| program(1.0) |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}})] |
| { |
| func main<ios17>(tensor<fp32, [?, 1, 1, 160589]> audio_and_weights) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"audio_and_weights", [1, 1, 1, 160589]}}), ("RangeDims", {{"audio_and_weights", [[1, 32], [1, 1], [1, 1], [160589, 160589]]}})))] { |
| tensor<fp32, [1, 125]> _interp_right_weight = const()[name = tensor<string, []>("_interp_right_weight"), val = tensor<fp32, [1, 125]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; |
| tensor<fp32, [1, 125]> _interp_left_weight = const()[name = tensor<string, []>("_interp_left_weight"), val = tensor<fp32, [1, 125]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640)))]; |
| tensor<fp32, [80, 257, 1]> _fbank_mel_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("_fbank_mel_weight_quantized"), quantized_data = tensor<int8, [80, 257, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1216))), scale = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22080))), zero_point = tensor<int8, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21888)))]; |
| tensor<fp32, [257, 1, 512]> _fbank_dft_imag_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("_fbank_dft_imag_weight_quantized"), quantized_data = tensor<int8, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22464))), scale = tensor<fp32, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154496))), zero_point = tensor<int8, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154112)))]; |
| tensor<fp32, [257, 1, 512]> _fbank_dft_real_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("_fbank_dft_real_weight_quantized"), quantized_data = tensor<int8, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155648))), scale = tensor<fp32, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287296))), zero_point = tensor<int8, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154112)))]; |
| tensor<fp32, [1, 400]> _fbank_window = const()[name = tensor<string, []>("_fbank_window"), val = tensor<fp32, [1, 400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288448)))]; |
| tensor<fp32, []> _fbank_eps = const()[name = tensor<string, []>("_fbank_eps"), val = tensor<fp32, []>(0x1.b7cdfep-34)]; |
| tensor<fp32, [400, 1, 400]> _fbank_frame_kernel_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("_fbank_frame_kernel_quantized"), quantized_data = tensor<int8, [400, 1, 400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290112))), scale = tensor<fp32, [400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450688))), zero_point = tensor<int8, [400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450176)))]; |
| tensor<fp32, [256]> resnet_seg_1_bias = const()[name = tensor<string, []>("resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452352)))]; |
| tensor<fp32, [256, 5120, 1, 1]> resnet_seg_1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("resnet_seg_1_weight_quantized"), quantized_data = tensor<int8, [256, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(453440))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1764544))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1764224)))]; |
| tensor<int32, [4]> var_24_begin_0 = const()[name = tensor<string, []>("op_24_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_24_end_0 = const()[name = tensor<string, []>("op_24_end_0"), val = tensor<int32, [4]>([0, 1, 1, 160000])]; |
| tensor<bool, [4]> var_24_end_mask_0 = const()[name = tensor<string, []>("op_24_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp32, [?, 1, 1, 160000]> var_24 = slice_by_index(begin = var_24_begin_0, end = var_24_end_0, end_mask = var_24_end_mask_0, x = audio_and_weights)[name = tensor<string, []>("op_24")]; |
| tensor<int32, [3]> concat_0x = const()[name = tensor<string, []>("concat_0x"), val = tensor<int32, [3]>([-1, 1, 160000])]; |
| tensor<fp32, [?, 1, 160000]> waveforms_1 = reshape(shape = concat_0x, x = var_24)[name = tensor<string, []>("waveforms_1")]; |
| tensor<int32, [4]> var_33_begin_0 = const()[name = tensor<string, []>("op_33_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 160000])]; |
| tensor<int32, [4]> var_33_end_0 = const()[name = tensor<string, []>("op_33_end_0"), val = tensor<int32, [4]>([0, 1, 1, 160589])]; |
| tensor<bool, [4]> var_33_end_mask_0 = const()[name = tensor<string, []>("op_33_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp32, [?, 1, 1, 589]> var_33 = slice_by_index(begin = var_33_begin_0, end = var_33_end_0, end_mask = var_33_end_mask_0, x = audio_and_weights)[name = tensor<string, []>("op_33")]; |
| tensor<int32, [2]> concat_1x = const()[name = tensor<string, []>("concat_1x"), val = tensor<int32, [2]>([-1, 589])]; |
| tensor<fp32, [?, 589]> weights_1 = reshape(shape = concat_1x, x = var_33)[name = tensor<string, []>("weights_1")]; |
| tensor<fp32, []> var_36_promoted = const()[name = tensor<string, []>("op_36_promoted"), val = tensor<fp32, []>(0x1p+15)]; |
| tensor<fp32, [?, 1, 160000]> waveforms_3 = mul(x = waveforms_1, y = var_36_promoted)[name = tensor<string, []>("waveforms_3")]; |
| tensor<string, []> frames_1_pad_type_0 = const()[name = tensor<string, []>("frames_1_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> frames_1_strides_0 = const()[name = tensor<string, []>("frames_1_strides_0"), val = tensor<int32, [1]>([160])]; |
| tensor<int32, [2]> frames_1_pad_0 = const()[name = tensor<string, []>("frames_1_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> frames_1_dilations_0 = const()[name = tensor<string, []>("frames_1_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> frames_1_groups_0 = const()[name = tensor<string, []>("frames_1_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [?, 400, 998]> 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 = _fbank_frame_kernel_quantized, x = waveforms_3)[name = tensor<string, []>("frames_1")]; |
| tensor<int32, [3]> frames_3_perm_0 = const()[name = tensor<string, []>("frames_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [2]> concat_2x = const()[name = tensor<string, []>("concat_2x"), val = tensor<int32, [2]>([-1, 400])]; |
| tensor<fp32, [?, 998, 400]> frames_3 = transpose(perm = frames_3_perm_0, x = frames_1)[name = tensor<string, []>("transpose_1")]; |
| tensor<fp32, [?, 400]> frames_5 = reshape(shape = concat_2x, x = frames_3)[name = tensor<string, []>("frames_5")]; |
| tensor<int32, [1]> var_86_axes_0 = const()[name = tensor<string, []>("op_86_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<bool, []> var_86_keep_dims_0 = const()[name = tensor<string, []>("op_86_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [?, 1]> var_86 = reduce_mean(axes = var_86_axes_0, keep_dims = var_86_keep_dims_0, x = frames_5)[name = tensor<string, []>("op_86")]; |
| tensor<fp32, [?, 400]> frames_7 = sub(x = frames_5, y = var_86)[name = tensor<string, []>("frames_7")]; |
| tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp32, [?, 1, 400]> input_1 = expand_dims(axes = input_1_axes_0, x = frames_7)[name = tensor<string, []>("input_1")]; |
| tensor<fp32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<fp32, []>(0x0p+0)]; |
