeaker-chunk-emb-w11.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:071a1200c0011d1f1cad36fc8c0bbe8d630c06fe6301cae2e010eea3ad9040b4 +size 170 diff --git a/wespeaker-chunk-emb-w11.mlmodelc/model.mil b/wespeaker-chunk-emb-w11.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..165bb0adb1bc84022c20888718d21cb4c2d579e3 --- /dev/null +++ b/wespeaker-chunk-emb-w11.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 11, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 11, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26530688)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-w11.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-w11.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..6cef103b289bc35640f5602780e0f519be4258c2 --- /dev/null +++ b/wespeaker-chunk-emb-w11.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a83bb0f97351b6d517d3aabdc5685a780f3e3b49eed73d2df56c5d955e675e05 +size 26868672 diff --git a/wespeaker-chunk-emb-w26-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-w26-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..9203379645e82abb0604b35019d108c11746af35 --- /dev/null +++ b/wespeaker-chunk-emb-w26-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1bc6c3bbdec3919593830e92f6c59038d0e1db25e4d21a58933d974057158689 +size 243 diff --git a/wespeaker-chunk-emb-w26-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-w26-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..5b92fe3a908eb762aa87e6db3d4394eacccf130d --- /dev/null +++ b/wespeaker-chunk-emb-w26-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:554db95cce21fe3a06128015675b48657e4352fb6bdda26929021b336608d4cd +size 400 diff --git a/wespeaker-chunk-emb-w26-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-w26-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..2229b416aee11a479156464c3b4b166b40f0d9fb --- /dev/null +++ b/wespeaker-chunk-emb-w26-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 26, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 26, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6688448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6888192))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-w26-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-w26-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..8ad1f030ede2c97e5e066e2942a97b341b4645f7 --- /dev/null +++ b/wespeaker-chunk-emb-w26-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e74bbd87f86ce138ea25a6961bd010ffd416c25fdfbe2c4c8cd38a3ae453a82c +size 6888568 diff --git a/wespeaker-chunk-emb-w26.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-w26.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..490024e3f27b10b5a7fff7e1accef24d3726e7b7 --- /dev/null +++ b/wespeaker-chunk-emb-w26.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1b618bb0b757e5af555af78d213ba85fdf7222227d78aaa4a7303dd113e6355f +size 243 diff --git a/wespeaker-chunk-emb-w26.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-w26.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..c5ff451b2d63642c98074e96dfa02abdd9bee307 --- /dev/null +++ b/wespeaker-chunk-emb-w26.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd9767d24d64b3d06afe4d6ce1e9d6f981bf03418bb03573af92f5d23aef8acb +size 170 diff --git a/wespeaker-chunk-emb-w26.mlmodelc/model.mil b/wespeaker-chunk-emb-w26.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..7f97c4fc8f1b14a818b4174f77b572de9607bdb7 --- /dev/null +++ b/wespeaker-chunk-emb-w26.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 26, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 26, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26538240)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-w26.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-w26.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..421999df9751825c9f72e6ec5eff224827273dcd --- /dev/null +++ b/wespeaker-chunk-emb-w26.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:59ac11c34310a15222978b380fd925a876a2d154dbbd75ab5a350427e8670c2b +size 27337024 diff --git a/wespeaker-chunk-emb-w56-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-w56-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..cdc6762e32b60374fcd19e390b26b5cbb1c757f1 --- /dev/null +++ b/wespeaker-chunk-emb-w56-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8f2f1dcb47e2ef3385dba5c7132eb7b548a2e763200fcee17cbcb8f51e48d892 +size 243 diff --git a/wespeaker-chunk-emb-w56-w8a16.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-w56-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..1e266000e50fe58ca99494dd8ad2bfb1217d9acd --- /dev/null +++ b/wespeaker-chunk-emb-w56-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e3a1296eecea06c30594bf8bf4007c1345621ec1d216b370f16309500077cb4 +size 402 diff --git a/wespeaker-chunk-emb-w56-w8a16.mlmodelc/model.mil b/wespeaker-chunk-emb-w56-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..2e8d69a6ddaa2c1cdf4295801ddb078a0ba0c65f --- /dev/null +++ b/wespeaker-chunk-emb-w56-w8a16.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 56, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 56, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6703424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7133568))))[name = string("zeros_like_quantized")]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-w56-w8a16.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-w56-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..562571b355cba167478cb13d5f1233b039fe6be8 --- /dev/null +++ b/wespeaker-chunk-emb-w56-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a6581225cea1cc2d5e25ce1a51b5a9d4dab8fbca24b8a1aecf0ce425248285e +size 7134304 diff --git a/wespeaker-chunk-emb-w56.mlmodelc/analytics/coremldata.bin b/wespeaker-chunk-emb-w56.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..719e9774ca5a48fd8303cfb9e872a2a3103471c5 --- /dev/null +++ b/wespeaker-chunk-emb-w56.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a2cb5bc6be6db9000d3c0b2703795567e979d3e785b59d2fa58d33c6901e519 +size 243 diff --git a/wespeaker-chunk-emb-w56.mlmodelc/coremldata.bin b/wespeaker-chunk-emb-w56.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..53cb576e3122b9508bc90aabd6a32a6950ccc6f0 --- /dev/null +++ b/wespeaker-chunk-emb-w56.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5fcb2c095d540e842cb200c9bf058e656f95379a51635e5afe79cb49f7e9fadd +size 172 diff --git a/wespeaker-chunk-emb-w56.mlmodelc/model.mil b/wespeaker-chunk-emb-w56.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a1906b7f5dcff5781bf4173ad0cc9d65374bf788 --- /dev/null +++ b/wespeaker-chunk-emb-w56.mlmodelc/model.mil @@ -0,0 +1,427 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) { + tensor p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))]; + tensor const_0 = const()[name = string("const_0"), val = tensor([0, 2, 1])]; + tensor unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor([1])]; + tensor permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")]; + tensor unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")]; + string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")]; + tensor conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor([1, 1])]; + tensor conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)]; + tensor const_201 = const()[name = string("const_201"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))]; + tensor const_202 = const()[name = string("const_202"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))]; + tensor _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")]; + tensor relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")]; + string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")]; + tensor conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor([1, 1])]; + tensor conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)]; + tensor const_203 = const()[name = string("const_203"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))]; + tensor const_204 = const()[name = string("const_204"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))]; + tensor _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")]; + tensor relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")]; + string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")]; + tensor conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor([1, 1])]; + tensor conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)]; + tensor const_205 = const()[name = string("const_205"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))]; + tensor const_206 = const()[name = string("const_206"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))]; + tensor _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")]; + tensor add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")]; + tensor relu_2 = relu(x = add)[name = string("relu_2")]; + string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")]; + tensor conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor([1, 1])]; + tensor conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)]; + tensor const_207 = const()[name = string("const_207"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))]; + tensor const_208 = const()[name = string("const_208"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))]; + tensor _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")]; + tensor relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")]; + string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")]; + tensor conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor([1, 1])]; + tensor conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)]; + tensor const_209 = const()[name = string("const_209"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))]; + tensor const_210 = const()[name = string("const_210"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))]; + tensor _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")]; + tensor add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")]; + tensor relu_4 = relu(x = add_1)[name = string("relu_4")]; + string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")]; + tensor conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor([1, 1])]; + tensor conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)]; + tensor const_211 = const()[name = string("const_211"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))]; + tensor const_212 = const()[name = string("const_212"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))]; + tensor _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")]; + tensor relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")]; + string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")]; + tensor conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor([1, 1])]; + tensor conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)]; + tensor const_213 = const()[name = string("const_213"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))]; + tensor const_214 = const()[name = string("const_214"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))]; + tensor _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")]; + tensor add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")]; + tensor relu_6 = relu(x = add_2)[name = string("relu_6")]; + string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")]; + tensor conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor([2, 2])]; + tensor conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)]; + tensor const_215 = const()[name = string("const_215"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))]; + tensor const_216 = const()[name = string("const_216"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))]; + tensor _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")]; + tensor relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")]; + string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")]; + tensor conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor([1, 1])]; + tensor conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)]; + tensor const_217 = const()[name = string("const_217"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))]; + tensor const_218 = const()[name = string("const_218"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))]; + tensor _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")]; + string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")]; + tensor conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor([2, 2])]; + tensor conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)]; + tensor const_219 = const()[name = string("const_219"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))]; + tensor const_220 = const()[name = string("const_220"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))]; + tensor _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")]; + tensor add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")]; + tensor relu_8 = relu(x = add_3)[name = string("relu_8")]; + string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")]; + tensor conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor([1, 1])]; + tensor conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)]; + tensor const_221 = const()[name = string("const_221"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))]; + tensor const_222 = const()[name = string("const_222"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))]; + tensor _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")]; + tensor relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")]; + string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")]; + tensor conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor([1, 1])]; + tensor conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)]; + tensor const_223 = const()[name = string("const_223"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))]; + tensor const_224 = const()[name = string("const_224"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))]; + tensor _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")]; + tensor add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")]; + tensor relu_10 = relu(x = add_4)[name = string("relu_10")]; + string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")]; + tensor conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor([1, 1])]; + tensor conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)]; + tensor const_225 = const()[name = string("const_225"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))]; + tensor const_226 = const()[name = string("const_226"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))]; + tensor _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")]; + tensor relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")]; + string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")]; + tensor conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor([1, 1])]; + tensor conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)]; + tensor const_227 = const()[name = string("const_227"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))]; + tensor const_228 = const()[name = string("const_228"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))]; + tensor _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")]; + tensor add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")]; + tensor relu_12 = relu(x = add_5)[name = string("relu_12")]; + string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")]; + tensor conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor([1, 1])]; + tensor conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)]; + tensor const_229 = const()[name = string("const_229"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))]; + tensor const_230 = const()[name = string("const_230"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))]; + tensor _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")]; + tensor relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")]; + string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")]; + tensor conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor([1, 1])]; + tensor conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)]; + tensor const_231 = const()[name = string("const_231"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))]; + tensor const_232 = const()[name = string("const_232"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))]; + tensor _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")]; + tensor add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")]; + tensor relu_14 = relu(x = add_6)[name = string("relu_14")]; + string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")]; + tensor conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor([2, 2])]; + tensor conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)]; + tensor const_233 = const()[name = string("const_233"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))]; + tensor const_234 = const()[name = string("const_234"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))]; + tensor _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")]; + tensor relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")]; + string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")]; + tensor conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor([1, 1])]; + tensor conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)]; + tensor const_235 = const()[name = string("const_235"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))]; + tensor const_236 = const()[name = string("const_236"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))]; + tensor _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")]; + string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")]; + tensor conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor([2, 2])]; + tensor conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)]; + tensor const_237 = const()[name = string("const_237"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))]; + tensor const_238 = const()[name = string("const_238"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))]; + tensor _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")]; + tensor add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")]; + tensor relu_16 = relu(x = add_7)[name = string("relu_16")]; + string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")]; + tensor conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor([1, 1])]; + tensor conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)]; + tensor const_239 = const()[name = string("const_239"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))]; + tensor const_240 = const()[name = string("const_240"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))]; + tensor _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")]; + tensor relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")]; + string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")]; + tensor conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor([1, 1])]; + tensor conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)]; + tensor const_241 = const()[name = string("const_241"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))]; + tensor const_242 = const()[name = string("const_242"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))]; + tensor _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")]; + tensor add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")]; + tensor relu_18 = relu(x = add_8)[name = string("relu_18")]; + string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")]; + tensor conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor([1, 1])]; + tensor conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)]; + tensor const_243 = const()[name = string("const_243"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))]; + tensor const_244 = const()[name = string("const_244"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))]; + tensor _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")]; + tensor relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")]; + string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")]; + tensor conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor([1, 1])]; + tensor conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)]; + tensor const_245 = const()[name = string("const_245"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))]; + tensor const_246 = const()[name = string("const_246"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))]; + tensor _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")]; + tensor add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")]; + tensor relu_20 = relu(x = add_9)[name = string("relu_20")]; + string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")]; + tensor conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor([1, 1])]; + tensor conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)]; + tensor const_247 = const()[name = string("const_247"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))]; + tensor const_248 = const()[name = string("const_248"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))]; + tensor _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")]; + tensor relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")]; + string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")]; + tensor conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor([1, 1])]; + tensor conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)]; + tensor const_249 = const()[name = string("const_249"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))]; + tensor const_250 = const()[name = string("const_250"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))]; + tensor _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")]; + tensor add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")]; + tensor relu_22 = relu(x = add_10)[name = string("relu_22")]; + string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")]; + tensor conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor([1, 1])]; + tensor conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)]; + tensor const_251 = const()[name = string("const_251"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))]; + tensor const_252 = const()[name = string("const_252"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))]; + tensor _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")]; + tensor relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")]; + string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")]; + tensor conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor([1, 1])]; + tensor conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)]; + tensor const_253 = const()[name = string("const_253"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))]; + tensor const_254 = const()[name = string("const_254"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))]; + tensor _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")]; + tensor add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")]; + tensor relu_24 = relu(x = add_11)[name = string("relu_24")]; + string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")]; + tensor conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor([1, 1])]; + tensor conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)]; + tensor const_255 = const()[name = string("const_255"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))]; + tensor const_256 = const()[name = string("const_256"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))]; + tensor _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")]; + tensor relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")]; + string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")]; + tensor conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor([1, 1])]; + tensor conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)]; + tensor const_257 = const()[name = string("const_257"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))]; + tensor const_258 = const()[name = string("const_258"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))]; + tensor _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")]; + tensor add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")]; + tensor relu_26 = relu(x = add_12)[name = string("relu_26")]; + string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")]; + tensor conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor([2, 2])]; + tensor conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)]; + tensor const_259 = const()[name = string("const_259"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))]; + tensor const_260 = const()[name = string("const_260"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))]; + tensor _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")]; + tensor relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")]; + string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")]; + tensor conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor([1, 1])]; + tensor conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)]; + tensor const_261 = const()[name = string("const_261"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))]; + tensor const_262 = const()[name = string("const_262"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))]; + tensor _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")]; + string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")]; + tensor conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor([2, 2])]; + tensor conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)]; + tensor const_263 = const()[name = string("const_263"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))]; + tensor const_264 = const()[name = string("const_264"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))]; + tensor _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")]; + tensor add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")]; + tensor relu_28 = relu(x = add_13)[name = string("relu_28")]; + string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")]; + tensor conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor([1, 1])]; + tensor conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)]; + tensor const_265 = const()[name = string("const_265"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))]; + tensor const_266 = const()[name = string("const_266"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))]; + tensor _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")]; + tensor relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")]; + string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")]; + tensor conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor([1, 1])]; + tensor conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)]; + tensor const_267 = const()[name = string("const_267"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))]; + tensor const_268 = const()[name = string("const_268"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))]; + tensor _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")]; + tensor add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")]; + tensor relu_30 = relu(x = add_14)[name = string("relu_30")]; + string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")]; + tensor conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor([1, 1])]; + tensor conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)]; + tensor const_269 = const()[name = string("const_269"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))]; + tensor const_270 = const()[name = string("const_270"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))]; + tensor _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")]; + tensor relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")]; + string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")]; + tensor conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor([1, 1])]; + tensor conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor([1, 1])]; + int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)]; + tensor const_271 = const()[name = string("const_271"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))]; + tensor const_272 = const()[name = string("const_272"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))]; + tensor _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")]; + tensor add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")]; + tensor relu_32 = relu(x = add_15)[name = string("relu_32")]; + tensor const_179 = const()[name = string("const_179"), val = tensor([1, 2560, -1])]; + tensor view = reshape(shape = const_179, x = relu_32)[name = string("view")]; + tensor squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor([0])]; + tensor squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")]; + int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)]; + bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)]; + tensor select_0 = const()[name = string("select_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))]; + int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)]; + tensor index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")]; + tensor const_182 = const()[name = string("const_182"), val = tensor([2560, 56, 125])]; + tensor view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")]; + tensor const_183 = const()[name = string("const_183"), val = tensor([1, 0, 2])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([3, 56, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")]; + tensor repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")]; + tensor unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor([1])]; + tensor unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor([3])]; + tensor upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")]; + tensor sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor([2])]; + bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)]; + tensor sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")]; + fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)]; + tensor gt = greater(x = sum_1, y = const_189)[name = string("gt")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")]; + tensor mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")]; + tensor sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor([2])]; + bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)]; + tensor sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")]; + tensor div = real_div(x = sum_2, y = where)[name = string("div")]; + tensor unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor([2])]; + tensor unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")]; + tensor sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")]; + tensor square = mul(x = sub, y = sub)[name = string("square")]; + tensor square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")]; + tensor sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor([2])]; + bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)]; + tensor sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")]; + tensor div_1 = real_div(x = sum_3, y = where)[name = string("div_1")]; + tensor sub_1 = sub(x = where, y = div_1)[name = string("sub_1")]; + fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)]; + tensor add_16 = add(x = sub_1, y = const_193)[name = string("add_16")]; + tensor mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")]; + tensor sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor([2])]; + bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)]; + tensor sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")]; + tensor div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")]; + fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)]; + tensor clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")]; + tensor sqrt = sqrt(x = clamp_min)[name = string("sqrt")]; + int32 const_196 = const()[name = string("const_196"), val = int32(-1)]; + bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)]; + tensor cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")]; + tensor zeros_like = const()[name = string("zeros_like"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26553216)))]; + fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")]; + int32 const_198 = const()[name = string("const_198"), val = int32(-1)]; + bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)]; + tensor cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")]; + fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)]; + tensor le = less_equal(x = sum_1, y = const_199)[name = string("le")]; + tensor const_200 = const()[name = string("const_200"), val = tensor([1, 5120])]; + tensor repeat = tile(reps = const_200, x = le)[name = string("repeat")]; + tensor where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")]; + tensor output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-chunk-emb-w56.mlmodelc/weights/weight.bin b/wespeaker-chunk-emb-w56.