| tensor<int32, [6]> var_90_pad_0 = const()[name = tensor<string, []>("op_90_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])]; |
| tensor<string, []> var_90_mode_0 = const()[name = tensor<string, []>("op_90_mode_0"), val = tensor<string, []>("replicate")]; |
| tensor<fp32, [?, 1, 401]> var_90 = pad(constant_val = const_0, mode = var_90_mode_0, pad = var_90_pad_0, x = input_1)[name = tensor<string, []>("op_90")]; |
| tensor<int32, [1]> padded_axes_0 = const()[name = tensor<string, []>("padded_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp32, [?, 401]> padded = squeeze(axes = padded_axes_0, x = var_90)[name = tensor<string, []>("padded")]; |
| tensor<int32, [2]> var_93_begin_0 = const()[name = tensor<string, []>("op_93_begin_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [2]> var_93_end_0 = const()[name = tensor<string, []>("op_93_end_0"), val = tensor<int32, [2]>([0, 400])]; |
| tensor<bool, [2]> var_93_end_mask_0 = const()[name = tensor<string, []>("op_93_end_mask_0"), val = tensor<bool, [2]>([true, false])]; |
| tensor<fp32, [?, 400]> var_93 = slice_by_index(begin = var_93_begin_0, end = var_93_end_0, end_mask = var_93_end_mask_0, x = padded)[name = tensor<string, []>("op_93")]; |
| tensor<fp32, []> var_94 = const()[name = tensor<string, []>("op_94"), val = tensor<fp32, []>(0x1.f0a3d8p-1)]; |
| tensor<fp32, [?, 400]> var_95 = mul(x = var_93, y = var_94)[name = tensor<string, []>("op_95")]; |
| tensor<fp32, [?, 400]> frames_9 = sub(x = frames_7, y = var_95)[name = tensor<string, []>("frames_9")]; |
| tensor<fp32, [?, 400]> frames_11 = mul(x = frames_9, y = _fbank_window)[name = tensor<string, []>("frames_11")]; |
| tensor<int32, [1]> input_3_axes_0 = const()[name = tensor<string, []>("input_3_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp32, [?, 1, 400]> input_3 = expand_dims(axes = input_3_axes_0, x = frames_11)[name = tensor<string, []>("input_3")]; |
| tensor<fp32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<fp32, []>(0x0p+0)]; |
| tensor<int32, [6]> var_100_pad_0 = const()[name = tensor<string, []>("op_100_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])]; |
| tensor<string, []> var_100_mode_0 = const()[name = tensor<string, []>("op_100_mode_0"), val = tensor<string, []>("constant")]; |
| tensor<fp32, [?, 1, 512]> var_100 = pad(constant_val = const_1, mode = var_100_mode_0, pad = var_100_pad_0, x = input_3)[name = tensor<string, []>("op_100")]; |
| tensor<string, []> var_107_pad_type_0 = const()[name = tensor<string, []>("op_107_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> var_107_strides_0 = const()[name = tensor<string, []>("op_107_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_107_pad_0 = const()[name = tensor<string, []>("op_107_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_107_dilations_0 = const()[name = tensor<string, []>("op_107_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> var_107_groups_0 = const()[name = tensor<string, []>("op_107_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [?, 257, 1]> var_107 = conv(dilations = var_107_dilations_0, groups = var_107_groups_0, pad = var_107_pad_0, pad_type = var_107_pad_type_0, strides = var_107_strides_0, weight = _fbank_dft_real_weight_quantized, x = var_100)[name = tensor<string, []>("op_107")]; |
| tensor<int32, [1]> real_axes_0 = const()[name = tensor<string, []>("real_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [?, 257]> real = squeeze(axes = real_axes_0, x = var_107)[name = tensor<string, []>("real")]; |
| tensor<string, []> var_113_pad_type_0 = const()[name = tensor<string, []>("op_113_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> var_113_strides_0 = const()[name = tensor<string, []>("op_113_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_113_pad_0 = const()[name = tensor<string, []>("op_113_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_113_dilations_0 = const()[name = tensor<string, []>("op_113_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> var_113_groups_0 = const()[name = tensor<string, []>("op_113_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [?, 257, 1]> var_113 = conv(dilations = var_113_dilations_0, groups = var_113_groups_0, pad = var_113_pad_0, pad_type = var_113_pad_type_0, strides = var_113_strides_0, weight = _fbank_dft_imag_weight_quantized, x = var_100)[name = tensor<string, []>("op_113")]; |
| tensor<int32, [1]> imag_axes_0 = const()[name = tensor<string, []>("imag_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [?, 257]> imag = squeeze(axes = imag_axes_0, x = var_113)[name = tensor<string, []>("imag")]; |
| tensor<fp32, []> var_55_promoted = const()[name = tensor<string, []>("op_55_promoted"), val = tensor<fp32, []>(0x1p+1)]; |
| tensor<fp32, [?, 257]> var_115 = pow(x = real, y = var_55_promoted)[name = tensor<string, []>("op_115")]; |
| tensor<fp32, []> var_55_promoted_1 = const()[name = tensor<string, []>("op_55_promoted_1"), val = tensor<fp32, []>(0x1p+1)]; |
| tensor<fp32, [?, 257]> var_116 = pow(x = imag, y = var_55_promoted_1)[name = tensor<string, []>("op_116")]; |
| tensor<fp32, [?, 257]> power = add(x = var_115, y = var_116)[name = tensor<string, []>("power")]; |
| tensor<int32, [1]> var_118_axes_0 = const()[name = tensor<string, []>("op_118_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [?, 257, 1]> var_118 = expand_dims(axes = var_118_axes_0, x = power)[name = tensor<string, []>("op_118")]; |
| tensor<string, []> var_123_pad_type_0 = const()[name = tensor<string, []>("op_123_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> var_123_strides_0 = const()[name = tensor<string, []>("op_123_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_123_pad_0 = const()[name = tensor<string, []>("op_123_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_123_dilations_0 = const()[name = tensor<string, []>("op_123_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> var_123_groups_0 = const()[name = tensor<string, []>("op_123_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [?, 80, 1]> var_123 = conv(dilations = var_123_dilations_0, groups = var_123_groups_0, pad = var_123_pad_0, pad_type = var_123_pad_type_0, strides = var_123_strides_0, weight = _fbank_mel_weight_quantized, x = var_118)[name = tensor<string, []>("op_123")]; |
| tensor<int32, [1]> mel_1_axes_0 = const()[name = tensor<string, []>("mel_1_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [?, 80]> mel_1 = squeeze(axes = mel_1_axes_0, x = var_123)[name = tensor<string, []>("mel_1")]; |
| tensor<fp32, []> const_2 = const()[name = tensor<string, []>("const_2"), val = tensor<fp32, []>(0x1.fffffep+127)]; |
| tensor<fp32, [?, 80]> clip_0 = clip(alpha = _fbank_eps, beta = const_2, x = mel_1)[name = tensor<string, []>("clip_0")]; |
| tensor<fp32, []> mel_epsilon_0 = const()[name = tensor<string, []>("mel_epsilon_0"), val = tensor<fp32, []>(0x1p-149)]; |
| tensor<fp32, [?, 80]> mel = log(epsilon = mel_epsilon_0, x = clip_0)[name = tensor<string, []>("mel")]; |
| tensor<int32, [3]> concat_3x = const()[name = tensor<string, []>("concat_3x"), val = tensor<int32, [3]>([-1, 998, 80])]; |
| tensor<fp32, [?, 998, 80]> var_128 = reshape(shape = concat_3x, x = mel)[name = tensor<string, []>("op_128")]; |
| tensor<int32, [1]> centered_axes_0 = const()[name = tensor<string, []>("centered_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<bool, []> centered_keep_dims_0 = const()[name = tensor<string, []>("centered_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [?, 1, 80]> centered = reduce_mean(axes = centered_axes_0, keep_dims = centered_keep_dims_0, x = var_128)[name = tensor<string, []>("centered")]; |
| tensor<fp32, [?, 998, 80]> features_1 = sub(x = var_128, y = centered)[name = tensor<string, []>("features_1")]; |
| tensor<int32, [3]> var_147 = const()[name = tensor<string, []>("op_147"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> input_5_axes_0 = const()[name = tensor<string, []>("input_5_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp32, [?, 80, 998]> var_148 = transpose(perm = var_147, x = features_1)[name = tensor<string, []>("transpose_0")]; |
| tensor<fp32, [?, 1, 80, 998]> input_5 = expand_dims(axes = input_5_axes_0, x = var_148)[name = tensor<string, []>("input_5")]; |
| tensor<int32, []> left_batch_dims_0 = const()[name = tensor<string, []>("left_batch_dims_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> left_validate_indices_0 = const()[name = tensor<string, []>("left_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [125]> select_0 = const()[name = tensor<string, []>("select_0"), val = tensor<int32, [125]>([0, 4, 9, 14, 18, 23, 28, 33, 37, 42, 47, 52, 56, 61, 66, 71, 75, 80, 85, 90, 94, 99, 104, 109, 113, 118, 123, 128, 132, 137, 142, 147, 151, 156, 161, 165, 170, 175, 180, 184, 189, 194, 199, 203, 208, 213, 218, 222, 227, 232, 237, 241, 246, 251, 256, 260, 265, 270, 275, 279, 284, 289, 294, 298, 303, 308, 312, 317, 322, 327, 331, 336, 341, 346, 350, 355, 360, 365, 369, 374, 379, 384, 388, 393, 398, 403, 407, 412, 417, 422, 426, 431, 436, 441, 445, 450, 455, 459, 464, 469, 474, 478, 483, 488, 493, 497, 502, 507, 512, 516, 521, 526, 531, 535, 540, 545, 550, 554, 559, 564, 569, 573, 578, 583, 588])]; |
| tensor<int32, []> left_axis_0 = const()[name = tensor<string, []>("left_axis_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [?, 125]> left = gather(axis = left_axis_0, batch_dims = left_batch_dims_0, indices = select_0, validate_indices = left_validate_indices_0, x = weights_1)[name = tensor<string, []>("left")]; |
| tensor<int32, []> right_batch_dims_0 = const()[name = tensor<string, []>("right_batch_dims_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> right_validate_indices_0 = const()[name = tensor<string, []>("right_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [125]> select_1 = const()[name = tensor<string, []>("select_1"), val = tensor<int32, [125]>([1, 5, 10, 15, 19, 24, 29, 34, 38, 43, 48, 53, 57, 62, 67, 72, 76, 81, 86, 91, 95, 100, 105, 110, 114, 119, 124, 129, 133, 138, 143, 148, 152, 157, 162, 166, 171, 176, 181, 185, 190, 195, 200, 204, 209, 214, 219, 223, 228, 233, 238, 242, 247, 252, 257, 261, 266, 271, 276, 280, 285, 290, 295, 299, 304, 309, 313, 318, 323, 328, 332, 337, 342, 347, 351, 356, 361, 366, 370, 375, 380, 385, 389, 394, 399, 404, 408, 413, 418, 423, 427, 432, 437, 442, 446, 451, 456, 460, 465, 470, 475, 479, 484, 489, 494, 498, 503, 508, 513, 517, 522, 527, 532, 536, 541, 546, 551, 555, 560, 565, 570, 574, 579, 584, 588])]; |
| tensor<int32, []> right_axis_0 = const()[name = tensor<string, []>("right_axis_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [?, 125]> right = gather(axis = right_axis_0, batch_dims = right_batch_dims_0, indices = select_1, validate_indices = right_validate_indices_0, x = weights_1)[name = tensor<string, []>("right")]; |
| tensor<fp32, [?, 125]> var_171 = mul(x = left, y = _interp_left_weight)[name = tensor<string, []>("op_171")]; |
| tensor<fp32, [?, 125]> var_172 = mul(x = right, y = _interp_right_weight)[name = tensor<string, []>("op_172")]; |
| tensor<fp32, [?, 125]> weights_3 = add(x = var_171, y = var_172)[name = tensor<string, []>("weights_3")]; |
| tensor<int32, []> var_176 = const()[name = tensor<string, []>("op_176"), val = tensor<int32, []>(-1)]; |
| tensor<fp32, []> var_177 = const()[name = tensor<string, []>("op_177"), val = tensor<fp32, []>(0x1.197998p-40)]; |
| tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_7_strides_0 = const()[name = tensor<string, []>("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_7_dilations_0 = const()[name = tensor<string, []>("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_7_groups_0 = const()[name = tensor<string, []>("input_7_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [32, 1, 3, 3]> const_5 = const()[name = tensor<string, []>("const_5"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1765632)))]; |
| tensor<fp32, [32]> const_6 = const()[name = tensor<string, []>("const_6"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1766848)))]; |
| tensor<fp32, [?, 32, 80, 998]> input_9 = conv(bias = const_6, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = const_5, x = input_5)[name = tensor<string, []>("input_9")]; |
| tensor<fp32, [?, 32, 80, 998]> input_11 = relu(x = input_9)[name = tensor<string, []>("input_11")]; |
| tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_13_strides_0 = const()[name = tensor<string, []>("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_13_dilations_0 = const()[name = tensor<string, []>("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_13_groups_0 = const()[name = tensor<string, []>("input_13_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [32, 32, 3, 3]> const_7_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_7_quantized"), quantized_data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1767040))), scale = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1776448))), zero_point = tensor<int8, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1776320)))]; |
| tensor<fp32, [32]> const_8 = const()[name = tensor<string, []>("const_8"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1776640)))]; |
| tensor<fp32, [?, 32, 80, 998]> input_15 = conv(bias = const_8, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = const_7_quantized, x = input_11)[name = tensor<string, []>("input_15")]; |
| tensor<fp32, [?, 32, 80, 998]> input_17 = relu(x = input_15)[name = tensor<string, []>("input_17")]; |
| tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_19_strides_0 = const()[name = tensor<string, []>("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_19_dilations_0 = const()[name = tensor<string, []>("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_19_groups_0 = const()[name = tensor<string, []>("input_19_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [32, 32, 3, 3]> const_9_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_9_quantized"), quantized_data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1776832))), scale = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1786240))), zero_point = tensor<int8, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1786112)))]; |
| tensor<fp32, [32]> const_10 = const()[name = tensor<string, []>("const_10"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1786432)))]; |