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..47d7affae969e08ce352f1afdb62508c610911e7 --- /dev/null +++ b/wespeaker-chunk-emb-w56.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab81aa2d7f432b0f319c5367edf2890b9d7ae37a5e49c491d0a8b9aefbea5d42 +size 28273600 diff --git a/wespeaker-fbank-30s.mlmodelc/analytics/coremldata.bin b/wespeaker-fbank-30s.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0eb1b41726bf4ad61862f4bb329e604cad19c1f1 --- /dev/null +++ b/wespeaker-fbank-30s.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:413a9d484295f712f6a496624abd9a663e72149d2fe43f8ad101dc1dc09981d0 +size 243 diff --git a/wespeaker-fbank-30s.mlmodelc/coremldata.bin b/wespeaker-fbank-30s.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..25d119ba72886dfa5b80673887b7e0e31c590bf0 --- /dev/null +++ b/wespeaker-fbank-30s.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d52fb49c6521e14c9366e0194e1e91f4550d8ba39d31e03e72bfe014b1e826fe +size 153 diff --git a/wespeaker-fbank-30s.mlmodelc/model.mil b/wespeaker-fbank-30s.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..849b2d479bfb4ac3ace9e1d3d4d83f9d079757b7 --- /dev/null +++ b/wespeaker-fbank-30s.mlmodelc/model.mil @@ -0,0 +1,59 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor waveform) { + tensor dft_sin = const()[name = string("dft_sin"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor dft_cos = const()[name = string("dft_cos"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526464)))]; + tensor identity_kernel = const()[name = string("identity_kernel"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1052864)))]; + fp32 var_23 = const()[name = string("op_23"), val = fp32(0x1p+15)]; + tensor signal = mul(x = waveform, y = var_23)[name = string("signal")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([160])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor frames_1 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = identity_kernel, x = signal)[name = string("frames_1")]; + tensor var_44 = const()[name = string("op_44"), val = tensor([0, 2, 1])]; + tensor var_50_axes_0 = const()[name = string("op_50_axes_0"), val = tensor([2])]; + bool var_50_keep_dims_0 = const()[name = string("op_50_keep_dims_0"), val = bool(true)]; + tensor frames_3 = transpose(perm = var_44, x = frames_1)[name = string("transpose_3")]; + tensor var_50 = reduce_mean(axes = var_50_axes_0, keep_dims = var_50_keep_dims_0, x = frames_3)[name = string("op_50")]; + tensor input_1 = sub(x = frames_3, y = var_50)[name = string("input_1")]; + fp32 const_0 = const()[name = string("const_0"), val = fp32(0x0p+0)]; + tensor var_58_pad_0 = const()[name = string("op_58_pad_0"), val = tensor([0, 0, 0, 0, 1, 0])]; + string var_58_mode_0 = const()[name = string("op_58_mode_0"), val = string("replicate")]; + tensor var_58 = pad(constant_val = const_0, mode = var_58_mode_0, pad = var_58_pad_0, x = input_1)[name = string("op_58")]; + tensor previous_begin_0 = const()[name = string("previous_begin_0"), val = tensor([0, 0, 0])]; + tensor previous_end_0 = const()[name = string("previous_end_0"), val = tensor([1, 2998, 400])]; + tensor previous_end_mask_0 = const()[name = string("previous_end_mask_0"), val = tensor([true, true, false])]; + tensor previous = slice_by_index(begin = previous_begin_0, end = previous_end_0, end_mask = previous_end_mask_0, x = var_58)[name = string("previous")]; + fp32 var_64 = const()[name = string("op_64"), val = fp32(0x1.f0a3d8p-1)]; + tensor var_65 = mul(x = previous, y = var_64)[name = string("op_65")]; + tensor frames_5 = sub(x = input_1, y = var_65)[name = string("frames_5")]; + tensor var_72 = const()[name = string("op_72"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1692928)))]; + tensor input = mul(x = frames_5, y = var_72)[name = string("input")]; + fp32 const_1 = const()[name = string("const_1"), val = fp32(0x0p+0)]; + tensor frames_pad_0 = const()[name = string("frames_pad_0"), val = tensor([0, 0, 0, 0, 0, 112])]; + string frames_mode_0 = const()[name = string("frames_mode_0"), val = string("constant")]; + tensor frames = pad(constant_val = const_1, mode = frames_mode_0, pad = frames_pad_0, x = input)[name = string("frames")]; + tensor real_part_bias_0 = const()[name = string("real_part_bias_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1694592)))]; + tensor real_part = linear(bias = real_part_bias_0, weight = dft_cos, x = frames)[name = string("real_part")]; + tensor imag_part = linear(bias = real_part_bias_0, weight = dft_sin, x = frames)[name = string("imag_part")]; + fp32 var_84 = const()[name = string("op_84"), val = fp32(0x1p+1)]; + tensor var_85 = pow(x = real_part, y = var_84)[name = string("op_85")]; + fp32 var_86 = const()[name = string("op_86"), val = fp32(0x1p+1)]; + tensor var_87 = pow(x = imag_part, y = var_86)[name = string("op_87")]; + tensor spectrum = add(x = var_85, y = var_87)[name = string("spectrum")]; + tensor transpose_2 = const()[name = string("transpose_2"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1695744)))]; + tensor mel_1_bias_0 = const()[name = string("mel_1_bias_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1778048)))]; + tensor mel_1 = linear(bias = mel_1_bias_0, weight = transpose_2, x = spectrum)[name = string("mel_1")]; + fp32 const_3 = const()[name = string("const_3"), val = fp32(0x1p-23)]; + tensor var_102 = maximum(x = mel_1, y = const_3)[name = string("op_102")]; + fp32 mel_3_epsilon_0 = const()[name = string("mel_3_epsilon_0"), val = fp32(0x1p-149)]; + tensor mel_3 = log(epsilon = mel_3_epsilon_0, x = var_102)[name = string("mel_3")]; + tensor var_108_axes_0 = const()[name = string("op_108_axes_0"), val = tensor([1])]; + bool var_108_keep_dims_0 = const()[name = string("op_108_keep_dims_0"), val = bool(true)]; + tensor var_108 = reduce_mean(axes = var_108_axes_0, keep_dims = var_108_keep_dims_0, x = mel_3)[name = string("op_108")]; + tensor output = sub(x = mel_3, y = var_108)[name = string("op_110")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-fbank-30s.mlmodelc/weights/weight.bin b/wespeaker-fbank-30s.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..59177914111259262071a33c96228b807bc5eccc --- /dev/null +++ b/wespeaker-fbank-30s.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27396cb0afdd09164a9d6b2dbd10688bed15230948b3e0a6692ec490c03ae4d7 +size 1778432 diff --git a/wespeaker-fbank-b32-f16.mlmodelc/analytics/coremldata.bin b/wespeaker-fbank-b32-f16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..83fa9d9c37ab104fef7c3606048ffd32e7359377 --- /dev/null +++ b/wespeaker-fbank-b32-f16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:22fc4f14e91bab2da0a8bccb551f7d313c71b06b9b6ea8462ca9422d96efd849 +size 243 diff --git a/wespeaker-fbank-b32-f16.mlmodelc/coremldata.bin b/wespeaker-fbank-b32-f16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..9d73265c81ed850fe6ec10ba32f5f1b5331333ed --- /dev/null +++ b/wespeaker-fbank-b32-f16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4241bd2543b6face59368f98e9cb7a049c46db8cc5ebd70a12814d3382324ede +size 168 diff --git a/wespeaker-fbank-b32-f16.mlmodelc/model.mil b/wespeaker-fbank-b32-f16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..9bce5ea9f85d85b4d798abb9a72c8bfa3ee8845f --- /dev/null +++ b/wespeaker-fbank-b32-f16.mlmodelc/model.mil @@ -0,0 +1,67 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor waveform) [FlexibleShapeInformation = tuple>>, tuple>>>>((("DefaultShapes", {{"waveform", [32, 1, 160000]}}), ("EnumeratedShapes", {{"1decda00", {{"waveform", [1, 1, 160000]}}}, {"9025f589", {{"waveform", [32, 1, 160000]}}}})))] { + tensor var_17_begin_0 = const()[name = string("op_17_begin_0"), val = tensor([0, 0, 0])]; + tensor var_17_end_0 = const()[name = string("op_17_end_0"), val = tensor([0, 1, 160000])]; + tensor var_17_end_mask_0 = const()[name = string("op_17_end_mask_0"), val = tensor([true, true, true])]; + string waveform_to_fp16_dtype_0 = const()[name = string("waveform_to_fp16_dtype_0"), val = string("fp16")]; + tensor waveform_to_fp16 = cast(dtype = waveform_to_fp16_dtype_0, x = waveform)[name = string("cast_2")]; + tensor var_17_cast_fp16 = slice_by_index(begin = var_17_begin_0, end = var_17_end_0, end_mask = var_17_end_mask_0, x = waveform_to_fp16)[name = string("op_17_cast_fp16")]; + fp16 var_23_to_fp16 = const()[name = string("op_23_to_fp16"), val = fp16(0x1p+15)]; + tensor signal_cast_fp16 = mul(x = var_17_cast_fp16, y = var_23_to_fp16)[name = string("signal_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([160])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor identity_kernel_to_fp16 = const()[name = string("identity_kernel_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = identity_kernel_to_fp16, x = signal_cast_fp16)[name = string("frames_1_cast_fp16")]; + tensor var_44 = const()[name = string("op_44"), val = tensor([0, 2, 1])]; + tensor var_50_axes_0 = const()[name = string("op_50_axes_0"), val = tensor([2])]; + bool var_50_keep_dims_0 = const()[name = string("op_50_keep_dims_0"), val = bool(true)]; + tensor frames_3_cast_fp16 = transpose(perm = var_44, x = frames_1_cast_fp16)[name = string("transpose_3")]; + tensor var_50_cast_fp16 = reduce_mean(axes = var_50_axes_0, keep_dims = var_50_keep_dims_0, x = frames_3_cast_fp16)[name = string("op_50_cast_fp16")]; + tensor input_1_cast_fp16 = sub(x = frames_3_cast_fp16, y = var_50_cast_fp16)[name = string("input_1_cast_fp16")]; + tensor var_58_pad_0 = const()[name = string("op_58_pad_0"), val = tensor([0, 0, 0, 0, 1, 0])]; + string var_58_mode_0 = const()[name = string("op_58_mode_0"), val = string("replicate")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)]; + tensor var_58_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = var_58_mode_0, pad = var_58_pad_0, x = input_1_cast_fp16)[name = string("op_58_cast_fp16")]; + tensor previous_begin_0 = const()[name = string("previous_begin_0"), val = tensor([0, 0, 0])]; + tensor previous_end_0 = const()[name = string("previous_end_0"), val = tensor([0, 998, 400])]; + tensor previous_end_mask_0 = const()[name = string("previous_end_mask_0"), val = tensor([true, true, false])]; + tensor previous_cast_fp16 = slice_by_index(begin = previous_begin_0, end = previous_end_0, end_mask = previous_end_mask_0, x = var_58_cast_fp16)[name = string("previous_cast_fp16")]; + fp16 var_64_to_fp16 = const()[name = string("op_64_to_fp16"), val = fp16(0x1.f0cp-1)]; + tensor var_65_cast_fp16 = mul(x = previous_cast_fp16, y = var_64_to_fp16)[name = string("op_65_cast_fp16")]; + tensor frames_5_cast_fp16 = sub(x = input_1_cast_fp16, y = var_65_cast_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_72_to_fp16 = const()[name = string("op_72_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320128)))]; + tensor input_cast_fp16 = mul(x = frames_5_cast_fp16, y = var_72_to_fp16)[name = string("input_cast_fp16")]; + tensor frames_pad_0 = const()[name = string("frames_pad_0"), val = tensor([0, 0, 0, 0, 0, 112])]; + string frames_mode_0 = const()[name = string("frames_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor frames_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = frames_mode_0, pad = frames_pad_0, x = input_cast_fp16)[name = string("frames_cast_fp16")]; + tensor dft_cos_to_fp16 = const()[name = string("dft_cos_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321024)))]; + tensor real_part_bias_0_to_fp16 = const()[name = string("real_part_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584256)))]; + tensor real_part_cast_fp16 = linear(bias = real_part_bias_0_to_fp16, weight = dft_cos_to_fp16, x = frames_cast_fp16)[name = string("real_part_cast_fp16")]; + tensor dft_sin_to_fp16 = const()[name = string("dft_sin_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584896)))]; + tensor imag_part_cast_fp16 = linear(bias = real_part_bias_0_to_fp16, weight = dft_sin_to_fp16, x = frames_cast_fp16)[name = string("imag_part_cast_fp16")]; + fp16 var_84_to_fp16 = const()[name = string("op_84_to_fp16"), val = fp16(0x1p+1)]; + tensor var_85_cast_fp16 = pow(x = real_part_cast_fp16, y = var_84_to_fp16)[name = string("op_85_cast_fp16")]; + fp16 var_86_to_fp16 = const()[name = string("op_86_to_fp16"), val = fp16(0x1p+1)]; + tensor var_87_cast_fp16 = pow(x = imag_part_cast_fp16, y = var_86_to_fp16)[name = string("op_87_cast_fp16")]; + tensor spectrum_cast_fp16 = add(x = var_85_cast_fp16, y = var_87_cast_fp16)[name = string("spectrum_cast_fp16")]; + tensor transpose_2_to_fp16 = const()[name = string("transpose_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(848128)))]; + tensor mel_1_bias_0_to_fp16 = const()[name = string("mel_1_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889344)))]; + tensor mel_1_cast_fp16 = linear(bias = mel_1_bias_0_to_fp16, weight = transpose_2_to_fp16, x = spectrum_cast_fp16)[name = string("mel_1_cast_fp16")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x1p-23)]; + tensor var_102_cast_fp16 = maximum(x = mel_1_cast_fp16, y = const_3_to_fp16)[name = string("op_102_cast_fp16")]; + fp32 mel_3_epsilon_0 = const()[name = string("mel_3_epsilon_0"), val = fp32(0x1p-149)]; + tensor mel_3_cast_fp16 = log(epsilon = mel_3_epsilon_0, x = var_102_cast_fp16)[name = string("mel_3_cast_fp16")]; + tensor var_108_axes_0 = const()[name = string("op_108_axes_0"), val = tensor([1])]; + bool var_108_keep_dims_0 = const()[name = string("op_108_keep_dims_0"), val = bool(true)]; + tensor var_108_cast_fp16 = reduce_mean(axes = var_108_axes_0, keep_dims = var_108_keep_dims_0, x = mel_3_cast_fp16)[name = string("op_108_cast_fp16")]; + tensor var_110_cast_fp16 = sub(x = mel_3_cast_fp16, y = var_108_cast_fp16)[name = string("op_110_cast_fp16")]; + string var_110_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_110_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor output = cast(dtype = var_110_cast_fp16_to_fp32_dtype_0, x = var_110_cast_fp16)[name = string("cast_1")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-fbank-b32-f16.mlmodelc/weights/weight.bin b/wespeaker-fbank-b32-f16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..af08f4ef3f36ca2064c6b8398cab8a76712942f4 --- /dev/null +++ b/wespeaker-fbank-b32-f16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d47647a3f80687ed2b5f692a1e8a0e10cb583d9dcf283313e0b38a79ba21c60 +size 889568 diff --git a/wespeaker-fbank-f16.mlmodelc/analytics/coremldata.bin b/wespeaker-fbank-f16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..83fa9d9c37ab104fef7c3606048ffd32e7359377 --- /dev/null +++ b/wespeaker-fbank-f16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:22fc4f14e91bab2da0a8bccb551f7d313c71b06b9b6ea8462ca9422d96efd849 +size 243 diff --git a/wespeaker-fbank-f16.mlmodelc/coremldata.bin b/wespeaker-fbank-f16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..9d73265c81ed850fe6ec10ba32f5f1b5331333ed --- /dev/null +++ b/wespeaker-fbank-f16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4241bd2543b6face59368f98e9cb7a049c46db8cc5ebd70a12814d3382324ede +size 168 diff --git a/wespeaker-fbank-f16.mlmodelc/model.mil b/wespeaker-fbank-f16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..9bce5ea9f85d85b4d798abb9a72c8bfa3ee8845f --- /dev/null +++ b/wespeaker-fbank-f16.mlmodelc/model.mil @@ -0,0 +1,67 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor waveform) [FlexibleShapeInformation = tuple>>, tuple>>>>((("DefaultShapes", {{"waveform", [32, 1, 160000]}}), ("EnumeratedShapes", {{"1decda00", {{"waveform", [1, 1, 160000]}}}, {"9025f589", {{"waveform", [32, 1, 160000]}}}})))] { + tensor var_17_begin_0 = const()[name = string("op_17_begin_0"), val = tensor([0, 0, 0])]; + tensor var_17_end_0 = const()[name = string("op_17_end_0"), val = tensor([0, 1, 160000])]; + tensor var_17_end_mask_0 = const()[name = string("op_17_end_mask_0"), val = tensor([true, true, true])]; + string waveform_to_fp16_dtype_0 = const()[name = string("waveform_to_fp16_dtype_0"), val = string("fp16")]; + tensor waveform_to_fp16 = cast(dtype = waveform_to_fp16_dtype_0, x = waveform)[name = string("cast_2")]; + tensor var_17_cast_fp16 = slice_by_index(begin = var_17_begin_0, end = var_17_end_0, end_mask = var_17_end_mask_0, x = waveform_to_fp16)[name = string("op_17_cast_fp16")]; + fp16 var_23_to_fp16 = const()[name = string("op_23_to_fp16"), val = fp16(0x1p+15)]; + tensor signal_cast_fp16 = mul(x = var_17_cast_fp16, y = var_23_to_fp16)[name = string("signal_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([160])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor identity_kernel_to_fp16 = const()[name = string("identity_kernel_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = identity_kernel_to_fp16, x = signal_cast_fp16)[name = string("frames_1_cast_fp16")]; + tensor var_44 = const()[name = string("op_44"), val = tensor([0, 2, 1])]; + tensor var_50_axes_0 = const()[name = string("op_50_axes_0"), val = tensor([2])]; + bool var_50_keep_dims_0 = const()[name = string("op_50_keep_dims_0"), val = bool(true)]; + tensor frames_3_cast_fp16 = transpose(perm = var_44, x = frames_1_cast_fp16)[name = string("transpose_3")]; + tensor var_50_cast_fp16 = reduce_mean(axes = var_50_axes_0, keep_dims = var_50_keep_dims_0, x = frames_3_cast_fp16)[name = string("op_50_cast_fp16")]; + tensor input_1_cast_fp16 = sub(x = frames_3_cast_fp16, y = var_50_cast_fp16)[name = string("input_1_cast_fp16")]; + tensor var_58_pad_0 = const()[name = string("op_58_pad_0"), val = tensor([0, 0, 0, 0, 1, 0])]; + string var_58_mode_0 = const()[name = string("op_58_mode_0"), val = string("replicate")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)]; + tensor var_58_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = var_58_mode_0, pad = var_58_pad_0, x = input_1_cast_fp16)[name = string("op_58_cast_fp16")]; + tensor previous_begin_0 = const()[name = string("previous_begin_0"), val = tensor([0, 0, 0])]; + tensor previous_end_0 = const()[name = string("previous_end_0"), val = tensor([0, 998, 400])]; + tensor previous_end_mask_0 = const()[name = string("previous_end_mask_0"), val = tensor([true, true, false])]; + tensor previous_cast_fp16 = slice_by_index(begin = previous_begin_0, end = previous_end_0, end_mask = previous_end_mask_0, x = var_58_cast_fp16)[name = string("previous_cast_fp16")]; + fp16 var_64_to_fp16 = const()[name = string("op_64_to_fp16"), val = fp16(0x1.f0cp-1)]; + tensor var_65_cast_fp16 = mul(x = previous_cast_fp16, y = var_64_to_fp16)[name = string("op_65_cast_fp16")]; + tensor frames_5_cast_fp16 = sub(x = input_1_cast_fp16, y = var_65_cast_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_72_to_fp16 = const()[name = string("op_72_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320128)))]; + tensor input_cast_fp16 = mul(x = frames_5_cast_fp16, y = var_72_to_fp16)[name = string("input_cast_fp16")]; + tensor frames_pad_0 = const()[name = string("frames_pad_0"), val = tensor([0, 0, 0, 0, 0, 112])]; + string frames_mode_0 = const()[name = string("frames_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor frames_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = frames_mode_0, pad = frames_pad_0, x = input_cast_fp16)[name = string("frames_cast_fp16")]; + tensor dft_cos_to_fp16 = const()[name = string("dft_cos_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321024)))]; + tensor real_part_bias_0_to_fp16 = const()[name = string("real_part_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584256)))]; + tensor real_part_cast_fp16 = linear(bias = real_part_bias_0_to_fp16, weight = dft_cos_to_fp16, x = frames_cast_fp16)[name = string("real_part_cast_fp16")]; + tensor dft_sin_to_fp16 = const()[name = string("dft_sin_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584896)))]; + tensor imag_part_cast_fp16 = linear(bias = real_part_bias_0_to_fp16, weight = dft_sin_to_fp16, x = frames_cast_fp16)[name = string("imag_part_cast_fp16")]; + fp16 var_84_to_fp16 = const()[name = string("op_84_to_fp16"), val = fp16(0x1p+1)]; + tensor var_85_cast_fp16 = pow(x = real_part_cast_fp16, y = var_84_to_fp16)[name = string("op_85_cast_fp16")]; + fp16 var_86_to_fp16 = const()[name = string("op_86_to_fp16"), val = fp16(0x1p+1)]; + tensor var_87_cast_fp16 = pow(x = imag_part_cast_fp16, y = var_86_to_fp16)[name = string("op_87_cast_fp16")]; + tensor spectrum_cast_fp16 = add(x = var_85_cast_fp16, y = var_87_cast_fp16)[name = string("spectrum_cast_fp16")]; + tensor transpose_2_to_fp16 = const()[name = string("transpose_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(848128)))]; + tensor mel_1_bias_0_to_fp16 = const()[name = string("mel_1_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889344)))]; + tensor mel_1_cast_fp16 = linear(bias = mel_1_bias_0_to_fp16, weight = transpose_2_to_fp16, x = spectrum_cast_fp16)[name = string("mel_1_cast_fp16")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x1p-23)]; + tensor var_102_cast_fp16 = maximum(x = mel_1_cast_fp16, y = const_3_to_fp16)[name = string("op_102_cast_fp16")]; + fp32 mel_3_epsilon_0 = const()[name = string("mel_3_epsilon_0"), val = fp32(0x1p-149)]; + tensor mel_3_cast_fp16 = log(epsilon = mel_3_epsilon_0, x = var_102_cast_fp16)[name = string("mel_3_cast_fp16")]; + tensor var_108_axes_0 = const()[name = string("op_108_axes_0"), val = tensor([1])]; + bool var_108_keep_dims_0 = const()[name = string("op_108_keep_dims_0"), val = bool(true)]; + tensor var_108_cast_fp16 = reduce_mean(axes = var_108_axes_0, keep_dims = var_108_keep_dims_0, x = mel_3_cast_fp16)[name = string("op_108_cast_fp16")]; + tensor var_110_cast_fp16 = sub(x = mel_3_cast_fp16, y = var_108_cast_fp16)[name = string("op_110_cast_fp16")]; + string var_110_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_110_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor output = cast(dtype = var_110_cast_fp16_to_fp32_dtype_0, x = var_110_cast_fp16)[name = string("cast_1")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-fbank-f16.mlmodelc/weights/weight.bin b/wespeaker-fbank-f16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..af08f4ef3f36ca2064c6b8398cab8a76712942f4 --- /dev/null +++ b/wespeaker-fbank-f16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d47647a3f80687ed2b5f692a1e8a0e10cb583d9dcf283313e0b38a79ba21c60 +size 889568 diff --git a/wespeaker-multimask-tail-b32-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-multimask-tail-b32-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2f5219890a027e3eeec935795345861b0fc3b80c --- /dev/null +++ b/wespeaker-multimask-tail-b32-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:932374c6884544be9b259bf8ef8b1a0433cbac96b81e12f3ed5be944c2d1dfb4 +size 243 diff --git a/wespeaker-multimask-tail-b32-w8a16.mlmodelc/coremldata.bin b/wespeaker-multimask-tail-b32-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..13447ba74b372718cc0292257afd1fde4ef18990 --- /dev/null +++ b/wespeaker-multimask-tail-b32-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7dc4a246f1390e2bfe56f0bdbd156e662e50c6538ac084e7e6c4484e23e2384 +size 426 diff --git a/wespeaker-multimask-tail-b32-w8a16.mlmodelc/model.mil b/wespeaker-multimask-tail-b32-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..74628e065bdd712b52e0ab758135631a0f82809b --- /dev/null +++ b/wespeaker-multimask-tail-b32-w8a16.mlmodelc/model.mil @@ -0,0 +1,416 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) [FlexibleShapeInformation = tuple>>, tuple>>>>((("DefaultShapes", {{"fbank", [32, 998, 80]}, {"masks", [96, 589]}}), ("EnumeratedShapes", {{"98e0d0f3", {{"fbank", [32, 998, 80]}, {"masks", [96, 589]}}}, {"fd4e3aa9", {{"fbank", [1, 998, 80]}, {"masks", [3, 589]}}}})))] { + tensor resnet_seg_1_bias = const()[name = string("resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936))))[name = string("resnet_seg_1_weight_quantized")]; + tensor var_20 = const()[name = string("op_20"), val = tensor([0, 2, 1])]; + tensor input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor([1])]; + tensor fbank_1 = transpose(perm = var_20, x = fbank)[name = string("transpose_1")]; + tensor input_1 = expand_dims(axes = input_1_axes_0, x = fbank_1)[name = string("input_1")]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor([1, 1])]; + tensor input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor([1, 1])]; + int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; + tensor const_0 = const()[name = string("const_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_1 = const()[name = string("const_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor input_5 = conv(bias = const_1, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_0, x = input_1)[name = string("input_5")]; + tensor input_7 = relu(x = input_5)[name = string("input_7")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1, 1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor const_2_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_2_quantized")]; + tensor const_3 = const()[name = string("const_3"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor input_11 = conv(bias = const_3, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_2_quantized, x = input_7)[name = string("input_11")]; + tensor input_13 = relu(x = input_11)[name = string("input_13")]; + string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")]; + tensor input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor([1, 1])]; + int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)]; + tensor const_4_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_4_quantized")]; + tensor const_5 = const()[name = string("const_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor out_1 = conv(bias = const_5, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_4_quantized, x = input_13)[name = string("out_1")]; + tensor input_17 = add(x = out_1, y = input_7)[name = string("input_17")]; + tensor input_19 = relu(x = input_17)[name = string("input_19")]; + string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")]; + tensor input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor([1, 1])]; + tensor input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor([1, 1])]; + int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)]; + tensor const_6_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_6_quantized")]; + tensor const_7 = const()[name = string("const_7"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor input_23 = conv(bias = const_7, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_6_quantized, x = input_19)[name = string("input_23")]; + tensor input_25 = relu(x = input_23)[name = string("input_25")]; + string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")]; + tensor input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor([1, 1])]; + tensor input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor([1, 1])]; + int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)]; + tensor const_8_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_8_quantized")]; + tensor const_9 = const()[name = string("const_9"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor out_3 = conv(bias = const_9, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_8_quantized, x = input_25)[name = string("out_3")]; + tensor input_29 = add(x = out_3, y = input_19)[name = string("input_29")]; + tensor input_31 = relu(x = input_29)[name = string("input_31")]; + string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")]; + tensor input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor([1, 1])]; + tensor input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor([1, 1])]; + int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)]; + tensor const_10_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_10_quantized")]; + tensor const_11 = const()[name = string("const_11"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor input_35 = conv(bias = const_11, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_10_quantized, x = input_31)[name = string("input_35")]; + tensor input_37 = relu(x = input_35)[name = string("input_37")]; + string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("custom")]; + tensor input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor([1, 1])]; + int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)]; + tensor const_12_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_12_quantized")]; + tensor const_13 = const()[name = string("const_13"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor out_5 = conv(bias = const_13, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_12_quantized, x = input_37)[name = string("out_5")]; + tensor input_41 = add(x = out_5, y = input_31)[name = string("input_41")]; + tensor input_43 = relu(x = input_41)[name = string("input_43")]; + string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")]; + tensor input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor([2, 2])]; + tensor input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor([1, 1])]; + int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)]; + tensor const_14_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_14_quantized")]; + tensor const_15 = const()[name = string("const_15"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor input_47 = conv(bias = const_15, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_14_quantized, x = input_43)[name = string("input_47")]; + tensor input_49 = relu(x = input_47)[name = string("input_49")]; + string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")]; + tensor input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor([1, 1])]; + tensor input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor([1, 1])]; + int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)]; + tensor const_16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_16_quantized")]; + tensor const_17 = const()[name = string("const_17"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor out_7 = conv(bias = const_17, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_16_quantized, x = input_49)[name = string("out_7")]; + string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")]; + tensor input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor([2, 2])]; + tensor input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor([1, 1])]; + int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)]; + tensor const_18 = const()[name = string("const_18"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_19 = const()[name = string("const_19"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor var_194 = conv(bias = const_19, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_18, x = input_43)[name = string("op_194")]; + tensor input_55 = add(x = out_7, y = var_194)[name = string("input_55")]; + tensor input_57 = relu(x = input_55)[name = string("input_57")]; + string input_59_pad_type_0 = const()[name = string("input_59_pad_type_0"), val = string("custom")]; + tensor input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_59_strides_0 = const()[name = string("input_59_strides_0"), val = tensor([1, 1])]; + tensor input_59_dilations_0 = const()[name = string("input_59_dilations_0"), val = tensor([1, 1])]; + int32 input_59_groups_0 = const()[name = string("input_59_groups_0"), val = int32(1)]; + tensor const_20_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_20_quantized")]; + tensor const_21 = const()[name = string("const_21"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor input_61 = conv(bias = const_21, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_20_quantized, x = input_57)[name = string("input_61")]; + tensor input_63 = relu(x = input_61)[name = string("input_63")]; + string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")]; + tensor input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor([1, 1])]; + tensor input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor([1, 1])]; + int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)]; + tensor const_22_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_22_quantized")]; + tensor const_23 = const()[name = string("const_23"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor out_9 = conv(bias = const_23, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_22_quantized, x = input_63)[name = string("out_9")]; + tensor input_67 = add(x = out_9, y = input_57)[name = string("input_67")]; + tensor input_69 = relu(x = input_67)[name = string("input_69")]; + string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")]; + tensor input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor([1, 1])]; + tensor input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor([1, 1])]; + int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)]; + tensor const_24_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_24_quantized")]; + tensor const_25 = const()[name = string("const_25"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor input_73 = conv(bias = const_25, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_24_quantized, x = input_69)[name = string("input_73")]; + tensor input_75 = relu(x = input_73)[name = string("input_75")]; + string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")]; + tensor input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor([1, 1])]; + tensor input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor([1, 1])]; + int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)]; + tensor const_26_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_26_quantized")]; + tensor const_27 = const()[name = string("const_27"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor out_11 = conv(bias = const_27, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_26_quantized, x = input_75)[name = string("out_11")]; + tensor input_79 = add(x = out_11, y = input_69)[name = string("input_79")]; + tensor input_81 = relu(x = input_79)[name = string("input_81")]; + string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")]; + tensor input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor([1, 1])]; + tensor input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor([1, 1])]; + int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(1)]; + tensor const_28_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_28_quantized")]; + tensor const_29 = const()[name = string("const_29"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor input_85 = conv(bias = const_29, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_28_quantized, x = input_81)[name = string("input_85")]; + tensor input_87 = relu(x = input_85)[name = string("input_87")]; + string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("custom")]; + tensor input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor([1, 1])]; + tensor input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor([1, 1])]; + int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)]; + tensor const_30_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_30_quantized")]; + tensor const_31 = const()[name = string("const_31"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor out_13 = conv(bias = const_31, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_30_quantized, x = input_87)[name = string("out_13")]; + tensor input_91 = add(x = out_13, y = input_81)[name = string("input_91")]; + tensor input_93 = relu(x = input_91)[name = string("input_93")]; + string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("custom")]; + tensor input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor([2, 2])]; + tensor input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor([1, 1])]; + int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)]; + tensor const_32_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_32_quantized")]; + tensor const_33 = const()[name = string("const_33"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor input_97 = conv(bias = const_33, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_32_quantized, x = input_93)[name = string("input_97")]; + tensor input_99 = relu(x = input_97)[name = string("input_99")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("custom")]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1, 1])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1, 1])]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)]; + tensor const_34_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_34_quantized")]; + tensor const_35 = const()[name = string("const_35"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor out_15 = conv(bias = const_35, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_34_quantized, x = input_99)[name = string("out_15")]; + string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")]; + tensor input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor([2, 2])]; + tensor input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor([1, 1])]; + int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; + tensor const_36_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_36_quantized")]; + tensor const_37 = const()[name = string("const_37"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor var_338 = conv(bias = const_37, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_36_quantized, x = input_93)[name = string("op_338")]; + tensor input_105 = add(x = out_15, y = var_338)[name = string("input_105")]; + tensor input_107 = relu(x = input_105)[name = string("input_107")]; + string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("custom")]; + tensor input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor([1, 1])]; + tensor input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor([1, 1])]; + int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)]; + tensor const_38_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_38_quantized")]; + tensor const_39 = const()[name = string("const_39"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor input_111 = conv(bias = const_39, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_38_quantized, x = input_107)[name = string("input_111")]; + tensor input_113 = relu(x = input_111)[name = string("input_113")]; + string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("custom")]; + tensor input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_115_strides_0 = const()[name = string("input_115_strides_0"), val = tensor([1, 1])]; + tensor input_115_dilations_0 = const()[name = string("input_115_dilations_0"), val = tensor([1, 1])]; + int32 input_115_groups_0 = const()[name = string("input_115_groups_0"), val = int32(1)]; + tensor const_40_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_40_quantized")]; + tensor const_41 = const()[name = string("const_41"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor out_17 = conv(bias = const_41, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_40_quantized, x = input_113)[name = string("out_17")]; + tensor input_117 = add(x = out_17, y = input_107)[name = string("input_117")]; + tensor input_119 = relu(x = input_117)[name = string("input_119")]; + string input_121_pad_type_0 = const()[name = string("input_121_pad_type_0"), val = string("custom")]; + tensor input_121_pad_0 = const()[name = string("input_121_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_121_strides_0 = const()[name = string("input_121_strides_0"), val = tensor([1, 1])]; + tensor input_121_dilations_0 = const()[name = string("input_121_dilations_0"), val = tensor([1, 1])]; + int32 input_121_groups_0 = const()[name = string("input_121_groups_0"), val = int32(1)]; + tensor const_42_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_42_quantized")]; + tensor const_43 = const()[name = string("const_43"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor input_123 = conv(bias = const_43, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_42_quantized, x = input_119)[name = string("input_123")]; + tensor input_125 = relu(x = input_123)[name = string("input_125")]; + string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("custom")]; + tensor input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor([1, 1])]; + tensor input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor([1, 1])]; + int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)]; + tensor const_44_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_44_quantized")]; + tensor const_45 = const()[name = string("const_45"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor out_19 = conv(bias = const_45, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_44_quantized, x = input_125)[name = string("out_19")]; + tensor input_129 = add(x = out_19, y = input_119)[name = string("input_129")]; + tensor input_131 = relu(x = input_129)[name = string("input_131")]; + string input_133_pad_type_0 = const()[name = string("input_133_pad_type_0"), val = string("custom")]; + tensor input_133_pad_0 = const()[name = string("input_133_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_133_strides_0 = const()[name = string("input_133_strides_0"), val = tensor([1, 1])]; + tensor input_133_dilations_0 = const()[name = string("input_133_dilations_0"), val = tensor([1, 1])]; + int32 input_133_groups_0 = const()[name = string("input_133_groups_0"), val = int32(1)]; + tensor const_46_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_46_quantized")]; + tensor const_47 = const()[name = string("const_47"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor input_135 = conv(bias = const_47, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_46_quantized, x = input_131)[name = string("input_135")]; + tensor input_137 = relu(x = input_135)[name = string("input_137")]; + string input_139_pad_type_0 = const()[name = string("input_139_pad_type_0"), val = string("custom")]; + tensor input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_139_strides_0 = const()[name = string("input_139_strides_0"), val = tensor([1, 1])]; + tensor input_139_dilations_0 = const()[name = string("input_139_dilations_0"), val = tensor([1, 1])]; + int32 input_139_groups_0 = const()[name = string("input_139_groups_0"), val = int32(1)]; + tensor const_48_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_48_quantized")]; + tensor const_49 = const()[name = string("const_49"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor out_21 = conv(bias = const_49, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_48_quantized, x = input_137)[name = string("out_21")]; + tensor input_141 = add(x = out_21, y = input_131)[name = string("input_141")]; + tensor input_143 = relu(x = input_141)[name = string("input_143")]; + string input_145_pad_type_0 = const()[name = string("input_145_pad_type_0"), val = string("custom")]; + tensor input_145_pad_0 = const()[name = string("input_145_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_145_strides_0 = const()[name = string("input_145_strides_0"), val = tensor([1, 1])]; + tensor input_145_dilations_0 = const()[name = string("input_145_dilations_0"), val = tensor([1, 1])]; + int32 input_145_groups_0 = const()[name = string("input_145_groups_0"), val = int32(1)]; + tensor const_50_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_50_quantized")]; + tensor const_51 = const()[name = string("const_51"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor input_147 = conv(bias = const_51, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_50_quantized, x = input_143)[name = string("input_147")]; + tensor input_149 = relu(x = input_147)[name = string("input_149")]; + string input_151_pad_type_0 = const()[name = string("input_151_pad_type_0"), val = string("custom")]; + tensor input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_151_strides_0 = const()[name = string("input_151_strides_0"), val = tensor([1, 1])]; + tensor input_151_dilations_0 = const()[name = string("input_151_dilations_0"), val = tensor([1, 1])]; + int32 input_151_groups_0 = const()[name = string("input_151_groups_0"), val = int32(1)]; + tensor const_52_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_52_quantized")]; + tensor const_53 = const()[name = string("const_53"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor out_23 = conv(bias = const_53, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_52_quantized, x = input_149)[name = string("out_23")]; + tensor input_153 = add(x = out_23, y = input_143)[name = string("input_153")]; + tensor input_155 = relu(x = input_153)[name = string("input_155")]; + string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("custom")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor([1, 1])]; + tensor input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor([1, 1])]; + int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)]; + tensor const_54_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_54_quantized")]; + tensor const_55 = const()[name = string("const_55"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor input_159 = conv(bias = const_55, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_54_quantized, x = input_155)[name = string("input_159")]; + tensor input_161 = relu(x = input_159)[name = string("input_161")]; + string input_163_pad_type_0 = const()[name = string("input_163_pad_type_0"), val = string("custom")]; + tensor input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_163_strides_0 = const()[name = string("input_163_strides_0"), val = tensor([1, 1])]; + tensor input_163_dilations_0 = const()[name = string("input_163_dilations_0"), val = tensor([1, 1])]; + int32 input_163_groups_0 = const()[name = string("input_163_groups_0"), val = int32(1)]; + tensor const_56_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_56_quantized")]; + tensor const_57 = const()[name = string("const_57"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor out_25 = conv(bias = const_57, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_56_quantized, x = input_161)[name = string("out_25")]; + tensor input_165 = add(x = out_25, y = input_155)[name = string("input_165")]; + tensor input_167 = relu(x = input_165)[name = string("input_167")]; + string input_169_pad_type_0 = const()[name = string("input_169_pad_type_0"), val = string("custom")]; + tensor input_169_pad_0 = const()[name = string("input_169_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_169_strides_0 = const()[name = string("input_169_strides_0"), val = tensor([2, 2])]; + tensor input_169_dilations_0 = const()[name = string("input_169_dilations_0"), val = tensor([1, 1])]; + int32 input_169_groups_0 = const()[name = string("input_169_groups_0"), val = int32(1)]; + tensor const_58_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_58_quantized")]; + tensor const_59 = const()[name = string("const_59"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor input_171 = conv(bias = const_59, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_58_quantized, x = input_167)[name = string("input_171")]; + tensor input_173 = relu(x = input_171)[name = string("input_173")]; + string input_175_pad_type_0 = const()[name = string("input_175_pad_type_0"), val = string("custom")]; + tensor input_175_pad_0 = const()[name = string("input_175_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_175_strides_0 = const()[name = string("input_175_strides_0"), val = tensor([1, 1])]; + tensor input_175_dilations_0 = const()[name = string("input_175_dilations_0"), val = tensor([1, 1])]; + int32 input_175_groups_0 = const()[name = string("input_175_groups_0"), val = int32(1)]; + tensor const_60_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_60_quantized")]; + tensor const_61 = const()[name = string("const_61"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor out_27 = conv(bias = const_61, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_60_quantized, x = input_173)[name = string("out_27")]; + string input_177_pad_type_0 = const()[name = string("input_177_pad_type_0"), val = string("valid")]; + tensor input_177_strides_0 = const()[name = string("input_177_strides_0"), val = tensor([2, 2])]; + tensor input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_177_dilations_0 = const()[name = string("input_177_dilations_0"), val = tensor([1, 1])]; + int32 input_177_groups_0 = const()[name = string("input_177_groups_0"), val = int32(1)]; + tensor const_62_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_62_quantized")]; + tensor const_63 = const()[name = string("const_63"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor var_537 = conv(bias = const_63, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_62_quantized, x = input_167)[name = string("op_537")]; + tensor input_179 = add(x = out_27, y = var_537)[name = string("input_179")]; + tensor input_181 = relu(x = input_179)[name = string("input_181")]; + string input_183_pad_type_0 = const()[name = string("input_183_pad_type_0"), val = string("custom")]; + tensor input_183_pad_0 = const()[name = string("input_183_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_183_strides_0 = const()[name = string("input_183_strides_0"), val = tensor([1, 1])]; + tensor input_183_dilations_0 = const()[name = string("input_183_dilations_0"), val = tensor([1, 1])]; + int32 input_183_groups_0 = const()[name = string("input_183_groups_0"), val = int32(1)]; + tensor const_64_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_64_quantized")]; + tensor const_65 = const()[name = string("const_65"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor input_185 = conv(bias = const_65, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_64_quantized, x = input_181)[name = string("input_185")]; + tensor input_187 = relu(x = input_185)[name = string("input_187")]; + string input_189_pad_type_0 = const()[name = string("input_189_pad_type_0"), val = string("custom")]; + tensor input_189_pad_0 = const()[name = string("input_189_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_189_strides_0 = const()[name = string("input_189_strides_0"), val = tensor([1, 1])]; + tensor input_189_dilations_0 = const()[name = string("input_189_dilations_0"), val = tensor([1, 1])]; + int32 input_189_groups_0 = const()[name = string("input_189_groups_0"), val = int32(1)]; + tensor const_66_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_66_quantized")]; + tensor const_67 = const()[name = string("const_67"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor out_29 = conv(bias = const_67, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_66_quantized, x = input_187)[name = string("out_29")]; + tensor input_191 = add(x = out_29, y = input_181)[name = string("input_191")]; + tensor input_193 = relu(x = input_191)[name = string("input_193")]; + string input_195_pad_type_0 = const()[name = string("input_195_pad_type_0"), val = string("custom")]; + tensor input_195_pad_0 = const()[name = string("input_195_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_195_strides_0 = const()[name = string("input_195_strides_0"), val = tensor([1, 1])]; + tensor input_195_dilations_0 = const()[name = string("input_195_dilations_0"), val = tensor([1, 1])]; + int32 input_195_groups_0 = const()[name = string("input_195_groups_0"), val = int32(1)]; + tensor const_68_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_68_quantized")]; + tensor const_69 = const()[name = string("const_69"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor input_197 = conv(bias = const_69, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_68_quantized, x = input_193)[name = string("input_197")]; + tensor input_199 = relu(x = input_197)[name = string("input_199")]; + string input_201_pad_type_0 = const()[name = string("input_201_pad_type_0"), val = string("custom")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_201_strides_0 = const()[name = string("input_201_strides_0"), val = tensor([1, 1])]; + tensor input_201_dilations_0 = const()[name = string("input_201_dilations_0"), val = tensor([1, 1])]; + int32 input_201_groups_0 = const()[name = string("input_201_groups_0"), val = int32(1)]; + tensor const_70_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_70_quantized")]; + tensor const_71 = const()[name = string("const_71"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor out = conv(bias = const_71, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_70_quantized, x = input_199)[name = string("out")]; + tensor input_203 = add(x = out, y = input_193)[name = string("input_203")]; + tensor frames_1 = relu(x = input_203)[name = string("frames_1")]; + tensor concat_0x = const()[name = string("concat_0x"), val = tensor([-1, 2560, 125])]; + tensor frames = reshape(shape = concat_0x, x = frames_1)[name = string("frames")]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = frames)[name = string("tile_0")]; + tensor concat_1x = const()[name = string("concat_1x"), val = tensor([3, -1, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_1x, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_2 = const()[name = string("concat_2"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor sequences = reshape(shape = concat_2, x = transpose_0)[name = string("sequences")]; + tensor input_205_axes_0 = const()[name = string("input_205_axes_0"), val = tensor([1])]; + tensor input_205 = expand_dims(axes = input_205_axes_0, x = masks)[name = string("input_205")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_205)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor([3])]; + tensor weights = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")]; + tensor weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor([2])]; + bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)]; + tensor weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights)[name = string("weight_sum")]; + fp32 var_631 = const()[name = string("op_631"), val = fp32(0x0p+0)]; + tensor var_632 = greater(x = weight_sum, y = var_631)[name = string("op_632")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_632)[name = string("safe_sum")]; + tensor var_640 = mul(x = sequences, y = weights)[name = string("op_640")]; + tensor var_645_axes_0 = const()[name = string("op_645_axes_0"), val = tensor([2])]; + bool var_645_keep_dims_0 = const()[name = string("op_645_keep_dims_0"), val = bool(false)]; + tensor var_645 = reduce_sum(axes = var_645_axes_0, keep_dims = var_645_keep_dims_0, x = var_640)[name = string("op_645")]; + tensor mean = real_div(x = var_645, y = safe_sum)[name = string("mean")]; + tensor var_648_axes_0 = const()[name = string("op_648_axes_0"), val = tensor([2])]; + tensor var_648 = expand_dims(axes = var_648_axes_0, x = mean)[name = string("op_648")]; + tensor var_650 = sub(x = sequences, y = var_648)[name = string("op_650")]; + tensor dx2 = mul(x = var_650, y = var_650)[name = string("dx2")]; + tensor var_652 = mul(x = weights, y = weights)[name = string("op_652")]; + tensor weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor([2])]; + bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)]; + tensor weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_652)[name = string("weight_sq_sum")]; + tensor var_658 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_658")]; + tensor var_660 = sub(x = safe_sum, y = var_658)[name = string("op_660")]; + fp32 var_662 = const()[name = string("op_662"), val = fp32(0x1.5798eep-27)]; + tensor denom = add(x = var_660, y = var_662)[name = string("denom")]; + tensor var_664 = mul(x = dx2, y = weights)[name = string("op_664")]; + tensor var_669_axes_0 = const()[name = string("op_669_axes_0"), val = tensor([2])]; + bool var_669_keep_dims_0 = const()[name = string("op_669_keep_dims_0"), val = bool(false)]; + tensor var_669 = reduce_sum(axes = var_669_axes_0, keep_dims = var_669_keep_dims_0, x = var_664)[name = string("op_669")]; + tensor var = real_div(x = var_669, y = denom)[name = string("var")]; + fp32 var_671 = const()[name = string("op_671"), val = fp32(0x1.b7cdfep-34)]; + tensor var_672 = maximum(x = var, y = var_671)[name = string("op_672")]; + tensor std = sqrt(x = var_672)[name = string("std")]; + int32 var_675 = const()[name = string("op_675"), val = int32(-1)]; + bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)]; + tensor stats = concat(axis = var_675, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")]; + tensor var_682 = sub(x = mean, y = mean)[name = string("sub_0")]; + fp32 var_689_value_0 = const()[name = string("op_689_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor var_689 = fill_like(ref_tensor = std, value = var_689_value_0)[name = string("op_689")]; + int32 var_691 = const()[name = string("op_691"), val = int32(-1)]; + bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)]; + tensor zero_stats = concat(axis = var_691, interleave = zero_stats_interleave_0, values = (var_682, var_689))[name = string("zero_stats")]; + fp32 var_693 = const()[name = string("op_693"), val = fp32(0x0p+0)]; + tensor var_694 = less_equal(x = weight_sum, y = var_693)[name = string("op_694")]; + tensor var_700 = const()[name = string("op_700"), val = tensor([1, 5120])]; + tensor zero_mask = tile(reps = var_700, x = var_694)[name = string("zero_mask")]; + tensor input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")]; + tensor output = linear(bias = resnet_seg_1_bias, weight = resnet_seg_1_weight_quantized, x = input)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-multimask-tail-b32-w8a16.mlmodelc/weights/weight.bin b/wespeaker-multimask-tail-b32-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..3114ce354da5e5fe51316189c5c49b584a0ff08f --- /dev/null +++ b/wespeaker-multimask-tail-b32-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed5873a1aeef9493f6ca8659ff3b75b7673a6b1d450e8973d165d2913181013a +size 6675328 diff --git a/wespeaker-multimask-tail-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-multimask-tail-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2f5219890a027e3eeec935795345861b0fc3b80c --- /dev/null +++ b/wespeaker-multimask-tail-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:932374c6884544be9b259bf8ef8b1a0433cbac96b81e12f3ed5be944c2d1dfb4 +size 243 diff --git a/wespeaker-multimask-tail-w8a16.mlmodelc/coremldata.bin b/wespeaker-multimask-tail-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..13447ba74b372718cc0292257afd1fde4ef18990 --- /dev/null +++ b/wespeaker-multimask-tail-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7dc4a246f1390e2bfe56f0bdbd156e662e50c6538ac084e7e6c4484e23e2384 +size 426 diff --git a/wespeaker-multimask-tail-w8a16.mlmodelc/model.mil b/wespeaker-multimask-tail-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..74628e065bdd712b52e0ab758135631a0f82809b --- /dev/null +++ b/wespeaker-multimask-tail-w8a16.mlmodelc/model.mil @@ -0,0 +1,416 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor masks) [FlexibleShapeInformation = tuple>>, tuple>>>>((("DefaultShapes", {{"fbank", [32, 998, 80]}, {"masks", [96, 589]}}), ("EnumeratedShapes", {{"98e0d0f3", {{"fbank", [32, 998, 80]}, {"masks", [96, 589]}}}, {"fd4e3aa9", {{"fbank", [1, 998, 80]}, {"masks", [3, 589]}}}})))] { + tensor resnet_seg_1_bias = const()[name = string("resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936))))[name = string("resnet_seg_1_weight_quantized")]; + tensor var_20 = const()[name = string("op_20"), val = tensor([0, 2, 1])]; + tensor input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor([1])]; + tensor fbank_1 = transpose(perm = var_20, x = fbank)[name = string("transpose_1")]; + tensor input_1 = expand_dims(axes = input_1_axes_0, x = fbank_1)[name = string("input_1")]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor([1, 1])]; + tensor input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor([1, 1])]; + int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; + tensor const_0 = const()[name = string("const_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_1 = const()[name = string("const_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor input_5 = conv(bias = const_1, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_0, x = input_1)[name = string("input_5")]; + tensor input_7 = relu(x = input_5)[name = string("input_7")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1, 1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor const_2_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_2_quantized")]; + tensor const_3 = const()[name = string("const_3"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor input_11 = conv(bias = const_3, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_2_quantized, x = input_7)[name = string("input_11")]; + tensor input_13 = relu(x = input_11)[name = string("input_13")]; + string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")]; + tensor input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor([1, 1])]; + int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)]; + tensor const_4_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_4_quantized")]; + tensor const_5 = const()[name = string("const_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor out_1 = conv(bias = const_5, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_4_quantized, x = input_13)[name = string("out_1")]; + tensor input_17 = add(x = out_1, y = input_7)[name = string("input_17")]; + tensor input_19 = relu(x = input_17)[name = string("input_19")]; + string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")]; + tensor input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor([1, 1])]; + tensor input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor([1, 1])]; + int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)]; + tensor const_6_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_6_quantized")]; + tensor const_7 = const()[name = string("const_7"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor input_23 = conv(bias = const_7, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_6_quantized, x = input_19)[name = string("input_23")]; + tensor input_25 = relu(x = input_23)[name = string("input_25")]; + string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")]; + tensor input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor([1, 1])]; + tensor input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor([1, 1])]; + int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)]; + tensor const_8_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_8_quantized")]; + tensor const_9 = const()[name = string("const_9"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor out_3 = conv(bias = const_9, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_8_quantized, x = input_25)[name = string("out_3")]; + tensor input_29 = add(x = out_3, y = input_19)[name = string("input_29")]; + tensor input_31 = relu(x = input_29)[name = string("input_31")]; + string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")]; + tensor input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor([1, 1])]; + tensor input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor([1, 1])]; + int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)]; + tensor const_10_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_10_quantized")]; + tensor const_11 = const()[name = string("const_11"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor input_35 = conv(bias = const_11, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_10_quantized, x = input_31)[name = string("input_35")]; + tensor input_37 = relu(x = input_35)[name = string("input_37")]; + string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("custom")]; + tensor input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor([1, 1])]; + int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)]; + tensor const_12_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_12_quantized")]; + tensor const_13 = const()[name = string("const_13"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor out_5 = conv(bias = const_13, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_12_quantized, x = input_37)[name = string("out_5")]; + tensor input_41 = add(x = out_5, y = input_31)[name = string("input_41")]; + tensor input_43 = relu(x = input_41)[name = string("input_43")]; + string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")]; + tensor input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor([2, 2])]; + tensor input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor([1, 1])]; + int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)]; + tensor const_14_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_14_quantized")]; + tensor const_15 = const()[name = string("const_15"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor input_47 = conv(bias = const_15, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_14_quantized, x = input_43)[name = string("input_47")]; + tensor input_49 = relu(x = input_47)[name = string("input_49")]; + string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")]; + tensor input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor([1, 1])]; + tensor input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor([1, 1])]; + int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)]; + tensor const_16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_16_quantized")]; + tensor const_17 = const()[name = string("const_17"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor out_7 = conv(bias = const_17, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_16_quantized, x = input_49)[name = string("out_7")]; + string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")]; + tensor input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor([2, 2])]; + tensor input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor([1, 1])]; + int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)]; + tensor const_18 = const()[name = string("const_18"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_19 = const()[name = string("const_19"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor var_194 = conv(bias = const_19, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_18, x = input_43)[name = string("op_194")]; + tensor input_55 = add(x = out_7, y = var_194)[name = string("input_55")]; + tensor input_57 = relu(x = input_55)[name = string("input_57")]; + string input_59_pad_type_0 = const()[name = string("input_59_pad_type_0"), val = string("custom")]; + tensor input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_59_strides_0 = const()[name = string("input_59_strides_0"), val = tensor([1, 1])]; + tensor input_59_dilations_0 = const()[name = string("input_59_dilations_0"), val = tensor([1, 1])]; + int32 input_59_groups_0 = const()[name = string("input_59_groups_0"), val = int32(1)]; + tensor const_20_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_20_quantized")]; + tensor const_21 = const()[name = string("const_21"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor input_61 = conv(bias = const_21, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_20_quantized, x = input_57)[name = string("input_61")]; + tensor input_63 = relu(x = input_61)[name = string("input_63")]; + string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")]; + tensor input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor([1, 1])]; + tensor input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor([1, 1])]; + int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)]; + tensor const_22_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_22_quantized")]; + tensor const_23 = const()[name = string("const_23"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor out_9 = conv(bias = const_23, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_22_quantized, x = input_63)[name = string("out_9")]; + tensor input_67 = add(x = out_9, y = input_57)[name = string("input_67")]; + tensor input_69 = relu(x = input_67)[name = string("input_69")]; + string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")]; + tensor input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor([1, 1])]; + tensor input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor([1, 1])]; + int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)]; + tensor const_24_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_24_quantized")]; + tensor const_25 = const()[name = string("const_25"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor input_73 = conv(bias = const_25, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_24_quantized, x = input_69)[name = string("input_73")]; + tensor input_75 = relu(x = input_73)[name = string("input_75")]; + string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")]; + tensor input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor([1, 1])]; + tensor input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor([1, 1])]; + int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)]; + tensor const_26_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_26_quantized")]; + tensor const_27 = const()[name = string("const_27"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor out_11 = conv(bias = const_27, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_26_quantized, x = input_75)[name = string("out_11")]; + tensor input_79 = add(x = out_11, y = input_69)[name = string("input_79")]; + tensor input_81 = relu(x = input_79)[name = string("input_81")]; + string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")]; + tensor input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor([1, 1])]; + tensor input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor([1, 1])]; + int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(1)]; + tensor const_28_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_28_quantized")]; + tensor const_29 = const()[name = string("const_29"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor input_85 = conv(bias = const_29, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_28_quantized, x = input_81)[name = string("input_85")]; + tensor input_87 = relu(x = input_85)[name = string("input_87")]; + string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("custom")]; + tensor input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor([1, 1])]; + tensor input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor([1, 1])]; + int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)]; + tensor const_30_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_30_quantized")]; + tensor const_31 = const()[name = string("const_31"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor out_13 = conv(bias = const_31, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_30_quantized, x = input_87)[name = string("out_13")]; + tensor input_91 = add(x = out_13, y = input_81)[name = string("input_91")]; + tensor input_93 = relu(x = input_91)[name = string("input_93")]; + string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("custom")]; + tensor input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor([2, 2])]; + tensor input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor([1, 1])]; + int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)]; + tensor const_32_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_32_quantized")]; + tensor const_33 = const()[name = string("const_33"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor input_97 = conv(bias = const_33, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_32_quantized, x = input_93)[name = string("input_97")]; + tensor input_99 = relu(x = input_97)[name = string("input_99")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("custom")]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1, 1])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1, 1])]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)]; + tensor const_34_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_34_quantized")]; + tensor const_35 = const()[name = string("const_35"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor out_15 = conv(bias = const_35, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_34_quantized, x = input_99)[name = string("out_15")]; + string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")]; + tensor input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor([2, 2])]; + tensor input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor([1, 1])]; + int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; + tensor const_36_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_36_quantized")]; + tensor const_37 = const()[name = string("const_37"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor var_338 = conv(bias = const_37, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_36_quantized, x = input_93)[name = string("op_338")]; + tensor input_105 = add(x = out_15, y = var_338)[name = string("input_105")]; + tensor input_107 = relu(x = input_105)[name = string("input_107")]; + string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("custom")]; + tensor input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor([1, 1])]; + tensor input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor([1, 1])]; + int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)]; + tensor const_38_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_38_quantized")]; + tensor const_39 = const()[name = string("const_39"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor input_111 = conv(bias = const_39, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_38_quantized, x = input_107)[name = string("input_111")]; + tensor input_113 = relu(x = input_111)[name = string("input_113")]; + string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("custom")]; + tensor input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_115_strides_0 = const()[name = string("input_115_strides_0"), val = tensor([1, 1])]; + tensor input_115_dilations_0 = const()[name = string("input_115_dilations_0"), val = tensor([1, 1])]; + int32 input_115_groups_0 = const()[name = string("input_115_groups_0"), val = int32(1)]; + tensor const_40_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_40_quantized")]; + tensor const_41 = const()[name = string("const_41"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor out_17 = conv(bias = const_41, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_40_quantized, x = input_113)[name = string("out_17")]; + tensor input_117 = add(x = out_17, y = input_107)[name = string("input_117")]; + tensor input_119 = relu(x = input_117)[name = string("input_119")]; + string input_121_pad_type_0 = const()[name = string("input_121_pad_type_0"), val = string("custom")]; + tensor input_121_pad_0 = const()[name = string("input_121_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_121_strides_0 = const()[name = string("input_121_strides_0"), val = tensor([1, 1])]; + tensor input_121_dilations_0 = const()[name = string("input_121_dilations_0"), val = tensor([1, 1])]; + int32 input_121_groups_0 = const()[name = string("input_121_groups_0"), val = int32(1)]; + tensor const_42_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_42_quantized")]; + tensor const_43 = const()[name = string("const_43"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor input_123 = conv(bias = const_43, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_42_quantized, x = input_119)[name = string("input_123")]; + tensor input_125 = relu(x = input_123)[name = string("input_125")]; + string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("custom")]; + tensor input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor([1, 1])]; + tensor input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor([1, 1])]; + int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)]; + tensor const_44_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_44_quantized")]; + tensor const_45 = const()[name = string("const_45"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor out_19 = conv(bias = const_45, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_44_quantized, x = input_125)[name = string("out_19")]; + tensor input_129 = add(x = out_19, y = input_119)[name = string("input_129")]; + tensor input_131 = relu(x = input_129)[name = string("input_131")]; + string input_133_pad_type_0 = const()[name = string("input_133_pad_type_0"), val = string("custom")]; + tensor input_133_pad_0 = const()[name = string("input_133_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_133_strides_0 = const()[name = string("input_133_strides_0"), val = tensor([1, 1])]; + tensor input_133_dilations_0 = const()[name = string("input_133_dilations_0"), val = tensor([1, 1])]; + int32 input_133_groups_0 = const()[name = string("input_133_groups_0"), val = int32(1)]; + tensor const_46_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_46_quantized")]; + tensor const_47 = const()[name = string("const_47"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor input_135 = conv(bias = const_47, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_46_quantized, x = input_131)[name = string("input_135")]; + tensor input_137 = relu(x = input_135)[name = string("input_137")]; + string input_139_pad_type_0 = const()[name = string("input_139_pad_type_0"), val = string("custom")]; + tensor input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_139_strides_0 = const()[name = string("input_139_strides_0"), val = tensor([1, 1])]; + tensor input_139_dilations_0 = const()[name = string("input_139_dilations_0"), val = tensor([1, 1])]; + int32 input_139_groups_0 = const()[name = string("input_139_groups_0"), val = int32(1)]; + tensor const_48_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_48_quantized")]; + tensor const_49 = const()[name = string("const_49"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor out_21 = conv(bias = const_49, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_48_quantized, x = input_137)[name = string("out_21")]; + tensor input_141 = add(x = out_21, y = input_131)[name = string("input_141")]; + tensor input_143 = relu(x = input_141)[name = string("input_143")]; + string input_145_pad_type_0 = const()[name = string("input_145_pad_type_0"), val = string("custom")]; + tensor input_145_pad_0 = const()[name = string("input_145_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_145_strides_0 = const()[name = string("input_145_strides_0"), val = tensor([1, 1])]; + tensor input_145_dilations_0 = const()[name = string("input_145_dilations_0"), val = tensor([1, 1])]; + int32 input_145_groups_0 = const()[name = string("input_145_groups_0"), val = int32(1)]; + tensor const_50_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_50_quantized")]; + tensor const_51 = const()[name = string("const_51"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor input_147 = conv(bias = const_51, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_50_quantized, x = input_143)[name = string("input_147")]; + tensor input_149 = relu(x = input_147)[name = string("input_149")]; + string input_151_pad_type_0 = const()[name = string("input_151_pad_type_0"), val = string("custom")]; + tensor input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_151_strides_0 = const()[name = string("input_151_strides_0"), val = tensor([1, 1])]; + tensor input_151_dilations_0 = const()[name = string("input_151_dilations_0"), val = tensor([1, 1])]; + int32 input_151_groups_0 = const()[name = string("input_151_groups_0"), val = int32(1)]; + tensor const_52_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_52_quantized")]; + tensor const_53 = const()[name = string("const_53"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor out_23 = conv(bias = const_53, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_52_quantized, x = input_149)[name = string("out_23")]; + tensor input_153 = add(x = out_23, y = input_143)[name = string("input_153")]; + tensor input_155 = relu(x = input_153)[name = string("input_155")]; + string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("custom")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor([1, 1])]; + tensor input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor([1, 1])]; + int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)]; + tensor const_54_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_54_quantized")]; + tensor const_55 = const()[name = string("const_55"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor input_159 = conv(bias = const_55, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_54_quantized, x = input_155)[name = string("input_159")]; + tensor input_161 = relu(x = input_159)[name = string("input_161")]; + string input_163_pad_type_0 = const()[name = string("input_163_pad_type_0"), val = string("custom")]; + tensor input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_163_strides_0 = const()[name = string("input_163_strides_0"), val = tensor([1, 1])]; + tensor input_163_dilations_0 = const()[name = string("input_163_dilations_0"), val = tensor([1, 1])]; + int32 input_163_groups_0 = const()[name = string("input_163_groups_0"), val = int32(1)]; + tensor const_56_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_56_quantized")]; + tensor const_57 = const()[name = string("const_57"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor out_25 = conv(bias = const_57, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_56_quantized, x = input_161)[name = string("out_25")]; + tensor input_165 = add(x = out_25, y = input_155)[name = string("input_165")]; + tensor input_167 = relu(x = input_165)[name = string("input_167")]; + string input_169_pad_type_0 = const()[name = string("input_169_pad_type_0"), val = string("custom")]; + tensor input_169_pad_0 = const()[name = string("input_169_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_169_strides_0 = const()[name = string("input_169_strides_0"), val = tensor([2, 2])]; + tensor input_169_dilations_0 = const()[name = string("input_169_dilations_0"), val = tensor([1, 1])]; + int32 input_169_groups_0 = const()[name = string("input_169_groups_0"), val = int32(1)]; + tensor const_58_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_58_quantized")]; + tensor const_59 = const()[name = string("const_59"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor input_171 = conv(bias = const_59, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_58_quantized, x = input_167)[name = string("input_171")]; + tensor input_173 = relu(x = input_171)[name = string("input_173")]; + string input_175_pad_type_0 = const()[name = string("input_175_pad_type_0"), val = string("custom")]; + tensor input_175_pad_0 = const()[name = string("input_175_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_175_strides_0 = const()[name = string("input_175_strides_0"), val = tensor([1, 1])]; + tensor input_175_dilations_0 = const()[name = string("input_175_dilations_0"), val = tensor([1, 1])]; + int32 input_175_groups_0 = const()[name = string("input_175_groups_0"), val = int32(1)]; + tensor const_60_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_60_quantized")]; + tensor const_61 = const()[name = string("const_61"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor out_27 = conv(bias = const_61, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_60_quantized, x = input_173)[name = string("out_27")]; + string input_177_pad_type_0 = const()[name = string("input_177_pad_type_0"), val = string("valid")]; + tensor input_177_strides_0 = const()[name = string("input_177_strides_0"), val = tensor([2, 2])]; + tensor input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_177_dilations_0 = const()[name = string("input_177_dilations_0"), val = tensor([1, 1])]; + int32 input_177_groups_0 = const()[name = string("input_177_groups_0"), val = int32(1)]; + tensor const_62_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_62_quantized")]; + tensor const_63 = const()[name = string("const_63"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor var_537 = conv(bias = const_63, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_62_quantized, x = input_167)[name = string("op_537")]; + tensor input_179 = add(x = out_27, y = var_537)[name = string("input_179")]; + tensor input_181 = relu(x = input_179)[name = string("input_181")]; + string input_183_pad_type_0 = const()[name = string("input_183_pad_type_0"), val = string("custom")]; + tensor input_183_pad_0 = const()[name = string("input_183_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_183_strides_0 = const()[name = string("input_183_strides_0"), val = tensor([1, 1])]; + tensor input_183_dilations_0 = const()[name = string("input_183_dilations_0"), val = tensor([1, 1])]; + int32 input_183_groups_0 = const()[name = string("input_183_groups_0"), val = int32(1)]; + tensor const_64_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_64_quantized")]; + tensor const_65 = const()[name = string("const_65"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor input_185 = conv(bias = const_65, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_64_quantized, x = input_181)[name = string("input_185")]; + tensor input_187 = relu(x = input_185)[name = string("input_187")]; + string input_189_pad_type_0 = const()[name = string("input_189_pad_type_0"), val = string("custom")]; + tensor input_189_pad_0 = const()[name = string("input_189_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_189_strides_0 = const()[name = string("input_189_strides_0"), val = tensor([1, 1])]; + tensor input_189_dilations_0 = const()[name = string("input_189_dilations_0"), val = tensor([1, 1])]; + int32 input_189_groups_0 = const()[name = string("input_189_groups_0"), val = int32(1)]; + tensor const_66_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_66_quantized")]; + tensor const_67 = const()[name = string("const_67"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor out_29 = conv(bias = const_67, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_66_quantized, x = input_187)[name = string("out_29")]; + tensor input_191 = add(x = out_29, y = input_181)[name = string("input_191")]; + tensor input_193 = relu(x = input_191)[name = string("input_193")]; + string input_195_pad_type_0 = const()[name = string("input_195_pad_type_0"), val = string("custom")]; + tensor input_195_pad_0 = const()[name = string("input_195_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_195_strides_0 = const()[name = string("input_195_strides_0"), val = tensor([1, 1])]; + tensor input_195_dilations_0 = const()[name = string("input_195_dilations_0"), val = tensor([1, 1])]; + int32 input_195_groups_0 = const()[name = string("input_195_groups_0"), val = int32(1)]; + tensor const_68_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_68_quantized")]; + tensor const_69 = const()[name = string("const_69"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor input_197 = conv(bias = const_69, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_68_quantized, x = input_193)[name = string("input_197")]; + tensor input_199 = relu(x = input_197)[name = string("input_199")]; + string input_201_pad_type_0 = const()[name = string("input_201_pad_type_0"), val = string("custom")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_201_strides_0 = const()[name = string("input_201_strides_0"), val = tensor([1, 1])]; + tensor input_201_dilations_0 = const()[name = string("input_201_dilations_0"), val = tensor([1, 1])]; + int32 input_201_groups_0 = const()[name = string("input_201_groups_0"), val = int32(1)]; + tensor const_70_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_70_quantized")]; + tensor const_71 = const()[name = string("const_71"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor out = conv(bias = const_71, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_70_quantized, x = input_199)[name = string("out")]; + tensor input_203 = add(x = out, y = input_193)[name = string("input_203")]; + tensor frames_1 = relu(x = input_203)[name = string("frames_1")]; + tensor concat_0x = const()[name = string("concat_0x"), val = tensor([-1, 2560, 125])]; + tensor frames = reshape(shape = concat_0x, x = frames_1)[name = string("frames")]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([3, 1, 1])]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = frames)[name = string("tile_0")]; + tensor concat_1x = const()[name = string("concat_1x"), val = tensor([3, -1, 2560, 125])]; + tensor reshape_0 = reshape(shape = concat_1x, x = tile_0)[name = string("reshape_0")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0, 2, 3])]; + tensor concat_2 = const()[name = string("concat_2"), val = tensor([-1, 2560, 125])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")]; + tensor sequences = reshape(shape = concat_2, x = transpose_0)[name = string("sequences")]; + tensor input_205_axes_0 = const()[name = string("input_205_axes_0"), val = tensor([1])]; + tensor input_205 = expand_dims(axes = input_205_axes_0, x = masks)[name = string("input_205")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_205)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor([3])]; + tensor weights = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")]; + tensor weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor([2])]; + bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)]; + tensor weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights)[name = string("weight_sum")]; + fp32 var_631 = const()[name = string("op_631"), val = fp32(0x0p+0)]; + tensor var_632 = greater(x = weight_sum, y = var_631)[name = string("op_632")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_632)[name = string("safe_sum")]; + tensor var_640 = mul(x = sequences, y = weights)[name = string("op_640")]; + tensor var_645_axes_0 = const()[name = string("op_645_axes_0"), val = tensor([2])]; + bool var_645_keep_dims_0 = const()[name = string("op_645_keep_dims_0"), val = bool(false)]; + tensor var_645 = reduce_sum(axes = var_645_axes_0, keep_dims = var_645_keep_dims_0, x = var_640)[name = string("op_645")]; + tensor mean = real_div(x = var_645, y = safe_sum)[name = string("mean")]; + tensor var_648_axes_0 = const()[name = string("op_648_axes_0"), val = tensor([2])]; + tensor var_648 = expand_dims(axes = var_648_axes_0, x = mean)[name = string("op_648")]; + tensor var_650 = sub(x = sequences, y = var_648)[name = string("op_650")]; + tensor dx2 = mul(x = var_650, y = var_650)[name = string("dx2")]; + tensor var_652 = mul(x = weights, y = weights)[name = string("op_652")]; + tensor weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor([2])]; + bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)]; + tensor weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_652)[name = string("weight_sq_sum")]; + tensor var_658 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_658")]; + tensor var_660 = sub(x = safe_sum, y = var_658)[name = string("op_660")]; + fp32 var_662 = const()[name = string("op_662"), val = fp32(0x1.5798eep-27)]; + tensor denom = add(x = var_660, y = var_662)[name = string("denom")]; + tensor var_664 = mul(x = dx2, y = weights)[name = string("op_664")]; + tensor var_669_axes_0 = const()[name = string("op_669_axes_0"), val = tensor([2])]; + bool var_669_keep_dims_0 = const()[name = string("op_669_keep_dims_0"), val = bool(false)]; + tensor var_669 = reduce_sum(axes = var_669_axes_0, keep_dims = var_669_keep_dims_0, x = var_664)[name = string("op_669")]; + tensor var = real_div(x = var_669, y = denom)[name = string("var")]; + fp32 var_671 = const()[name = string("op_671"), val = fp32(0x1.b7cdfep-34)]; + tensor var_672 = maximum(x = var, y = var_671)[name = string("op_672")]; + tensor std = sqrt(x = var_672)[name = string("std")]; + int32 var_675 = const()[name = string("op_675"), val = int32(-1)]; + bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)]; + tensor stats = concat(axis = var_675, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")]; + tensor var_682 = sub(x = mean, y = mean)[name = string("sub_0")]; + fp32 var_689_value_0 = const()[name = string("op_689_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor var_689 = fill_like(ref_tensor = std, value = var_689_value_0)[name = string("op_689")]; + int32 var_691 = const()[name = string("op_691"), val = int32(-1)]; + bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)]; + tensor zero_stats = concat(axis = var_691, interleave = zero_stats_interleave_0, values = (var_682, var_689))[name = string("zero_stats")]; + fp32 var_693 = const()[name = string("op_693"), val = fp32(0x0p+0)]; + tensor var_694 = less_equal(x = weight_sum, y = var_693)[name = string("op_694")]; + tensor var_700 = const()[name = string("op_700"), val = tensor([1, 5120])]; + tensor zero_mask = tile(reps = var_700, x = var_694)[name = string("zero_mask")]; + tensor input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")]; + tensor output = linear(bias = resnet_seg_1_bias, weight = resnet_seg_1_weight_quantized, x = input)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-multimask-tail-w8a16.mlmodelc/weights/weight.bin b/wespeaker-multimask-tail-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..3114ce354da5e5fe51316189c5c49b584a0ff08f --- /dev/null +++ b/wespeaker-multimask-tail-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed5873a1aeef9493f6ca8659ff3b75b7673a6b1d450e8973d165d2913181013a +size 6675328 diff --git a/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..988ce7018e2872e6116dc111c94fad2e79d2bb4b --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f137a77cbeb0ad73acb52c3abd3e2ec7d077807afbb3bc50c3647e49ffba53d9 +size 243 diff --git a/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/coremldata.bin b/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..7368358583a65689f082ce0b87a68fbd2cc53428 --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:faca12fea014822a2842e0945d41613309b2315a832efa3734247d71361354ea +size 443 diff --git a/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/model.mil b/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..b3513e9f08dfd798bed3c079fa4e7853710ded6f --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/model.mil @@ -0,0 +1,408 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor weights) [FlexibleShapeInformation = tuple>>, tuple>>>>((("DefaultShapes", {{"fbank", [32, 998, 80]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"316ab78f", {{"fbank", [3, 998, 80]}, {"weights", [3, 589]}}}, {"f6770b54", {{"fbank", [1, 998, 80]}, {"weights", [1, 589]}}}, {"fd0b6e18", {{"fbank", [32, 998, 80]}, {"weights", [32, 589]}}}})))] { + tensor resnet_seg_1_bias = const()[name = string("resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936))))[name = string("resnet_seg_1_weight_quantized")]; + tensor var_20 = const()[name = string("op_20"), val = tensor([0, 2, 1])]; + tensor input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor([1])]; + tensor fbank_1 = transpose(perm = var_20, x = fbank)[name = string("transpose_0")]; + tensor input_1 = expand_dims(axes = input_1_axes_0, x = fbank_1)[name = string("input_1")]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor([1, 1])]; + tensor input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor([1, 1])]; + int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; + tensor const_0 = const()[name = string("const_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_1 = const()[name = string("const_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor input_5 = conv(bias = const_1, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_0, x = input_1)[name = string("input_5")]; + tensor input_7 = relu(x = input_5)[name = string("input_7")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1, 1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor const_2_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_2_quantized")]; + tensor const_3 = const()[name = string("const_3"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor input_11 = conv(bias = const_3, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_2_quantized, x = input_7)[name = string("input_11")]; + tensor input_13 = relu(x = input_11)[name = string("input_13")]; + string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")]; + tensor input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor([1, 1])]; + int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)]; + tensor const_4_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_4_quantized")]; + tensor const_5 = const()[name = string("const_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor out_1 = conv(bias = const_5, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_4_quantized, x = input_13)[name = string("out_1")]; + tensor input_17 = add(x = out_1, y = input_7)[name = string("input_17")]; + tensor input_19 = relu(x = input_17)[name = string("input_19")]; + string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")]; + tensor input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor([1, 1])]; + tensor input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor([1, 1])]; + int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)]; + tensor const_6_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_6_quantized")]; + tensor const_7 = const()[name = string("const_7"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor input_23 = conv(bias = const_7, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_6_quantized, x = input_19)[name = string("input_23")]; + tensor input_25 = relu(x = input_23)[name = string("input_25")]; + string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")]; + tensor input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor([1, 1])]; + tensor input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor([1, 1])]; + int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)]; + tensor const_8_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_8_quantized")]; + tensor const_9 = const()[name = string("const_9"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor out_3 = conv(bias = const_9, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_8_quantized, x = input_25)[name = string("out_3")]; + tensor input_29 = add(x = out_3, y = input_19)[name = string("input_29")]; + tensor input_31 = relu(x = input_29)[name = string("input_31")]; + string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")]; + tensor input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor([1, 1])]; + tensor input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor([1, 1])]; + int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)]; + tensor const_10_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_10_quantized")]; + tensor const_11 = const()[name = string("const_11"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor input_35 = conv(bias = const_11, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_10_quantized, x = input_31)[name = string("input_35")]; + tensor input_37 = relu(x = input_35)[name = string("input_37")]; + string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("custom")]; + tensor input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor([1, 1])]; + int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)]; + tensor const_12_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_12_quantized")]; + tensor const_13 = const()[name = string("const_13"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor out_5 = conv(bias = const_13, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_12_quantized, x = input_37)[name = string("out_5")]; + tensor input_41 = add(x = out_5, y = input_31)[name = string("input_41")]; + tensor input_43 = relu(x = input_41)[name = string("input_43")]; + string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")]; + tensor input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor([2, 2])]; + tensor input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor([1, 1])]; + int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)]; + tensor const_14_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_14_quantized")]; + tensor const_15 = const()[name = string("const_15"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor input_47 = conv(bias = const_15, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_14_quantized, x = input_43)[name = string("input_47")]; + tensor input_49 = relu(x = input_47)[name = string("input_49")]; + string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")]; + tensor input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor([1, 1])]; + tensor input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor([1, 1])]; + int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)]; + tensor const_16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_16_quantized")]; + tensor const_17 = const()[name = string("const_17"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor out_7 = conv(bias = const_17, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_16_quantized, x = input_49)[name = string("out_7")]; + string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")]; + tensor input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor([2, 2])]; + tensor input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor([1, 1])]; + int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)]; + tensor const_18 = const()[name = string("const_18"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_19 = const()[name = string("const_19"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor var_194 = conv(bias = const_19, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_18, x = input_43)[name = string("op_194")]; + tensor input_55 = add(x = out_7, y = var_194)[name = string("input_55")]; + tensor input_57 = relu(x = input_55)[name = string("input_57")]; + string input_59_pad_type_0 = const()[name = string("input_59_pad_type_0"), val = string("custom")]; + tensor input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_59_strides_0 = const()[name = string("input_59_strides_0"), val = tensor([1, 1])]; + tensor input_59_dilations_0 = const()[name = string("input_59_dilations_0"), val = tensor([1, 1])]; + int32 input_59_groups_0 = const()[name = string("input_59_groups_0"), val = int32(1)]; + tensor const_20_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_20_quantized")]; + tensor const_21 = const()[name = string("const_21"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor input_61 = conv(bias = const_21, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_20_quantized, x = input_57)[name = string("input_61")]; + tensor input_63 = relu(x = input_61)[name = string("input_63")]; + string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")]; + tensor input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor([1, 1])]; + tensor input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor([1, 1])]; + int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)]; + tensor const_22_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_22_quantized")]; + tensor const_23 = const()[name = string("const_23"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor out_9 = conv(bias = const_23, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_22_quantized, x = input_63)[name = string("out_9")]; + tensor input_67 = add(x = out_9, y = input_57)[name = string("input_67")]; + tensor input_69 = relu(x = input_67)[name = string("input_69")]; + string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")]; + tensor input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor([1, 1])]; + tensor input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor([1, 1])]; + int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)]; + tensor const_24_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_24_quantized")]; + tensor const_25 = const()[name = string("const_25"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor input_73 = conv(bias = const_25, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_24_quantized, x = input_69)[name = string("input_73")]; + tensor input_75 = relu(x = input_73)[name = string("input_75")]; + string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")]; + tensor input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor([1, 1])]; + tensor input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor([1, 1])]; + int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)]; + tensor const_26_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_26_quantized")]; + tensor const_27 = const()[name = string("const_27"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor out_11 = conv(bias = const_27, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_26_quantized, x = input_75)[name = string("out_11")]; + tensor input_79 = add(x = out_11, y = input_69)[name = string("input_79")]; + tensor input_81 = relu(x = input_79)[name = string("input_81")]; + string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")]; + tensor input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor([1, 1])]; + tensor input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor([1, 1])]; + int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(1)]; + tensor const_28_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_28_quantized")]; + tensor const_29 = const()[name = string("const_29"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor input_85 = conv(bias = const_29, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_28_quantized, x = input_81)[name = string("input_85")]; + tensor input_87 = relu(x = input_85)[name = string("input_87")]; + string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("custom")]; + tensor input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor([1, 1])]; + tensor input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor([1, 1])]; + int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)]; + tensor const_30_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_30_quantized")]; + tensor const_31 = const()[name = string("const_31"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor out_13 = conv(bias = const_31, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_30_quantized, x = input_87)[name = string("out_13")]; + tensor input_91 = add(x = out_13, y = input_81)[name = string("input_91")]; + tensor input_93 = relu(x = input_91)[name = string("input_93")]; + string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("custom")]; + tensor input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor([2, 2])]; + tensor input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor([1, 1])]; + int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)]; + tensor const_32_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_32_quantized")]; + tensor const_33 = const()[name = string("const_33"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor input_97 = conv(bias = const_33, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_32_quantized, x = input_93)[name = string("input_97")]; + tensor input_99 = relu(x = input_97)[name = string("input_99")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("custom")]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1, 1])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1, 1])]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)]; + tensor const_34_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_34_quantized")]; + tensor const_35 = const()[name = string("const_35"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor out_15 = conv(bias = const_35, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_34_quantized, x = input_99)[name = string("out_15")]; + string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")]; + tensor input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor([2, 2])]; + tensor input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor([1, 1])]; + int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; + tensor const_36_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_36_quantized")]; + tensor const_37 = const()[name = string("const_37"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor var_338 = conv(bias = const_37, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_36_quantized, x = input_93)[name = string("op_338")]; + tensor input_105 = add(x = out_15, y = var_338)[name = string("input_105")]; + tensor input_107 = relu(x = input_105)[name = string("input_107")]; + string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("custom")]; + tensor input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor([1, 1])]; + tensor input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor([1, 1])]; + int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)]; + tensor const_38_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_38_quantized")]; + tensor const_39 = const()[name = string("const_39"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor input_111 = conv(bias = const_39, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_38_quantized, x = input_107)[name = string("input_111")]; + tensor input_113 = relu(x = input_111)[name = string("input_113")]; + string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("custom")]; + tensor input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_115_strides_0 = const()[name = string("input_115_strides_0"), val = tensor([1, 1])]; + tensor input_115_dilations_0 = const()[name = string("input_115_dilations_0"), val = tensor([1, 1])]; + int32 input_115_groups_0 = const()[name = string("input_115_groups_0"), val = int32(1)]; + tensor const_40_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_40_quantized")]; + tensor const_41 = const()[name = string("const_41"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor out_17 = conv(bias = const_41, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_40_quantized, x = input_113)[name = string("out_17")]; + tensor input_117 = add(x = out_17, y = input_107)[name = string("input_117")]; + tensor input_119 = relu(x = input_117)[name = string("input_119")]; + string input_121_pad_type_0 = const()[name = string("input_121_pad_type_0"), val = string("custom")]; + tensor input_121_pad_0 = const()[name = string("input_121_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_121_strides_0 = const()[name = string("input_121_strides_0"), val = tensor([1, 1])]; + tensor input_121_dilations_0 = const()[name = string("input_121_dilations_0"), val = tensor([1, 1])]; + int32 input_121_groups_0 = const()[name = string("input_121_groups_0"), val = int32(1)]; + tensor const_42_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_42_quantized")]; + tensor const_43 = const()[name = string("const_43"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor input_123 = conv(bias = const_43, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_42_quantized, x = input_119)[name = string("input_123")]; + tensor input_125 = relu(x = input_123)[name = string("input_125")]; + string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("custom")]; + tensor input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor([1, 1])]; + tensor input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor([1, 1])]; + int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)]; + tensor const_44_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_44_quantized")]; + tensor const_45 = const()[name = string("const_45"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor out_19 = conv(bias = const_45, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_44_quantized, x = input_125)[name = string("out_19")]; + tensor input_129 = add(x = out_19, y = input_119)[name = string("input_129")]; + tensor input_131 = relu(x = input_129)[name = string("input_131")]; + string input_133_pad_type_0 = const()[name = string("input_133_pad_type_0"), val = string("custom")]; + tensor input_133_pad_0 = const()[name = string("input_133_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_133_strides_0 = const()[name = string("input_133_strides_0"), val = tensor([1, 1])]; + tensor input_133_dilations_0 = const()[name = string("input_133_dilations_0"), val = tensor([1, 1])]; + int32 input_133_groups_0 = const()[name = string("input_133_groups_0"), val = int32(1)]; + tensor const_46_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_46_quantized")]; + tensor const_47 = const()[name = string("const_47"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor input_135 = conv(bias = const_47, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_46_quantized, x = input_131)[name = string("input_135")]; + tensor input_137 = relu(x = input_135)[name = string("input_137")]; + string input_139_pad_type_0 = const()[name = string("input_139_pad_type_0"), val = string("custom")]; + tensor input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_139_strides_0 = const()[name = string("input_139_strides_0"), val = tensor([1, 1])]; + tensor input_139_dilations_0 = const()[name = string("input_139_dilations_0"), val = tensor([1, 1])]; + int32 input_139_groups_0 = const()[name = string("input_139_groups_0"), val = int32(1)]; + tensor const_48_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_48_quantized")]; + tensor const_49 = const()[name = string("const_49"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor out_21 = conv(bias = const_49, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_48_quantized, x = input_137)[name = string("out_21")]; + tensor input_141 = add(x = out_21, y = input_131)[name = string("input_141")]; + tensor input_143 = relu(x = input_141)[name = string("input_143")]; + string input_145_pad_type_0 = const()[name = string("input_145_pad_type_0"), val = string("custom")]; + tensor input_145_pad_0 = const()[name = string("input_145_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_145_strides_0 = const()[name = string("input_145_strides_0"), val = tensor([1, 1])]; + tensor input_145_dilations_0 = const()[name = string("input_145_dilations_0"), val = tensor([1, 1])]; + int32 input_145_groups_0 = const()[name = string("input_145_groups_0"), val = int32(1)]; + tensor const_50_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_50_quantized")]; + tensor const_51 = const()[name = string("const_51"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor input_147 = conv(bias = const_51, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_50_quantized, x = input_143)[name = string("input_147")]; + tensor input_149 = relu(x = input_147)[name = string("input_149")]; + string input_151_pad_type_0 = const()[name = string("input_151_pad_type_0"), val = string("custom")]; + tensor input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_151_strides_0 = const()[name = string("input_151_strides_0"), val = tensor([1, 1])]; + tensor input_151_dilations_0 = const()[name = string("input_151_dilations_0"), val = tensor([1, 1])]; + int32 input_151_groups_0 = const()[name = string("input_151_groups_0"), val = int32(1)]; + tensor const_52_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_52_quantized")]; + tensor const_53 = const()[name = string("const_53"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor out_23 = conv(bias = const_53, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_52_quantized, x = input_149)[name = string("out_23")]; + tensor input_153 = add(x = out_23, y = input_143)[name = string("input_153")]; + tensor input_155 = relu(x = input_153)[name = string("input_155")]; + string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("custom")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor([1, 1])]; + tensor input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor([1, 1])]; + int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)]; + tensor const_54_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_54_quantized")]; + tensor const_55 = const()[name = string("const_55"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor input_159 = conv(bias = const_55, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_54_quantized, x = input_155)[name = string("input_159")]; + tensor input_161 = relu(x = input_159)[name = string("input_161")]; + string input_163_pad_type_0 = const()[name = string("input_163_pad_type_0"), val = string("custom")]; + tensor input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_163_strides_0 = const()[name = string("input_163_strides_0"), val = tensor([1, 1])]; + tensor input_163_dilations_0 = const()[name = string("input_163_dilations_0"), val = tensor([1, 1])]; + int32 input_163_groups_0 = const()[name = string("input_163_groups_0"), val = int32(1)]; + tensor const_56_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_56_quantized")]; + tensor const_57 = const()[name = string("const_57"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor out_25 = conv(bias = const_57, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_56_quantized, x = input_161)[name = string("out_25")]; + tensor input_165 = add(x = out_25, y = input_155)[name = string("input_165")]; + tensor input_167 = relu(x = input_165)[name = string("input_167")]; + string input_169_pad_type_0 = const()[name = string("input_169_pad_type_0"), val = string("custom")]; + tensor input_169_pad_0 = const()[name = string("input_169_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_169_strides_0 = const()[name = string("input_169_strides_0"), val = tensor([2, 2])]; + tensor input_169_dilations_0 = const()[name = string("input_169_dilations_0"), val = tensor([1, 1])]; + int32 input_169_groups_0 = const()[name = string("input_169_groups_0"), val = int32(1)]; + tensor const_58_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_58_quantized")]; + tensor const_59 = const()[name = string("const_59"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor input_171 = conv(bias = const_59, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_58_quantized, x = input_167)[name = string("input_171")]; + tensor input_173 = relu(x = input_171)[name = string("input_173")]; + string input_175_pad_type_0 = const()[name = string("input_175_pad_type_0"), val = string("custom")]; + tensor input_175_pad_0 = const()[name = string("input_175_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_175_strides_0 = const()[name = string("input_175_strides_0"), val = tensor([1, 1])]; + tensor input_175_dilations_0 = const()[name = string("input_175_dilations_0"), val = tensor([1, 1])]; + int32 input_175_groups_0 = const()[name = string("input_175_groups_0"), val = int32(1)]; + tensor const_60_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_60_quantized")]; + tensor const_61 = const()[name = string("const_61"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor out_27 = conv(bias = const_61, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_60_quantized, x = input_173)[name = string("out_27")]; + string input_177_pad_type_0 = const()[name = string("input_177_pad_type_0"), val = string("valid")]; + tensor input_177_strides_0 = const()[name = string("input_177_strides_0"), val = tensor([2, 2])]; + tensor input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_177_dilations_0 = const()[name = string("input_177_dilations_0"), val = tensor([1, 1])]; + int32 input_177_groups_0 = const()[name = string("input_177_groups_0"), val = int32(1)]; + tensor const_62_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_62_quantized")]; + tensor const_63 = const()[name = string("const_63"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor var_537 = conv(bias = const_63, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_62_quantized, x = input_167)[name = string("op_537")]; + tensor input_179 = add(x = out_27, y = var_537)[name = string("input_179")]; + tensor input_181 = relu(x = input_179)[name = string("input_181")]; + string input_183_pad_type_0 = const()[name = string("input_183_pad_type_0"), val = string("custom")]; + tensor input_183_pad_0 = const()[name = string("input_183_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_183_strides_0 = const()[name = string("input_183_strides_0"), val = tensor([1, 1])]; + tensor input_183_dilations_0 = const()[name = string("input_183_dilations_0"), val = tensor([1, 1])]; + int32 input_183_groups_0 = const()[name = string("input_183_groups_0"), val = int32(1)]; + tensor const_64_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_64_quantized")]; + tensor const_65 = const()[name = string("const_65"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor input_185 = conv(bias = const_65, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_64_quantized, x = input_181)[name = string("input_185")]; + tensor input_187 = relu(x = input_185)[name = string("input_187")]; + string input_189_pad_type_0 = const()[name = string("input_189_pad_type_0"), val = string("custom")]; + tensor input_189_pad_0 = const()[name = string("input_189_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_189_strides_0 = const()[name = string("input_189_strides_0"), val = tensor([1, 1])]; + tensor input_189_dilations_0 = const()[name = string("input_189_dilations_0"), val = tensor([1, 1])]; + int32 input_189_groups_0 = const()[name = string("input_189_groups_0"), val = int32(1)]; + tensor const_66_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_66_quantized")]; + tensor const_67 = const()[name = string("const_67"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor out_29 = conv(bias = const_67, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_66_quantized, x = input_187)[name = string("out_29")]; + tensor input_191 = add(x = out_29, y = input_181)[name = string("input_191")]; + tensor input_193 = relu(x = input_191)[name = string("input_193")]; + string input_195_pad_type_0 = const()[name = string("input_195_pad_type_0"), val = string("custom")]; + tensor input_195_pad_0 = const()[name = string("input_195_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_195_strides_0 = const()[name = string("input_195_strides_0"), val = tensor([1, 1])]; + tensor input_195_dilations_0 = const()[name = string("input_195_dilations_0"), val = tensor([1, 1])]; + int32 input_195_groups_0 = const()[name = string("input_195_groups_0"), val = int32(1)]; + tensor const_68_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_68_quantized")]; + tensor const_69 = const()[name = string("const_69"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor input_197 = conv(bias = const_69, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_68_quantized, x = input_193)[name = string("input_197")]; + tensor input_199 = relu(x = input_197)[name = string("input_199")]; + string input_201_pad_type_0 = const()[name = string("input_201_pad_type_0"), val = string("custom")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_201_strides_0 = const()[name = string("input_201_strides_0"), val = tensor([1, 1])]; + tensor input_201_dilations_0 = const()[name = string("input_201_dilations_0"), val = tensor([1, 1])]; + int32 input_201_groups_0 = const()[name = string("input_201_groups_0"), val = int32(1)]; + tensor const_70_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_70_quantized")]; + tensor const_71 = const()[name = string("const_71"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor out = conv(bias = const_71, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_70_quantized, x = input_199)[name = string("out")]; + tensor input_203 = add(x = out, y = input_193)[name = string("input_203")]; + tensor frames = relu(x = input_203)[name = string("frames")]; + tensor concat_0x = const()[name = string("concat_0x"), val = tensor([-1, 2560, 125])]; + tensor sequences = reshape(shape = concat_0x, x = frames)[name = string("sequences")]; + tensor input_205_axes_0 = const()[name = string("input_205_axes_0"), val = tensor([1])]; + tensor input_205 = expand_dims(axes = input_205_axes_0, x = weights)[name = string("input_205")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_205)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor([3])]; + tensor weights_1 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")]; + tensor weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor([2])]; + bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)]; + tensor weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_1)[name = string("weight_sum")]; + fp32 var_627 = const()[name = string("op_627"), val = fp32(0x0p+0)]; + tensor var_628 = greater(x = weight_sum, y = var_627)[name = string("op_628")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_628)[name = string("safe_sum")]; + tensor var_636 = mul(x = sequences, y = weights_1)[name = string("op_636")]; + tensor var_641_axes_0 = const()[name = string("op_641_axes_0"), val = tensor([2])]; + bool var_641_keep_dims_0 = const()[name = string("op_641_keep_dims_0"), val = bool(false)]; + tensor var_641 = reduce_sum(axes = var_641_axes_0, keep_dims = var_641_keep_dims_0, x = var_636)[name = string("op_641")]; + tensor mean = real_div(x = var_641, y = safe_sum)[name = string("mean")]; + tensor var_644_axes_0 = const()[name = string("op_644_axes_0"), val = tensor([2])]; + tensor var_644 = expand_dims(axes = var_644_axes_0, x = mean)[name = string("op_644")]; + tensor var_646 = sub(x = sequences, y = var_644)[name = string("op_646")]; + tensor dx2 = mul(x = var_646, y = var_646)[name = string("dx2")]; + tensor var_648 = mul(x = weights_1, y = weights_1)[name = string("op_648")]; + tensor weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor([2])]; + bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)]; + tensor weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_648)[name = string("weight_sq_sum")]; + tensor var_654 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_654")]; + tensor var_656 = sub(x = safe_sum, y = var_654)[name = string("op_656")]; + fp32 var_658 = const()[name = string("op_658"), val = fp32(0x1.5798eep-27)]; + tensor denom = add(x = var_656, y = var_658)[name = string("denom")]; + tensor var_660 = mul(x = dx2, y = weights_1)[name = string("op_660")]; + tensor var_665_axes_0 = const()[name = string("op_665_axes_0"), val = tensor([2])]; + bool var_665_keep_dims_0 = const()[name = string("op_665_keep_dims_0"), val = bool(false)]; + tensor var_665 = reduce_sum(axes = var_665_axes_0, keep_dims = var_665_keep_dims_0, x = var_660)[name = string("op_665")]; + tensor var = real_div(x = var_665, y = denom)[name = string("var")]; + fp32 var_667 = const()[name = string("op_667"), val = fp32(0x1.b7cdfep-34)]; + tensor var_668 = maximum(x = var, y = var_667)[name = string("op_668")]; + tensor std = sqrt(x = var_668)[name = string("std")]; + int32 var_671 = const()[name = string("op_671"), val = int32(-1)]; + bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)]; + tensor stats = concat(axis = var_671, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")]; + tensor var_678 = sub(x = mean, y = mean)[name = string("sub_0")]; + fp32 var_685_value_0 = const()[name = string("op_685_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor var_685 = fill_like(ref_tensor = std, value = var_685_value_0)[name = string("op_685")]; + int32 var_687 = const()[name = string("op_687"), val = int32(-1)]; + bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)]; + tensor zero_stats = concat(axis = var_687, interleave = zero_stats_interleave_0, values = (var_678, var_685))[name = string("zero_stats")]; + fp32 var_689 = const()[name = string("op_689"), val = fp32(0x0p+0)]; + tensor var_690 = less_equal(x = weight_sum, y = var_689)[name = string("op_690")]; + tensor var_696 = const()[name = string("op_696"), val = tensor([1, 5120])]; + tensor zero_mask = tile(reps = var_696, x = var_690)[name = string("zero_mask")]; + tensor input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")]; + tensor output = linear(bias = resnet_seg_1_bias, weight = resnet_seg_1_weight_quantized, x = input)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/weights/weight.bin b/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..3114ce354da5e5fe51316189c5c49b584a0ff08f --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-b3-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed5873a1aeef9493f6ca8659ff3b75b7673a6b1d450e8973d165d2913181013a +size 6675328 diff --git a/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..988ce7018e2872e6116dc111c94fad2e79d2bb4b --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f137a77cbeb0ad73acb52c3abd3e2ec7d077807afbb3bc50c3647e49ffba53d9 +size 243 diff --git a/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/coremldata.bin b/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..7368358583a65689f082ce0b87a68fbd2cc53428 --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:faca12fea014822a2842e0945d41613309b2315a832efa3734247d71361354ea +size 443 diff --git a/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/model.mil b/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..b3513e9f08dfd798bed3c079fa4e7853710ded6f --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/model.mil @@ -0,0 +1,408 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor weights) [FlexibleShapeInformation = tuple>>, tuple>>>>((("DefaultShapes", {{"fbank", [32, 998, 80]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"316ab78f", {{"fbank", [3, 998, 80]}, {"weights", [3, 589]}}}, {"f6770b54", {{"fbank", [1, 998, 80]}, {"weights", [1, 589]}}}, {"fd0b6e18", {{"fbank", [32, 998, 80]}, {"weights", [32, 589]}}}})))] { + tensor resnet_seg_1_bias = const()[name = string("resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936))))[name = string("resnet_seg_1_weight_quantized")]; + tensor var_20 = const()[name = string("op_20"), val = tensor([0, 2, 1])]; + tensor input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor([1])]; + tensor fbank_1 = transpose(perm = var_20, x = fbank)[name = string("transpose_0")]; + tensor input_1 = expand_dims(axes = input_1_axes_0, x = fbank_1)[name = string("input_1")]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor([1, 1])]; + tensor input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor([1, 1])]; + int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; + tensor const_0 = const()[name = string("const_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_1 = const()[name = string("const_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor input_5 = conv(bias = const_1, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_0, x = input_1)[name = string("input_5")]; + tensor input_7 = relu(x = input_5)[name = string("input_7")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1, 1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor const_2_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_2_quantized")]; + tensor const_3 = const()[name = string("const_3"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor input_11 = conv(bias = const_3, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_2_quantized, x = input_7)[name = string("input_11")]; + tensor input_13 = relu(x = input_11)[name = string("input_13")]; + string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")]; + tensor input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor([1, 1])]; + int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)]; + tensor const_4_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_4_quantized")]; + tensor const_5 = const()[name = string("const_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor out_1 = conv(bias = const_5, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_4_quantized, x = input_13)[name = string("out_1")]; + tensor input_17 = add(x = out_1, y = input_7)[name = string("input_17")]; + tensor input_19 = relu(x = input_17)[name = string("input_19")]; + string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")]; + tensor input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor([1, 1])]; + tensor input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor([1, 1])]; + int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)]; + tensor const_6_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_6_quantized")]; + tensor const_7 = const()[name = string("const_7"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor input_23 = conv(bias = const_7, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_6_quantized, x = input_19)[name = string("input_23")]; + tensor input_25 = relu(x = input_23)[name = string("input_25")]; + string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")]; + tensor input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor([1, 1])]; + tensor input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor([1, 1])]; + int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)]; + tensor const_8_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_8_quantized")]; + tensor const_9 = const()[name = string("const_9"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor out_3 = conv(bias = const_9, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_8_quantized, x = input_25)[name = string("out_3")]; + tensor input_29 = add(x = out_3, y = input_19)[name = string("input_29")]; + tensor input_31 = relu(x = input_29)[name = string("input_31")]; + string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")]; + tensor input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor([1, 1])]; + tensor input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor([1, 1])]; + int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)]; + tensor const_10_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_10_quantized")]; + tensor const_11 = const()[name = string("const_11"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor input_35 = conv(bias = const_11, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_10_quantized, x = input_31)[name = string("input_35")]; + tensor input_37 = relu(x = input_35)[name = string("input_37")]; + string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("custom")]; + tensor input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor([1, 1])]; + int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)]; + tensor const_12_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_12_quantized")]; + tensor const_13 = const()[name = string("const_13"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor out_5 = conv(bias = const_13, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_12_quantized, x = input_37)[name = string("out_5")]; + tensor input_41 = add(x = out_5, y = input_31)[name = string("input_41")]; + tensor input_43 = relu(x = input_41)[name = string("input_43")]; + string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")]; + tensor input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor([2, 2])]; + tensor input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor([1, 1])]; + int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)]; + tensor const_14_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_14_quantized")]; + tensor const_15 = const()[name = string("const_15"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor input_47 = conv(bias = const_15, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_14_quantized, x = input_43)[name = string("input_47")]; + tensor input_49 = relu(x = input_47)[name = string("input_49")]; + string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")]; + tensor input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor([1, 1])]; + tensor input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor([1, 1])]; + int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)]; + tensor const_16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_16_quantized")]; + tensor const_17 = const()[name = string("const_17"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor out_7 = conv(bias = const_17, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_16_quantized, x = input_49)[name = string("out_7")]; + string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")]; + tensor input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor([2, 2])]; + tensor input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor([1, 1])]; + int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)]; + tensor const_18 = const()[name = string("const_18"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_19 = const()[name = string("const_19"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor var_194 = conv(bias = const_19, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_18, x = input_43)[name = string("op_194")]; + tensor input_55 = add(x = out_7, y = var_194)[name = string("input_55")]; + tensor input_57 = relu(x = input_55)[name = string("input_57")]; + string input_59_pad_type_0 = const()[name = string("input_59_pad_type_0"), val = string("custom")]; + tensor input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_59_strides_0 = const()[name = string("input_59_strides_0"), val = tensor([1, 1])]; + tensor input_59_dilations_0 = const()[name = string("input_59_dilations_0"), val = tensor([1, 1])]; + int32 input_59_groups_0 = const()[name = string("input_59_groups_0"), val = int32(1)]; + tensor const_20_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_20_quantized")]; + tensor const_21 = const()[name = string("const_21"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor input_61 = conv(bias = const_21, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_20_quantized, x = input_57)[name = string("input_61")]; + tensor input_63 = relu(x = input_61)[name = string("input_63")]; + string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")]; + tensor input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor([1, 1])]; + tensor input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor([1, 1])]; + int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)]; + tensor const_22_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_22_quantized")]; + tensor const_23 = const()[name = string("const_23"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor out_9 = conv(bias = const_23, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_22_quantized, x = input_63)[name = string("out_9")]; + tensor input_67 = add(x = out_9, y = input_57)[name = string("input_67")]; + tensor input_69 = relu(x = input_67)[name = string("input_69")]; + string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")]; + tensor input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor([1, 1])]; + tensor input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor([1, 1])]; + int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)]; + tensor const_24_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_24_quantized")]; + tensor const_25 = const()[name = string("const_25"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor input_73 = conv(bias = const_25, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_24_quantized, x = input_69)[name = string("input_73")]; + tensor input_75 = relu(x = input_73)[name = string("input_75")]; + string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")]; + tensor input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor([1, 1])]; + tensor input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor([1, 1])]; + int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)]; + tensor const_26_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_26_quantized")]; + tensor const_27 = const()[name = string("const_27"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor out_11 = conv(bias = const_27, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_26_quantized, x = input_75)[name = string("out_11")]; + tensor input_79 = add(x = out_11, y = input_69)[name = string("input_79")]; + tensor input_81 = relu(x = input_79)[name = string("input_81")]; + string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")]; + tensor input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor([1, 1])]; + tensor input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor([1, 1])]; + int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(1)]; + tensor const_28_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_28_quantized")]; + tensor const_29 = const()[name = string("const_29"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor input_85 = conv(bias = const_29, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_28_quantized, x = input_81)[name = string("input_85")]; + tensor input_87 = relu(x = input_85)[name = string("input_87")]; + string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("custom")]; + tensor input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor([1, 1])]; + tensor input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor([1, 1])]; + int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)]; + tensor const_30_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_30_quantized")]; + tensor const_31 = const()[name = string("const_31"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor out_13 = conv(bias = const_31, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_30_quantized, x = input_87)[name = string("out_13")]; + tensor input_91 = add(x = out_13, y = input_81)[name = string("input_91")]; + tensor input_93 = relu(x = input_91)[name = string("input_93")]; + string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("custom")]; + tensor input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor([2, 2])]; + tensor input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor([1, 1])]; + int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)]; + tensor const_32_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_32_quantized")]; + tensor const_33 = const()[name = string("const_33"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor input_97 = conv(bias = const_33, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_32_quantized, x = input_93)[name = string("input_97")]; + tensor input_99 = relu(x = input_97)[name = string("input_99")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("custom")]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1, 1])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1, 1])]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)]; + tensor const_34_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_34_quantized")]; + tensor const_35 = const()[name = string("const_35"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor out_15 = conv(bias = const_35, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_34_quantized, x = input_99)[name = string("out_15")]; + string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")]; + tensor input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor([2, 2])]; + tensor input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor([1, 1])]; + int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; + tensor const_36_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_36_quantized")]; + tensor const_37 = const()[name = string("const_37"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor var_338 = conv(bias = const_37, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_36_quantized, x = input_93)[name = string("op_338")]; + tensor input_105 = add(x = out_15, y = var_338)[name = string("input_105")]; + tensor input_107 = relu(x = input_105)[name = string("input_107")]; + string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("custom")]; + tensor input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor([1, 1])]; + tensor input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor([1, 1])]; + int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)]; + tensor const_38_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_38_quantized")]; + tensor const_39 = const()[name = string("const_39"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor input_111 = conv(bias = const_39, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_38_quantized, x = input_107)[name = string("input_111")]; + tensor input_113 = relu(x = input_111)[name = string("input_113")]; + string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("custom")]; + tensor input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_115_strides_0 = const()[name = string("input_115_strides_0"), val = tensor([1, 1])]; + tensor input_115_dilations_0 = const()[name = string("input_115_dilations_0"), val = tensor([1, 1])]; + int32 input_115_groups_0 = const()[name = string("input_115_groups_0"), val = int32(1)]; + tensor const_40_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_40_quantized")]; + tensor const_41 = const()[name = string("const_41"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor out_17 = conv(bias = const_41, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_40_quantized, x = input_113)[name = string("out_17")]; + tensor input_117 = add(x = out_17, y = input_107)[name = string("input_117")]; + tensor input_119 = relu(x = input_117)[name = string("input_119")]; + string input_121_pad_type_0 = const()[name = string("input_121_pad_type_0"), val = string("custom")]; + tensor input_121_pad_0 = const()[name = string("input_121_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_121_strides_0 = const()[name = string("input_121_strides_0"), val = tensor([1, 1])]; + tensor input_121_dilations_0 = const()[name = string("input_121_dilations_0"), val = tensor([1, 1])]; + int32 input_121_groups_0 = const()[name = string("input_121_groups_0"), val = int32(1)]; + tensor const_42_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_42_quantized")]; + tensor const_43 = const()[name = string("const_43"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor input_123 = conv(bias = const_43, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_42_quantized, x = input_119)[name = string("input_123")]; + tensor input_125 = relu(x = input_123)[name = string("input_125")]; + string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("custom")]; + tensor input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor([1, 1])]; + tensor input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor([1, 1])]; + int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)]; + tensor const_44_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_44_quantized")]; + tensor const_45 = const()[name = string("const_45"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor out_19 = conv(bias = const_45, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_44_quantized, x = input_125)[name = string("out_19")]; + tensor input_129 = add(x = out_19, y = input_119)[name = string("input_129")]; + tensor input_131 = relu(x = input_129)[name = string("input_131")]; + string input_133_pad_type_0 = const()[name = string("input_133_pad_type_0"), val = string("custom")]; + tensor input_133_pad_0 = const()[name = string("input_133_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_133_strides_0 = const()[name = string("input_133_strides_0"), val = tensor([1, 1])]; + tensor input_133_dilations_0 = const()[name = string("input_133_dilations_0"), val = tensor([1, 1])]; + int32 input_133_groups_0 = const()[name = string("input_133_groups_0"), val = int32(1)]; + tensor const_46_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_46_quantized")]; + tensor const_47 = const()[name = string("const_47"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor input_135 = conv(bias = const_47, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_46_quantized, x = input_131)[name = string("input_135")]; + tensor input_137 = relu(x = input_135)[name = string("input_137")]; + string input_139_pad_type_0 = const()[name = string("input_139_pad_type_0"), val = string("custom")]; + tensor input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_139_strides_0 = const()[name = string("input_139_strides_0"), val = tensor([1, 1])]; + tensor input_139_dilations_0 = const()[name = string("input_139_dilations_0"), val = tensor([1, 1])]; + int32 input_139_groups_0 = const()[name = string("input_139_groups_0"), val = int32(1)]; + tensor const_48_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_48_quantized")]; + tensor const_49 = const()[name = string("const_49"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor out_21 = conv(bias = const_49, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_48_quantized, x = input_137)[name = string("out_21")]; + tensor input_141 = add(x = out_21, y = input_131)[name = string("input_141")]; + tensor input_143 = relu(x = input_141)[name = string("input_143")]; + string input_145_pad_type_0 = const()[name = string("input_145_pad_type_0"), val = string("custom")]; + tensor input_145_pad_0 = const()[name = string("input_145_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_145_strides_0 = const()[name = string("input_145_strides_0"), val = tensor([1, 1])]; + tensor input_145_dilations_0 = const()[name = string("input_145_dilations_0"), val = tensor([1, 1])]; + int32 input_145_groups_0 = const()[name = string("input_145_groups_0"), val = int32(1)]; + tensor const_50_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_50_quantized")]; + tensor const_51 = const()[name = string("const_51"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor input_147 = conv(bias = const_51, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_50_quantized, x = input_143)[name = string("input_147")]; + tensor input_149 = relu(x = input_147)[name = string("input_149")]; + string input_151_pad_type_0 = const()[name = string("input_151_pad_type_0"), val = string("custom")]; + tensor input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_151_strides_0 = const()[name = string("input_151_strides_0"), val = tensor([1, 1])]; + tensor input_151_dilations_0 = const()[name = string("input_151_dilations_0"), val = tensor([1, 1])]; + int32 input_151_groups_0 = const()[name = string("input_151_groups_0"), val = int32(1)]; + tensor const_52_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_52_quantized")]; + tensor const_53 = const()[name = string("const_53"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor out_23 = conv(bias = const_53, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_52_quantized, x = input_149)[name = string("out_23")]; + tensor input_153 = add(x = out_23, y = input_143)[name = string("input_153")]; + tensor input_155 = relu(x = input_153)[name = string("input_155")]; + string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("custom")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor([1, 1])]; + tensor input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor([1, 1])]; + int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)]; + tensor const_54_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_54_quantized")]; + tensor const_55 = const()[name = string("const_55"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor input_159 = conv(bias = const_55, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_54_quantized, x = input_155)[name = string("input_159")]; + tensor input_161 = relu(x = input_159)[name = string("input_161")]; + string input_163_pad_type_0 = const()[name = string("input_163_pad_type_0"), val = string("custom")]; + tensor input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_163_strides_0 = const()[name = string("input_163_strides_0"), val = tensor([1, 1])]; + tensor input_163_dilations_0 = const()[name = string("input_163_dilations_0"), val = tensor([1, 1])]; + int32 input_163_groups_0 = const()[name = string("input_163_groups_0"), val = int32(1)]; + tensor const_56_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_56_quantized")]; + tensor const_57 = const()[name = string("const_57"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor out_25 = conv(bias = const_57, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_56_quantized, x = input_161)[name = string("out_25")]; + tensor input_165 = add(x = out_25, y = input_155)[name = string("input_165")]; + tensor input_167 = relu(x = input_165)[name = string("input_167")]; + string input_169_pad_type_0 = const()[name = string("input_169_pad_type_0"), val = string("custom")]; + tensor input_169_pad_0 = const()[name = string("input_169_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_169_strides_0 = const()[name = string("input_169_strides_0"), val = tensor([2, 2])]; + tensor input_169_dilations_0 = const()[name = string("input_169_dilations_0"), val = tensor([1, 1])]; + int32 input_169_groups_0 = const()[name = string("input_169_groups_0"), val = int32(1)]; + tensor const_58_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_58_quantized")]; + tensor const_59 = const()[name = string("const_59"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor input_171 = conv(bias = const_59, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_58_quantized, x = input_167)[name = string("input_171")]; + tensor input_173 = relu(x = input_171)[name = string("input_173")]; + string input_175_pad_type_0 = const()[name = string("input_175_pad_type_0"), val = string("custom")]; + tensor input_175_pad_0 = const()[name = string("input_175_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_175_strides_0 = const()[name = string("input_175_strides_0"), val = tensor([1, 1])]; + tensor input_175_dilations_0 = const()[name = string("input_175_dilations_0"), val = tensor([1, 1])]; + int32 input_175_groups_0 = const()[name = string("input_175_groups_0"), val = int32(1)]; + tensor const_60_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_60_quantized")]; + tensor const_61 = const()[name = string("const_61"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor out_27 = conv(bias = const_61, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_60_quantized, x = input_173)[name = string("out_27")]; + string input_177_pad_type_0 = const()[name = string("input_177_pad_type_0"), val = string("valid")]; + tensor input_177_strides_0 = const()[name = string("input_177_strides_0"), val = tensor([2, 2])]; + tensor input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_177_dilations_0 = const()[name = string("input_177_dilations_0"), val = tensor([1, 1])]; + int32 input_177_groups_0 = const()[name = string("input_177_groups_0"), val = int32(1)]; + tensor const_62_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_62_quantized")]; + tensor const_63 = const()[name = string("const_63"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor var_537 = conv(bias = const_63, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_62_quantized, x = input_167)[name = string("op_537")]; + tensor input_179 = add(x = out_27, y = var_537)[name = string("input_179")]; + tensor input_181 = relu(x = input_179)[name = string("input_181")]; + string input_183_pad_type_0 = const()[name = string("input_183_pad_type_0"), val = string("custom")]; + tensor input_183_pad_0 = const()[name = string("input_183_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_183_strides_0 = const()[name = string("input_183_strides_0"), val = tensor([1, 1])]; + tensor input_183_dilations_0 = const()[name = string("input_183_dilations_0"), val = tensor([1, 1])]; + int32 input_183_groups_0 = const()[name = string("input_183_groups_0"), val = int32(1)]; + tensor const_64_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_64_quantized")]; + tensor const_65 = const()[name = string("const_65"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor input_185 = conv(bias = const_65, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_64_quantized, x = input_181)[name = string("input_185")]; + tensor input_187 = relu(x = input_185)[name = string("input_187")]; + string input_189_pad_type_0 = const()[name = string("input_189_pad_type_0"), val = string("custom")]; + tensor input_189_pad_0 = const()[name = string("input_189_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_189_strides_0 = const()[name = string("input_189_strides_0"), val = tensor([1, 1])]; + tensor input_189_dilations_0 = const()[name = string("input_189_dilations_0"), val = tensor([1, 1])]; + int32 input_189_groups_0 = const()[name = string("input_189_groups_0"), val = int32(1)]; + tensor const_66_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_66_quantized")]; + tensor const_67 = const()[name = string("const_67"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor out_29 = conv(bias = const_67, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_66_quantized, x = input_187)[name = string("out_29")]; + tensor input_191 = add(x = out_29, y = input_181)[name = string("input_191")]; + tensor input_193 = relu(x = input_191)[name = string("input_193")]; + string input_195_pad_type_0 = const()[name = string("input_195_pad_type_0"), val = string("custom")]; + tensor input_195_pad_0 = const()[name = string("input_195_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_195_strides_0 = const()[name = string("input_195_strides_0"), val = tensor([1, 1])]; + tensor input_195_dilations_0 = const()[name = string("input_195_dilations_0"), val = tensor([1, 1])]; + int32 input_195_groups_0 = const()[name = string("input_195_groups_0"), val = int32(1)]; + tensor const_68_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_68_quantized")]; + tensor const_69 = const()[name = string("const_69"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor input_197 = conv(bias = const_69, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_68_quantized, x = input_193)[name = string("input_197")]; + tensor input_199 = relu(x = input_197)[name = string("input_199")]; + string input_201_pad_type_0 = const()[name = string("input_201_pad_type_0"), val = string("custom")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_201_strides_0 = const()[name = string("input_201_strides_0"), val = tensor([1, 1])]; + tensor input_201_dilations_0 = const()[name = string("input_201_dilations_0"), val = tensor([1, 1])]; + int32 input_201_groups_0 = const()[name = string("input_201_groups_0"), val = int32(1)]; + tensor const_70_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_70_quantized")]; + tensor const_71 = const()[name = string("const_71"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor out = conv(bias = const_71, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_70_quantized, x = input_199)[name = string("out")]; + tensor input_203 = add(x = out, y = input_193)[name = string("input_203")]; + tensor frames = relu(x = input_203)[name = string("frames")]; + tensor concat_0x = const()[name = string("concat_0x"), val = tensor([-1, 2560, 125])]; + tensor sequences = reshape(shape = concat_0x, x = frames)[name = string("sequences")]; + tensor input_205_axes_0 = const()[name = string("input_205_axes_0"), val = tensor([1])]; + tensor input_205 = expand_dims(axes = input_205_axes_0, x = weights)[name = string("input_205")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_205)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor([3])]; + tensor weights_1 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")]; + tensor weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor([2])]; + bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)]; + tensor weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_1)[name = string("weight_sum")]; + fp32 var_627 = const()[name = string("op_627"), val = fp32(0x0p+0)]; + tensor var_628 = greater(x = weight_sum, y = var_627)[name = string("op_628")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_628)[name = string("safe_sum")]; + tensor var_636 = mul(x = sequences, y = weights_1)[name = string("op_636")]; + tensor var_641_axes_0 = const()[name = string("op_641_axes_0"), val = tensor([2])]; + bool var_641_keep_dims_0 = const()[name = string("op_641_keep_dims_0"), val = bool(false)]; + tensor var_641 = reduce_sum(axes = var_641_axes_0, keep_dims = var_641_keep_dims_0, x = var_636)[name = string("op_641")]; + tensor mean = real_div(x = var_641, y = safe_sum)[name = string("mean")]; + tensor var_644_axes_0 = const()[name = string("op_644_axes_0"), val = tensor([2])]; + tensor var_644 = expand_dims(axes = var_644_axes_0, x = mean)[name = string("op_644")]; + tensor var_646 = sub(x = sequences, y = var_644)[name = string("op_646")]; + tensor dx2 = mul(x = var_646, y = var_646)[name = string("dx2")]; + tensor var_648 = mul(x = weights_1, y = weights_1)[name = string("op_648")]; + tensor weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor([2])]; + bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)]; + tensor weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_648)[name = string("weight_sq_sum")]; + tensor var_654 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_654")]; + tensor var_656 = sub(x = safe_sum, y = var_654)[name = string("op_656")]; + fp32 var_658 = const()[name = string("op_658"), val = fp32(0x1.5798eep-27)]; + tensor denom = add(x = var_656, y = var_658)[name = string("denom")]; + tensor var_660 = mul(x = dx2, y = weights_1)[name = string("op_660")]; + tensor var_665_axes_0 = const()[name = string("op_665_axes_0"), val = tensor([2])]; + bool var_665_keep_dims_0 = const()[name = string("op_665_keep_dims_0"), val = bool(false)]; + tensor var_665 = reduce_sum(axes = var_665_axes_0, keep_dims = var_665_keep_dims_0, x = var_660)[name = string("op_665")]; + tensor var = real_div(x = var_665, y = denom)[name = string("var")]; + fp32 var_667 = const()[name = string("op_667"), val = fp32(0x1.b7cdfep-34)]; + tensor var_668 = maximum(x = var, y = var_667)[name = string("op_668")]; + tensor std = sqrt(x = var_668)[name = string("std")]; + int32 var_671 = const()[name = string("op_671"), val = int32(-1)]; + bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)]; + tensor stats = concat(axis = var_671, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")]; + tensor var_678 = sub(x = mean, y = mean)[name = string("sub_0")]; + fp32 var_685_value_0 = const()[name = string("op_685_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor var_685 = fill_like(ref_tensor = std, value = var_685_value_0)[name = string("op_685")]; + int32 var_687 = const()[name = string("op_687"), val = int32(-1)]; + bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)]; + tensor zero_stats = concat(axis = var_687, interleave = zero_stats_interleave_0, values = (var_678, var_685))[name = string("zero_stats")]; + fp32 var_689 = const()[name = string("op_689"), val = fp32(0x0p+0)]; + tensor var_690 = less_equal(x = weight_sum, y = var_689)[name = string("op_690")]; + tensor var_696 = const()[name = string("op_696"), val = tensor([1, 5120])]; + tensor zero_mask = tile(reps = var_696, x = var_690)[name = string("zero_mask")]; + tensor input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")]; + tensor output = linear(bias = resnet_seg_1_bias, weight = resnet_seg_1_weight_quantized, x = input)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/weights/weight.bin b/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..3114ce354da5e5fe51316189c5c49b584a0ff08f --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-b32-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed5873a1aeef9493f6ca8659ff3b75b7673a6b1d450e8973d165d2913181013a +size 6675328 diff --git a/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/analytics/coremldata.bin b/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..988ce7018e2872e6116dc111c94fad2e79d2bb4b --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f137a77cbeb0ad73acb52c3abd3e2ec7d077807afbb3bc50c3647e49ffba53d9 +size 243 diff --git a/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/coremldata.bin b/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..7368358583a65689f082ce0b87a68fbd2cc53428 --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:faca12fea014822a2842e0945d41613309b2315a832efa3734247d71361354ea +size 443 diff --git a/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/model.mil b/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..b3513e9f08dfd798bed3c079fa4e7853710ded6f --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/model.mil @@ -0,0 +1,408 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})] +{ + func main(tensor fbank, tensor weights) [FlexibleShapeInformation = tuple>>, tuple>>>>((("DefaultShapes", {{"fbank", [32, 998, 80]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"316ab78f", {{"fbank", [3, 998, 80]}, {"weights", [3, 589]}}}, {"f6770b54", {{"fbank", [1, 998, 80]}, {"weights", [1, 589]}}}, {"fd0b6e18", {{"fbank", [32, 998, 80]}, {"weights", [32, 589]}}}})))] { + tensor resnet_seg_1_bias = const()[name = string("resnet_seg_1_bias"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936))))[name = string("resnet_seg_1_weight_quantized")]; + tensor var_20 = const()[name = string("op_20"), val = tensor([0, 2, 1])]; + tensor input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor([1])]; + tensor fbank_1 = transpose(perm = var_20, x = fbank)[name = string("transpose_0")]; + tensor input_1 = expand_dims(axes = input_1_axes_0, x = fbank_1)[name = string("input_1")]; + string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; + tensor input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor([1, 1])]; + tensor input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor([1, 1])]; + int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; + tensor const_0 = const()[name = string("const_0"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))]; + tensor const_1 = const()[name = string("const_1"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))]; + tensor input_5 = conv(bias = const_1, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_0, x = input_1)[name = string("input_5")]; + tensor input_7 = relu(x = input_5)[name = string("input_7")]; + string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")]; + tensor input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor([1, 1])]; + int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; + tensor const_2_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_2_quantized")]; + tensor const_3 = const()[name = string("const_3"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))]; + tensor input_11 = conv(bias = const_3, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_2_quantized, x = input_7)[name = string("input_11")]; + tensor input_13 = relu(x = input_11)[name = string("input_13")]; + string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")]; + tensor input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor([1, 1])]; + int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)]; + tensor const_4_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_4_quantized")]; + tensor const_5 = const()[name = string("const_5"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))]; + tensor out_1 = conv(bias = const_5, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_4_quantized, x = input_13)[name = string("out_1")]; + tensor input_17 = add(x = out_1, y = input_7)[name = string("input_17")]; + tensor input_19 = relu(x = input_17)[name = string("input_19")]; + string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")]; + tensor input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor([1, 1])]; + tensor input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor([1, 1])]; + int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)]; + tensor const_6_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_6_quantized")]; + tensor const_7 = const()[name = string("const_7"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))]; + tensor input_23 = conv(bias = const_7, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_6_quantized, x = input_19)[name = string("input_23")]; + tensor input_25 = relu(x = input_23)[name = string("input_25")]; + string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")]; + tensor input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor([1, 1])]; + tensor input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor([1, 1])]; + int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)]; + tensor const_8_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_8_quantized")]; + tensor const_9 = const()[name = string("const_9"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))]; + tensor out_3 = conv(bias = const_9, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_8_quantized, x = input_25)[name = string("out_3")]; + tensor input_29 = add(x = out_3, y = input_19)[name = string("input_29")]; + tensor input_31 = relu(x = input_29)[name = string("input_31")]; + string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")]; + tensor input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor([1, 1])]; + tensor input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor([1, 1])]; + int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)]; + tensor const_10_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_10_quantized")]; + tensor const_11 = const()[name = string("const_11"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))]; + tensor input_35 = conv(bias = const_11, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_10_quantized, x = input_31)[name = string("input_35")]; + tensor input_37 = relu(x = input_35)[name = string("input_37")]; + string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("custom")]; + tensor input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor([1, 1])]; + int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)]; + tensor const_12_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_12_quantized")]; + tensor const_13 = const()[name = string("const_13"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))]; + tensor out_5 = conv(bias = const_13, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_12_quantized, x = input_37)[name = string("out_5")]; + tensor input_41 = add(x = out_5, y = input_31)[name = string("input_41")]; + tensor input_43 = relu(x = input_41)[name = string("input_43")]; + string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")]; + tensor input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor([2, 2])]; + tensor input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor([1, 1])]; + int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)]; + tensor