| tensor<fp32, [?, 32, 80, 998]> out_1 = conv(bias = const_10, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = const_9_quantized, x = input_17)[name = tensor<string, []>("out_1")]; |
| tensor<fp32, [?, 32, 80, 998]> input_21 = add(x = out_1, y = input_11)[name = tensor<string, []>("input_21")]; |
| tensor<fp32, [?, 32, 80, 998]> input_23 = relu(x = input_21)[name = tensor<string, []>("input_23")]; |
| tensor<string, []> input_25_pad_type_0 = const()[name = tensor<string, []>("input_25_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_25_pad_0 = const()[name = tensor<string, []>("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_25_strides_0 = const()[name = tensor<string, []>("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_25_dilations_0 = const()[name = tensor<string, []>("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_25_groups_0 = const()[name = tensor<string, []>("input_25_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [32, 32, 3, 3]> const_11_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_11_quantized"), quantized_data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1786624))), scale = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1796032))), zero_point = tensor<int8, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1795904)))]; |
| tensor<fp32, [32]> const_12 = const()[name = tensor<string, []>("const_12"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1796224)))]; |
| tensor<fp32, [?, 32, 80, 998]> input_27 = conv(bias = const_12, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = const_11_quantized, x = input_23)[name = tensor<string, []>("input_27")]; |
| tensor<fp32, [?, 32, 80, 998]> input_29 = relu(x = input_27)[name = tensor<string, []>("input_29")]; |
| tensor<string, []> input_31_pad_type_0 = const()[name = tensor<string, []>("input_31_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_31_pad_0 = const()[name = tensor<string, []>("input_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_31_strides_0 = const()[name = tensor<string, []>("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_31_dilations_0 = const()[name = tensor<string, []>("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_31_groups_0 = const()[name = tensor<string, []>("input_31_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [32, 32, 3, 3]> const_13_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_13_quantized"), quantized_data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1796416))), scale = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1805824))), zero_point = tensor<int8, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1805696)))]; |
| tensor<fp32, [32]> const_14 = const()[name = tensor<string, []>("const_14"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1806016)))]; |
| tensor<fp32, [?, 32, 80, 998]> out_3 = conv(bias = const_14, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = const_13_quantized, x = input_29)[name = tensor<string, []>("out_3")]; |
| tensor<fp32, [?, 32, 80, 998]> input_33 = add(x = out_3, y = input_23)[name = tensor<string, []>("input_33")]; |
| tensor<fp32, [?, 32, 80, 998]> input_35 = relu(x = input_33)[name = tensor<string, []>("input_35")]; |
| tensor<string, []> input_37_pad_type_0 = const()[name = tensor<string, []>("input_37_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_37_pad_0 = const()[name = tensor<string, []>("input_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_37_strides_0 = const()[name = tensor<string, []>("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_37_dilations_0 = const()[name = tensor<string, []>("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_37_groups_0 = const()[name = tensor<string, []>("input_37_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [32, 32, 3, 3]> const_15_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_15_quantized"), quantized_data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1806208))), scale = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1815616))), zero_point = tensor<int8, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1815488)))]; |
| tensor<fp32, [32]> const_16 = const()[name = tensor<string, []>("const_16"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1815808)))]; |
| tensor<fp32, [?, 32, 80, 998]> input_39 = conv(bias = const_16, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = const_15_quantized, x = input_35)[name = tensor<string, []>("input_39")]; |
| tensor<fp32, [?, 32, 80, 998]> input_41 = relu(x = input_39)[name = tensor<string, []>("input_41")]; |
| tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [32, 32, 3, 3]> const_17_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_17_quantized"), quantized_data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1816000))), scale = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1825408))), zero_point = tensor<int8, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1825280)))]; |
| tensor<fp32, [32]> const_18 = const()[name = tensor<string, []>("const_18"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1825600)))]; |
| tensor<fp32, [?, 32, 80, 998]> out_5 = conv(bias = const_18, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = const_17_quantized, x = input_41)[name = tensor<string, []>("out_5")]; |
| tensor<fp32, [?, 32, 80, 998]> input_45 = add(x = out_5, y = input_35)[name = tensor<string, []>("input_45")]; |
| tensor<fp32, [?, 32, 80, 998]> input_47 = relu(x = input_45)[name = tensor<string, []>("input_47")]; |
| tensor<string, []> input_49_pad_type_0 = const()[name = tensor<string, []>("input_49_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_49_pad_0 = const()[name = tensor<string, []>("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_49_strides_0 = const()[name = tensor<string, []>("input_49_strides_0"), val = tensor<int32, [2]>([2, 2])]; |
| tensor<int32, [2]> input_49_dilations_0 = const()[name = tensor<string, []>("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_49_groups_0 = const()[name = tensor<string, []>("input_49_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [64, 32, 3, 3]> const_19_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_19_quantized"), quantized_data = tensor<int8, [64, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1825792))), scale = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1844416))), zero_point = tensor<int8, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1844288)))]; |
| tensor<fp32, [64]> const_20 = const()[name = tensor<string, []>("const_20"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1844736)))]; |
| tensor<fp32, [?, 64, 40, 499]> input_51 = conv(bias = const_20, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_19_quantized, x = input_47)[name = tensor<string, []>("input_51")]; |
| tensor<fp32, [?, 64, 40, 499]> input_53 = relu(x = input_51)[name = tensor<string, []>("input_53")]; |
| tensor<string, []> input_55_pad_type_0 = const()[name = tensor<string, []>("input_55_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_55_pad_0 = const()[name = tensor<string, []>("input_55_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_55_strides_0 = const()[name = tensor<string, []>("input_55_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_55_dilations_0 = const()[name = tensor<string, []>("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_55_groups_0 = const()[name = tensor<string, []>("input_55_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [64, 64, 3, 3]> const_21_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_21_quantized"), quantized_data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1845056))), scale = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1882112))), zero_point = tensor<int8, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1881984)))]; |
| tensor<fp32, [64]> const_22 = const()[name = tensor<string, []>("const_22"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1882432)))]; |
| tensor<fp32, [?, 64, 40, 499]> out_7 = conv(bias = const_22, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = const_21_quantized, x = input_53)[name = tensor<string, []>("out_7")]; |
| tensor<string, []> input_57_pad_type_0 = const()[name = tensor<string, []>("input_57_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [2]> input_57_strides_0 = const()[name = tensor<string, []>("input_57_strides_0"), val = tensor<int32, [2]>([2, 2])]; |
| tensor<int32, [4]> input_57_pad_0 = const()[name = tensor<string, []>("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_57_dilations_0 = const()[name = tensor<string, []>("input_57_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_57_groups_0 = const()[name = tensor<string, []>("input_57_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [64, 32, 1, 1]> const_23_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_23_quantized"), quantized_data = tensor<int8, [64, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1882752))), scale = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1884992))), zero_point = tensor<int8, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1884864)))]; |
| tensor<fp32, [64]> const_24 = const()[name = tensor<string, []>("const_24"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1885312)))]; |
| tensor<fp32, [?, 64, 40, 499]> var_339 = conv(bias = const_24, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_23_quantized, x = input_47)[name = tensor<string, []>("op_339")]; |
| tensor<fp32, [?, 64, 40, 499]> input_59 = add(x = out_7, y = var_339)[name = tensor<string, []>("input_59")]; |
| tensor<fp32, [?, 64, 40, 499]> input_61 = relu(x = input_59)[name = tensor<string, []>("input_61")]; |
| tensor<string, []> input_63_pad_type_0 = const()[name = tensor<string, []>("input_63_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_63_pad_0 = const()[name = tensor<string, []>("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_63_strides_0 = const()[name = tensor<string, []>("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_63_dilations_0 = const()[name = tensor<string, []>("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_63_groups_0 = const()[name = tensor<string, []>("input_63_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [64, 64, 3, 3]> const_25_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_25_quantized"), quantized_data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1885632))), scale = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1922688))), zero_point = tensor<int8, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1922560)))]; |
| tensor<fp32, [64]> const_26 = const()[name = tensor<string, []>("const_26"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1923008)))]; |
| tensor<fp32, [?, 64, 40, 499]> input_65 = conv(bias = const_26, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = const_25_quantized, x = input_61)[name = tensor<string, []>("input_65")]; |
| tensor<fp32, [?, 64, 40, 499]> input_67 = relu(x = input_65)[name = tensor<string, []>("input_67")]; |
| tensor<string, []> input_69_pad_type_0 = const()[name = tensor<string, []>("input_69_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_69_pad_0 = const()[name = tensor<string, []>("input_69_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_69_strides_0 = const()[name = tensor<string, []>("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_69_dilations_0 = const()[name = tensor<string, []>("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_69_groups_0 = const()[name = tensor<string, []>("input_69_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [64, 64, 3, 3]> const_27_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_27_quantized"), quantized_data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1923328))), scale = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1960384))), zero_point = tensor<int8, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1960256)))]; |
| tensor<fp32, [64]> const_28 = const()[name = tensor<string, []>("const_28"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1960704)))]; |
| tensor<fp32, [?, 64, 40, 499]> out_9 = conv(bias = const_28, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = const_27_quantized, x = input_67)[name = tensor<string, []>("out_9")]; |
| tensor<fp32, [?, 64, 40, 499]> input_71 = add(x = out_9, y = input_61)[name = tensor<string, []>("input_71")]; |
| tensor<fp32, [?, 64, 40, 499]> input_73 = relu(x = input_71)[name = tensor<string, []>("input_73")]; |
| tensor<string, []> input_75_pad_type_0 = const()[name = tensor<string, []>("input_75_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_75_pad_0 = const()[name = tensor<string, []>("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_75_strides_0 = const()[name = tensor<string, []>("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_75_dilations_0 = const()[name = tensor<string, []>("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_75_groups_0 = const()[name = tensor<string, []>("input_75_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [64, 64, 3, 3]> const_29_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_29_quantized"), quantized_data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1961024))), scale = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1998080))), zero_point = tensor<int8, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1997952)))]; |
| tensor<fp32, [64]> const_30 = const()[name = tensor<string, []>("const_30"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1998400)))]; |
| tensor<fp32, [?, 64, 40, 499]> input_77 = conv(bias = const_30, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = const_29_quantized, x = input_73)[name = tensor<string, []>("input_77")]; |
| tensor<fp32, [?, 64, 40, 499]> input_79 = relu(x = input_77)[name = tensor<string, []>("input_79")]; |
| tensor<string, []> input_81_pad_type_0 = const()[name = tensor<string, []>("input_81_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_81_pad_0 = const()[name = tensor<string, []>("input_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_81_strides_0 = const()[name = tensor<string, []>("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_81_dilations_0 = const()[name = tensor<string, []>("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_81_groups_0 = const()[name = tensor<string, []>("input_81_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [64, 64, 3, 3]> const_31_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_31_quantized"), quantized_data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1998720))), scale = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2035776))), zero_point = tensor<int8, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2035648)))]; |