const_14_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_14_quantized")]; + tensor const_15 = const()[name = string("const_15"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))]; + tensor input_47 = conv(bias = const_15, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_14_quantized, x = input_43)[name = string("input_47")]; + tensor input_49 = relu(x = input_47)[name = string("input_49")]; + string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")]; + tensor input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor([1, 1])]; + tensor input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor([1, 1])]; + int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)]; + tensor const_16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_16_quantized")]; + tensor const_17 = const()[name = string("const_17"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))]; + tensor out_7 = conv(bias = const_17, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_16_quantized, x = input_49)[name = string("out_7")]; + string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")]; + tensor input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor([2, 2])]; + tensor input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor([1, 1])]; + int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)]; + tensor const_18 = const()[name = string("const_18"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))]; + tensor const_19 = const()[name = string("const_19"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))]; + tensor var_194 = conv(bias = const_19, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_18, x = input_43)[name = string("op_194")]; + tensor input_55 = add(x = out_7, y = var_194)[name = string("input_55")]; + tensor input_57 = relu(x = input_55)[name = string("input_57")]; + string input_59_pad_type_0 = const()[name = string("input_59_pad_type_0"), val = string("custom")]; + tensor input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_59_strides_0 = const()[name = string("input_59_strides_0"), val = tensor([1, 1])]; + tensor input_59_dilations_0 = const()[name = string("input_59_dilations_0"), val = tensor([1, 1])]; + int32 input_59_groups_0 = const()[name = string("input_59_groups_0"), val = int32(1)]; + tensor const_20_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_20_quantized")]; + tensor const_21 = const()[name = string("const_21"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))]; + tensor input_61 = conv(bias = const_21, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_20_quantized, x = input_57)[name = string("input_61")]; + tensor input_63 = relu(x = input_61)[name = string("input_63")]; + string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")]; + tensor input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor([1, 1])]; + tensor input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor([1, 1])]; + int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)]; + tensor const_22_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_22_quantized")]; + tensor const_23 = const()[name = string("const_23"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))]; + tensor out_9 = conv(bias = const_23, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_22_quantized, x = input_63)[name = string("out_9")]; + tensor input_67 = add(x = out_9, y = input_57)[name = string("input_67")]; + tensor input_69 = relu(x = input_67)[name = string("input_69")]; + string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")]; + tensor input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor([1, 1])]; + tensor input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor([1, 1])]; + int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)]; + tensor const_24_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_24_quantized")]; + tensor const_25 = const()[name = string("const_25"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))]; + tensor input_73 = conv(bias = const_25, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_24_quantized, x = input_69)[name = string("input_73")]; + tensor input_75 = relu(x = input_73)[name = string("input_75")]; + string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")]; + tensor input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor([1, 1])]; + tensor input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor([1, 1])]; + int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)]; + tensor const_26_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_26_quantized")]; + tensor const_27 = const()[name = string("const_27"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))]; + tensor out_11 = conv(bias = const_27, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_26_quantized, x = input_75)[name = string("out_11")]; + tensor input_79 = add(x = out_11, y = input_69)[name = string("input_79")]; + tensor input_81 = relu(x = input_79)[name = string("input_81")]; + string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")]; + tensor input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor([1, 1])]; + tensor input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor([1, 1])]; + int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(1)]; + tensor const_28_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_28_quantized")]; + tensor const_29 = const()[name = string("const_29"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))]; + tensor input_85 = conv(bias = const_29, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_28_quantized, x = input_81)[name = string("input_85")]; + tensor input_87 = relu(x = input_85)[name = string("input_87")]; + string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("custom")]; + tensor input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor([1, 1])]; + tensor input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor([1, 1])]; + int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)]; + tensor const_30_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_30_quantized")]; + tensor const_31 = const()[name = string("const_31"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))]; + tensor out_13 = conv(bias = const_31, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_30_quantized, x = input_87)[name = string("out_13")]; + tensor input_91 = add(x = out_13, y = input_81)[name = string("input_91")]; + tensor input_93 = relu(x = input_91)[name = string("input_93")]; + string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("custom")]; + tensor input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor([2, 2])]; + tensor input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor([1, 1])]; + int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)]; + tensor const_32_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_32_quantized")]; + tensor const_33 = const()[name = string("const_33"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))]; + tensor input_97 = conv(bias = const_33, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_32_quantized, x = input_93)[name = string("input_97")]; + tensor input_99 = relu(x = input_97)[name = string("input_99")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("custom")]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1, 1])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1, 1])]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)]; + tensor const_34_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_34_quantized")]; + tensor const_35 = const()[name = string("const_35"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))]; + tensor out_15 = conv(bias = const_35, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_34_quantized, x = input_99)[name = string("out_15")]; + string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")]; + tensor input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor([2, 2])]; + tensor input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor([1, 1])]; + int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; + tensor const_36_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_36_quantized")]; + tensor const_37 = const()[name = string("const_37"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))]; + tensor var_338 = conv(bias = const_37, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_36_quantized, x = input_93)[name = string("op_338")]; + tensor input_105 = add(x = out_15, y = var_338)[name = string("input_105")]; + tensor input_107 = relu(x = input_105)[name = string("input_107")]; + string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("custom")]; + tensor input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor([1, 1])]; + tensor input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor([1, 1])]; + int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)]; + tensor const_38_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_38_quantized")]; + tensor const_39 = const()[name = string("const_39"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))]; + tensor input_111 = conv(bias = const_39, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_38_quantized, x = input_107)[name = string("input_111")]; + tensor input_113 = relu(x = input_111)[name = string("input_113")]; + string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("custom")]; + tensor input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_115_strides_0 = const()[name = string("input_115_strides_0"), val = tensor([1, 1])]; + tensor input_115_dilations_0 = const()[name = string("input_115_dilations_0"), val = tensor([1, 1])]; + int32 input_115_groups_0 = const()[name = string("input_115_groups_0"), val = int32(1)]; + tensor const_40_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_40_quantized")]; + tensor const_41 = const()[name = string("const_41"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))]; + tensor out_17 = conv(bias = const_41, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_40_quantized, x = input_113)[name = string("out_17")]; + tensor input_117 = add(x = out_17, y = input_107)[name = string("input_117")]; + tensor input_119 = relu(x = input_117)[name = string("input_119")]; + string input_121_pad_type_0 = const()[name = string("input_121_pad_type_0"), val = string("custom")]; + tensor input_121_pad_0 = const()[name = string("input_121_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_121_strides_0 = const()[name = string("input_121_strides_0"), val = tensor([1, 1])]; + tensor input_121_dilations_0 = const()[name = string("input_121_dilations_0"), val = tensor([1, 1])]; + int32 input_121_groups_0 = const()[name = string("input_121_groups_0"), val = int32(1)]; + tensor const_42_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_42_quantized")]; + tensor const_43 = const()[name = string("const_43"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))]; + tensor input_123 = conv(bias = const_43, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_42_quantized, x = input_119)[name = string("input_123")]; + tensor input_125 = relu(x = input_123)[name = string("input_125")]; + string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("custom")]; + tensor input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor([1, 1])]; + tensor input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor([1, 1])]; + int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)]; + tensor const_44_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_44_quantized")]; + tensor const_45 = const()[name = string("const_45"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))]; + tensor out_19 = conv(bias = const_45, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_44_quantized, x = input_125)[name = string("out_19")]; + tensor input_129 = add(x = out_19, y = input_119)[name = string("input_129")]; + tensor input_131 = relu(x = input_129)[name = string("input_131")]; + string input_133_pad_type_0 = const()[name = string("input_133_pad_type_0"), val = string("custom")]; + tensor input_133_pad_0 = const()[name = string("input_133_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_133_strides_0 = const()[name = string("input_133_strides_0"), val = tensor([1, 1])]; + tensor input_133_dilations_0 = const()[name = string("input_133_dilations_0"), val = tensor([1, 1])]; + int32 input_133_groups_0 = const()[name = string("input_133_groups_0"), val = int32(1)]; + tensor const_46_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_46_quantized")]; + tensor const_47 = const()[name = string("const_47"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))]; + tensor input_135 = conv(bias = const_47, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_46_quantized, x = input_131)[name = string("input_135")]; + tensor input_137 = relu(x = input_135)[name = string("input_137")]; + string input_139_pad_type_0 = const()[name = string("input_139_pad_type_0"), val = string("custom")]; + tensor input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_139_strides_0 = const()[name = string("input_139_strides_0"), val = tensor([1, 1])]; + tensor input_139_dilations_0 = const()[name = string("input_139_dilations_0"), val = tensor([1, 1])]; + int32 input_139_groups_0 = const()[name = string("input_139_groups_0"), val = int32(1)]; + tensor const_48_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_48_quantized")]; + tensor const_49 = const()[name = string("const_49"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))]; + tensor out_21 = conv(bias = const_49, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_48_quantized, x = input_137)[name = string("out_21")]; + tensor input_141 = add(x = out_21, y = input_131)[name = string("input_141")]; + tensor input_143 = relu(x = input_141)[name = string("input_143")]; + string input_145_pad_type_0 = const()[name = string("input_145_pad_type_0"), val = string("custom")]; + tensor input_145_pad_0 = const()[name = string("input_145_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_145_strides_0 = const()[name = string("input_145_strides_0"), val = tensor([1, 1])]; + tensor input_145_dilations_0 = const()[name = string("input_145_dilations_0"), val = tensor([1, 1])]; + int32 input_145_groups_0 = const()[name = string("input_145_groups_0"), val = int32(1)]; + tensor const_50_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_50_quantized")]; + tensor const_51 = const()[name = string("const_51"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))]; + tensor input_147 = conv(bias = const_51, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_50_quantized, x = input_143)[name = string("input_147")]; + tensor input_149 = relu(x = input_147)[name = string("input_149")]; + string input_151_pad_type_0 = const()[name = string("input_151_pad_type_0"), val = string("custom")]; + tensor input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_151_strides_0 = const()[name = string("input_151_strides_0"), val = tensor([1, 1])]; + tensor input_151_dilations_0 = const()[name = string("input_151_dilations_0"), val = tensor([1, 1])]; + int32 input_151_groups_0 = const()[name = string("input_151_groups_0"), val = int32(1)]; + tensor const_52_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_52_quantized")]; + tensor const_53 = const()[name = string("const_53"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))]; + tensor out_23 = conv(bias = const_53, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_52_quantized, x = input_149)[name = string("out_23")]; + tensor input_153 = add(x = out_23, y = input_143)[name = string("input_153")]; + tensor input_155 = relu(x = input_153)[name = string("input_155")]; + string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("custom")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor([1, 1])]; + tensor input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor([1, 1])]; + int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)]; + tensor const_54_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_54_quantized")]; + tensor const_55 = const()[name = string("const_55"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))]; + tensor input_159 = conv(bias = const_55, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_54_quantized, x = input_155)[name = string("input_159")]; + tensor input_161 = relu(x = input_159)[name = string("input_161")]; + string input_163_pad_type_0 = const()[name = string("input_163_pad_type_0"), val = string("custom")]; + tensor input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_163_strides_0 = const()[name = string("input_163_strides_0"), val = tensor([1, 1])]; + tensor input_163_dilations_0 = const()[name = string("input_163_dilations_0"), val = tensor([1, 1])]; + int32 input_163_groups_0 = const()[name = string("input_163_groups_0"), val = int32(1)]; + tensor const_56_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_56_quantized")]; + tensor const_57 = const()[name = string("const_57"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))]; + tensor out_25 = conv(bias = const_57, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_56_quantized, x = input_161)[name = string("out_25")]; + tensor input_165 = add(x = out_25, y = input_155)[name = string("input_165")]; + tensor input_167 = relu(x = input_165)[name = string("input_167")]; + string input_169_pad_type_0 = const()[name = string("input_169_pad_type_0"), val = string("custom")]; + tensor input_169_pad_0 = const()[name = string("input_169_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_169_strides_0 = const()[name = string("input_169_strides_0"), val = tensor([2, 2])]; + tensor input_169_dilations_0 = const()[name = string("input_169_dilations_0"), val = tensor([1, 1])]; + int32 input_169_groups_0 = const()[name = string("input_169_groups_0"), val = int32(1)]; + tensor const_58_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_58_quantized")]; + tensor const_59 = const()[name = string("const_59"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))]; + tensor input_171 = conv(bias = const_59, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_58_quantized, x = input_167)[name = string("input_171")]; + tensor input_173 = relu(x = input_171)[name = string("input_173")]; + string input_175_pad_type_0 = const()[name = string("input_175_pad_type_0"), val = string("custom")]; + tensor input_175_pad_0 = const()[name = string("input_175_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_175_strides_0 = const()[name = string("input_175_strides_0"), val = tensor([1, 1])]; + tensor input_175_dilations_0 = const()[name = string("input_175_dilations_0"), val = tensor([1, 1])]; + int32 input_175_groups_0 = const()[name = string("input_175_groups_0"), val = int32(1)]; + tensor const_60_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_60_quantized")]; + tensor const_61 = const()[name = string("const_61"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))]; + tensor out_27 = conv(bias = const_61, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_60_quantized, x = input_173)[name = string("out_27")]; + string input_177_pad_type_0 = const()[name = string("input_177_pad_type_0"), val = string("valid")]; + tensor input_177_strides_0 = const()[name = string("input_177_strides_0"), val = tensor([2, 2])]; + tensor input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_177_dilations_0 = const()[name = string("input_177_dilations_0"), val = tensor([1, 1])]; + int32 input_177_groups_0 = const()[name = string("input_177_groups_0"), val = int32(1)]; + tensor const_62_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_62_quantized")]; + tensor const_63 = const()[name = string("const_63"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))]; + tensor var_537 = conv(bias = const_63, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_62_quantized, x = input_167)[name = string("op_537")]; + tensor input_179 = add(x = out_27, y = var_537)[name = string("input_179")]; + tensor input_181 = relu(x = input_179)[name = string("input_181")]; + string input_183_pad_type_0 = const()[name = string("input_183_pad_type_0"), val = string("custom")]; + tensor input_183_pad_0 = const()[name = string("input_183_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_183_strides_0 = const()[name = string("input_183_strides_0"), val = tensor([1, 1])]; + tensor input_183_dilations_0 = const()[name = string("input_183_dilations_0"), val = tensor([1, 1])]; + int32 input_183_groups_0 = const()[name = string("input_183_groups_0"), val = int32(1)]; + tensor const_64_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_64_quantized")]; + tensor const_65 = const()[name = string("const_65"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))]; + tensor input_185 = conv(bias = const_65, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_64_quantized, x = input_181)[name = string("input_185")]; + tensor input_187 = relu(x = input_185)[name = string("input_187")]; + string input_189_pad_type_0 = const()[name = string("input_189_pad_type_0"), val = string("custom")]; + tensor input_189_pad_0 = const()[name = string("input_189_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_189_strides_0 = const()[name = string("input_189_strides_0"), val = tensor([1, 1])]; + tensor input_189_dilations_0 = const()[name = string("input_189_dilations_0"), val = tensor([1, 1])]; + int32 input_189_groups_0 = const()[name = string("input_189_groups_0"), val = int32(1)]; + tensor const_66_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_66_quantized")]; + tensor const_67 = const()[name = string("const_67"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))]; + tensor out_29 = conv(bias = const_67, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_66_quantized, x = input_187)[name = string("out_29")]; + tensor input_191 = add(x = out_29, y = input_181)[name = string("input_191")]; + tensor input_193 = relu(x = input_191)[name = string("input_193")]; + string input_195_pad_type_0 = const()[name = string("input_195_pad_type_0"), val = string("custom")]; + tensor input_195_pad_0 = const()[name = string("input_195_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_195_strides_0 = const()[name = string("input_195_strides_0"), val = tensor([1, 1])]; + tensor input_195_dilations_0 = const()[name = string("input_195_dilations_0"), val = tensor([1, 1])]; + int32 input_195_groups_0 = const()[name = string("input_195_groups_0"), val = int32(1)]; + tensor const_68_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_68_quantized")]; + tensor const_69 = const()[name = string("const_69"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))]; + tensor input_197 = conv(bias = const_69, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_68_quantized, x = input_193)[name = string("input_197")]; + tensor input_199 = relu(x = input_197)[name = string("input_199")]; + string input_201_pad_type_0 = const()[name = string("input_201_pad_type_0"), val = string("custom")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_201_strides_0 = const()[name = string("input_201_strides_0"), val = tensor([1, 1])]; + tensor input_201_dilations_0 = const()[name = string("input_201_dilations_0"), val = tensor([1, 1])]; + int32 input_201_groups_0 = const()[name = string("input_201_groups_0"), val = int32(1)]; + tensor const_70_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_70_quantized")]; + tensor const_71 = const()[name = string("const_71"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))]; + tensor out = conv(bias = const_71, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_70_quantized, x = input_199)[name = string("out")]; + tensor input_203 = add(x = out, y = input_193)[name = string("input_203")]; + tensor frames = relu(x = input_203)[name = string("frames")]; + tensor concat_0x = const()[name = string("concat_0x"), val = tensor([-1, 2560, 125])]; + tensor sequences = reshape(shape = concat_0x, x = frames)[name = string("sequences")]; + tensor input_205_axes_0 = const()[name = string("input_205_axes_0"), val = tensor([1])]; + tensor input_205 = expand_dims(axes = input_205_axes_0, x = weights)[name = string("input_205")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_205)[name = string("expand_dims_0")]; + fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)]; + fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)]; + tensor upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")]; + tensor weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor([3])]; + tensor weights_1 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")]; + tensor weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor([2])]; + bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)]; + tensor weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_1)[name = string("weight_sum")]; + fp32 var_627 = const()[name = string("op_627"), val = fp32(0x0p+0)]; + tensor var_628 = greater(x = weight_sum, y = var_627)[name = string("op_628")]; + fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)]; + tensor fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")]; + tensor safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_628)[name = string("safe_sum")]; + tensor var_636 = mul(x = sequences, y = weights_1)[name = string("op_636")]; + tensor var_641_axes_0 = const()[name = string("op_641_axes_0"), val = tensor([2])]; + bool var_641_keep_dims_0 = const()[name = string("op_641_keep_dims_0"), val = bool(false)]; + tensor var_641 = reduce_sum(axes = var_641_axes_0, keep_dims = var_641_keep_dims_0, x = var_636)[name = string("op_641")]; + tensor mean = real_div(x = var_641, y = safe_sum)[name = string("mean")]; + tensor var_644_axes_0 = const()[name = string("op_644_axes_0"), val = tensor([2])]; + tensor var_644 = expand_dims(axes = var_644_axes_0, x = mean)[name = string("op_644")]; + tensor var_646 = sub(x = sequences, y = var_644)[name = string("op_646")]; + tensor dx2 = mul(x = var_646, y = var_646)[name = string("dx2")]; + tensor var_648 = mul(x = weights_1, y = weights_1)[name = string("op_648")]; + tensor weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor([2])]; + bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)]; + tensor weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_648)[name = string("weight_sq_sum")]; + tensor var_654 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_654")]; + tensor var_656 = sub(x = safe_sum, y = var_654)[name = string("op_656")]; + fp32 var_658 = const()[name = string("op_658"), val = fp32(0x1.5798eep-27)]; + tensor denom = add(x = var_656, y = var_658)[name = string("denom")]; + tensor var_660 = mul(x = dx2, y = weights_1)[name = string("op_660")]; + tensor var_665_axes_0 = const()[name = string("op_665_axes_0"), val = tensor([2])]; + bool var_665_keep_dims_0 = const()[name = string("op_665_keep_dims_0"), val = bool(false)]; + tensor var_665 = reduce_sum(axes = var_665_axes_0, keep_dims = var_665_keep_dims_0, x = var_660)[name = string("op_665")]; + tensor var = real_div(x = var_665, y = denom)[name = string("var")]; + fp32 var_667 = const()[name = string("op_667"), val = fp32(0x1.b7cdfep-34)]; + tensor var_668 = maximum(x = var, y = var_667)[name = string("op_668")]; + tensor std = sqrt(x = var_668)[name = string("std")]; + int32 var_671 = const()[name = string("op_671"), val = int32(-1)]; + bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)]; + tensor stats = concat(axis = var_671, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")]; + tensor var_678 = sub(x = mean, y = mean)[name = string("sub_0")]; + fp32 var_685_value_0 = const()[name = string("op_685_value_0"), val = fp32(0x1.4f8b58p-17)]; + tensor var_685 = fill_like(ref_tensor = std, value = var_685_value_0)[name = string("op_685")]; + int32 var_687 = const()[name = string("op_687"), val = int32(-1)]; + bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)]; + tensor zero_stats = concat(axis = var_687, interleave = zero_stats_interleave_0, values = (var_678, var_685))[name = string("zero_stats")]; + fp32 var_689 = const()[name = string("op_689"), val = fp32(0x0p+0)]; + tensor var_690 = less_equal(x = weight_sum, y = var_689)[name = string("op_690")]; + tensor var_696 = const()[name = string("op_696"), val = tensor([1, 5120])]; + tensor zero_mask = tile(reps = var_696, x = var_690)[name = string("zero_mask")]; + tensor input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")]; + tensor output = linear(bias = resnet_seg_1_bias, weight = resnet_seg_1_weight_quantized, x = input)[name = string("linear_0")]; + } -> (output); +} \ No newline at end of file diff --git a/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/weights/weight.bin b/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..3114ce354da5e5fe51316189c5c49b584a0ff08f --- /dev/null +++ b/wespeaker-voxceleb-resnet34-tail-w8a16.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed5873a1aeef9493f6ca8659ff3b75b7673a6b1d450e8973d165d2913181013a +size 6675328