| tensor<fp32, [64]> const_32 = const()[name = tensor<string, []>("const_32"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2036096)))]; |
| tensor<fp32, [?, 64, 40, 499]> out_11 = conv(bias = const_32, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = const_31_quantized, x = input_79)[name = tensor<string, []>("out_11")]; |
| tensor<fp32, [?, 64, 40, 499]> input_83 = add(x = out_11, y = input_73)[name = tensor<string, []>("input_83")]; |
| tensor<fp32, [?, 64, 40, 499]> input_85 = relu(x = input_83)[name = tensor<string, []>("input_85")]; |
| tensor<string, []> input_87_pad_type_0 = const()[name = tensor<string, []>("input_87_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_87_pad_0 = const()[name = tensor<string, []>("input_87_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_87_strides_0 = const()[name = tensor<string, []>("input_87_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_87_dilations_0 = const()[name = tensor<string, []>("input_87_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_87_groups_0 = const()[name = tensor<string, []>("input_87_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [64, 64, 3, 3]> const_33_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_33_quantized"), quantized_data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2036416))), scale = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2073472))), zero_point = tensor<int8, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2073344)))]; |
| tensor<fp32, [64]> const_34 = const()[name = tensor<string, []>("const_34"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2073792)))]; |
| tensor<fp32, [?, 64, 40, 499]> input_89 = conv(bias = const_34, dilations = input_87_dilations_0, groups = input_87_groups_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = const_33_quantized, x = input_85)[name = tensor<string, []>("input_89")]; |
| tensor<fp32, [?, 64, 40, 499]> input_91 = relu(x = input_89)[name = tensor<string, []>("input_91")]; |
| tensor<string, []> input_93_pad_type_0 = const()[name = tensor<string, []>("input_93_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_93_pad_0 = const()[name = tensor<string, []>("input_93_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_93_strides_0 = const()[name = tensor<string, []>("input_93_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_93_dilations_0 = const()[name = tensor<string, []>("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_93_groups_0 = const()[name = tensor<string, []>("input_93_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [64, 64, 3, 3]> const_35_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_35_quantized"), quantized_data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2074112))), scale = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2111168))), zero_point = tensor<int8, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2111040)))]; |
| tensor<fp32, [64]> const_36 = const()[name = tensor<string, []>("const_36"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2111488)))]; |
| tensor<fp32, [?, 64, 40, 499]> out_13 = conv(bias = const_36, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = const_35_quantized, x = input_91)[name = tensor<string, []>("out_13")]; |
| tensor<fp32, [?, 64, 40, 499]> input_95 = add(x = out_13, y = input_85)[name = tensor<string, []>("input_95")]; |
| tensor<fp32, [?, 64, 40, 499]> input_97 = relu(x = input_95)[name = tensor<string, []>("input_97")]; |
| tensor<string, []> input_99_pad_type_0 = const()[name = tensor<string, []>("input_99_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_99_pad_0 = const()[name = tensor<string, []>("input_99_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_99_strides_0 = const()[name = tensor<string, []>("input_99_strides_0"), val = tensor<int32, [2]>([2, 2])]; |
| tensor<int32, [2]> input_99_dilations_0 = const()[name = tensor<string, []>("input_99_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_99_groups_0 = const()[name = tensor<string, []>("input_99_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 64, 3, 3]> const_37_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_37_quantized"), quantized_data = tensor<int8, [128, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2111808))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185792))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_38 = const()[name = tensor<string, []>("const_38"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2186368)))]; |
| tensor<fp32, [?, 128, 20, 250]> input_101 = conv(bias = const_38, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = const_37_quantized, x = input_97)[name = tensor<string, []>("input_101")]; |
| tensor<fp32, [?, 128, 20, 250]> input_103 = relu(x = input_101)[name = tensor<string, []>("input_103")]; |
| tensor<string, []> input_105_pad_type_0 = const()[name = tensor<string, []>("input_105_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_105_pad_0 = const()[name = tensor<string, []>("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_105_strides_0 = const()[name = tensor<string, []>("input_105_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_105_dilations_0 = const()[name = tensor<string, []>("input_105_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_105_groups_0 = const()[name = tensor<string, []>("input_105_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 128, 3, 3]> const_39_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_39_quantized"), quantized_data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2186944))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2334464))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_40 = const()[name = tensor<string, []>("const_40"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2335040)))]; |
| tensor<fp32, [?, 128, 20, 250]> out_15 = conv(bias = const_40, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = const_39_quantized, x = input_103)[name = tensor<string, []>("out_15")]; |
| tensor<string, []> input_107_pad_type_0 = const()[name = tensor<string, []>("input_107_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [2]> input_107_strides_0 = const()[name = tensor<string, []>("input_107_strides_0"), val = tensor<int32, [2]>([2, 2])]; |
| tensor<int32, [4]> input_107_pad_0 = const()[name = tensor<string, []>("input_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_107_dilations_0 = const()[name = tensor<string, []>("input_107_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_107_groups_0 = const()[name = tensor<string, []>("input_107_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 64, 1, 1]> const_41_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_41_quantized"), quantized_data = tensor<int8, [128, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2335616))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2343872))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_42 = const()[name = tensor<string, []>("const_42"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2344448)))]; |
| tensor<fp32, [?, 128, 20, 250]> var_475 = conv(bias = const_42, dilations = input_107_dilations_0, groups = input_107_groups_0, pad = input_107_pad_0, pad_type = input_107_pad_type_0, strides = input_107_strides_0, weight = const_41_quantized, x = input_97)[name = tensor<string, []>("op_475")]; |
| tensor<fp32, [?, 128, 20, 250]> input_109 = add(x = out_15, y = var_475)[name = tensor<string, []>("input_109")]; |
| tensor<fp32, [?, 128, 20, 250]> input_111 = relu(x = input_109)[name = tensor<string, []>("input_111")]; |
| tensor<string, []> input_113_pad_type_0 = const()[name = tensor<string, []>("input_113_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_113_pad_0 = const()[name = tensor<string, []>("input_113_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_113_strides_0 = const()[name = tensor<string, []>("input_113_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_113_dilations_0 = const()[name = tensor<string, []>("input_113_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_113_groups_0 = const()[name = tensor<string, []>("input_113_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 128, 3, 3]> const_43_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_43_quantized"), quantized_data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2345024))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2492544))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_44 = const()[name = tensor<string, []>("const_44"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2493120)))]; |
| tensor<fp32, [?, 128, 20, 250]> input_115 = conv(bias = const_44, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = const_43_quantized, x = input_111)[name = tensor<string, []>("input_115")]; |
| tensor<fp32, [?, 128, 20, 250]> input_117 = relu(x = input_115)[name = tensor<string, []>("input_117")]; |
| tensor<string, []> input_119_pad_type_0 = const()[name = tensor<string, []>("input_119_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_119_pad_0 = const()[name = tensor<string, []>("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_119_strides_0 = const()[name = tensor<string, []>("input_119_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_119_dilations_0 = const()[name = tensor<string, []>("input_119_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_119_groups_0 = const()[name = tensor<string, []>("input_119_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 128, 3, 3]> const_45_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_45_quantized"), quantized_data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2493696))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2641216))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_46 = const()[name = tensor<string, []>("const_46"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2641792)))]; |
| tensor<fp32, [?, 128, 20, 250]> out_17 = conv(bias = const_46, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = const_45_quantized, x = input_117)[name = tensor<string, []>("out_17")]; |
| tensor<fp32, [?, 128, 20, 250]> input_121 = add(x = out_17, y = input_111)[name = tensor<string, []>("input_121")]; |
| tensor<fp32, [?, 128, 20, 250]> input_123 = relu(x = input_121)[name = tensor<string, []>("input_123")]; |
| tensor<string, []> input_125_pad_type_0 = const()[name = tensor<string, []>("input_125_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_125_pad_0 = const()[name = tensor<string, []>("input_125_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_125_strides_0 = const()[name = tensor<string, []>("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_125_dilations_0 = const()[name = tensor<string, []>("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_125_groups_0 = const()[name = tensor<string, []>("input_125_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 128, 3, 3]> const_47_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_47_quantized"), quantized_data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2642368))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2789888))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_48 = const()[name = tensor<string, []>("const_48"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2790464)))]; |
| tensor<fp32, [?, 128, 20, 250]> input_127 = conv(bias = const_48, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = const_47_quantized, x = input_123)[name = tensor<string, []>("input_127")]; |
| tensor<fp32, [?, 128, 20, 250]> input_129 = relu(x = input_127)[name = tensor<string, []>("input_129")]; |
| tensor<string, []> input_131_pad_type_0 = const()[name = tensor<string, []>("input_131_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_131_pad_0 = const()[name = tensor<string, []>("input_131_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_131_strides_0 = const()[name = tensor<string, []>("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_131_dilations_0 = const()[name = tensor<string, []>("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_131_groups_0 = const()[name = tensor<string, []>("input_131_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 128, 3, 3]> const_49_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_49_quantized"), quantized_data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2791040))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2938560))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_50 = const()[name = tensor<string, []>("const_50"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2939136)))]; |
| tensor<fp32, [?, 128, 20, 250]> out_19 = conv(bias = const_50, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = const_49_quantized, x = input_129)[name = tensor<string, []>("out_19")]; |
| tensor<fp32, [?, 128, 20, 250]> input_133 = add(x = out_19, y = input_123)[name = tensor<string, []>("input_133")]; |
| tensor<fp32, [?, 128, 20, 250]> input_135 = relu(x = input_133)[name = tensor<string, []>("input_135")]; |
| tensor<string, []> input_137_pad_type_0 = const()[name = tensor<string, []>("input_137_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_137_pad_0 = const()[name = tensor<string, []>("input_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_137_strides_0 = const()[name = tensor<string, []>("input_137_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_137_dilations_0 = const()[name = tensor<string, []>("input_137_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_137_groups_0 = const()[name = tensor<string, []>("input_137_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 128, 3, 3]> const_51_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_51_quantized"), quantized_data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2939712))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3087232))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_52 = const()[name = tensor<string, []>("const_52"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3087808)))]; |
| tensor<fp32, [?, 128, 20, 250]> input_139 = conv(bias = const_52, dilations = input_137_dilations_0, groups = input_137_groups_0, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = input_137_strides_0, weight = const_51_quantized, x = input_135)[name = tensor<string, []>("input_139")]; |
| tensor<fp32, [?, 128, 20, 250]> input_141 = relu(x = input_139)[name = tensor<string, []>("input_141")]; |
| tensor<string, []> input_143_pad_type_0 = const()[name = tensor<string, []>("input_143_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_143_pad_0 = const()[name = tensor<string, []>("input_143_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_143_strides_0 = const()[name = tensor<string, []>("input_143_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_143_dilations_0 = const()[name = tensor<string, []>("input_143_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_143_groups_0 = const()[name = tensor<string, []>("input_143_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 128, 3, 3]> const_53_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_53_quantized"), quantized_data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3088384))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3235904))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_54 = const()[name = tensor<string, []>("const_54"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3236480)))]; |
| tensor<fp32, [?, 128, 20, 250]> out_21 = conv(bias = const_54, dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = const_53_quantized, x = input_141)[name = tensor<string, []>("out_21")]; |
| tensor<fp32, [?, 128, 20, 250]> input_145 = add(x = out_21, y = input_135)[name = tensor<string, []>("input_145")]; |
| tensor<fp32, [?, 128, 20, 250]> input_147 = relu(x = input_145)[name = tensor<string, []>("input_147")]; |
| tensor<string, []> input_149_pad_type_0 = const()[name = tensor<string, []>("input_149_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_149_pad_0 = const()[name = tensor<string, []>("input_149_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_149_strides_0 = const()[name = tensor<string, []>("input_149_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_149_dilations_0 = const()[name = tensor<string, []>("input_149_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_149_groups_0 = const()[name = tensor<string, []>("input_149_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 128, 3, 3]> const_55_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_55_quantized"), quantized_data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3237056))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3384576))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_56 = const()[name = tensor<string, []>("const_56"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3385152)))]; |
| tensor<fp32, [?, 128, 20, 250]> input_151 = conv(bias = const_56, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = const_55_quantized, x = input_147)[name = tensor<string, []>("input_151")]; |
| tensor<fp32, [?, 128, 20, 250]> input_153 = relu(x = input_151)[name = tensor<string, []>("input_153")]; |
| tensor<string, []> input_155_pad_type_0 = const()[name = tensor<string, []>("input_155_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_155_pad_0 = const()[name = tensor<string, []>("input_155_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_155_strides_0 = const()[name = tensor<string, []>("input_155_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_155_dilations_0 = const()[name = tensor<string, []>("input_155_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_155_groups_0 = const()[name = tensor<string, []>("input_155_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 128, 3, 3]> const_57_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_57_quantized"), quantized_data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3385728))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3533248))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_58 = const()[name = tensor<string, []>("const_58"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3533824)))]; |
| tensor<fp32, [?, 128, 20, 250]> out_23 = conv(bias = const_58, dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = const_57_quantized, x = input_153)[name = tensor<string, []>("out_23")]; |
| tensor<fp32, [?, 128, 20, 250]> input_157 = add(x = out_23, y = input_147)[name = tensor<string, []>("input_157")]; |
| tensor<fp32, [?, 128, 20, 250]> input_159 = relu(x = input_157)[name = tensor<string, []>("input_159")]; |
| tensor<string, []> input_161_pad_type_0 = const()[name = tensor<string, []>("input_161_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_161_pad_0 = const()[name = tensor<string, []>("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_161_strides_0 = const()[name = tensor<string, []>("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_161_dilations_0 = const()[name = tensor<string, []>("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_161_groups_0 = const()[name = tensor<string, []>("input_161_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 128, 3, 3]> const_59_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_59_quantized"), quantized_data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3534400))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3681920))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_60 = const()[name = tensor<string, []>("const_60"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3682496)))]; |
| tensor<fp32, [?, 128, 20, 250]> input_163 = conv(bias = const_60, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_59_quantized, x = input_159)[name = tensor<string, []>("input_163")]; |
| tensor<fp32, [?, 128, 20, 250]> input_165 = relu(x = input_163)[name = tensor<string, []>("input_165")]; |
| tensor<string, []> input_167_pad_type_0 = const()[name = tensor<string, []>("input_167_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_167_pad_0 = const()[name = tensor<string, []>("input_167_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_167_strides_0 = const()[name = tensor<string, []>("input_167_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_167_dilations_0 = const()[name = tensor<string, []>("input_167_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_167_groups_0 = const()[name = tensor<string, []>("input_167_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [128, 128, 3, 3]> const_61_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_61_quantized"), quantized_data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3683072))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3830592))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2185600)))]; |
| tensor<fp32, [128]> const_62 = const()[name = tensor<string, []>("const_62"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3831168)))]; |
| tensor<fp32, [?, 128, 20, 250]> out_25 = conv(bias = const_62, dilations = input_167_dilations_0, groups = input_167_groups_0, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = input_167_strides_0, weight = const_61_quantized, x = input_165)[name = tensor<string, []>("out_25")]; |
| tensor<fp32, [?, 128, 20, 250]> input_169 = add(x = out_25, y = input_159)[name = tensor<string, []>("input_169")]; |
| tensor<fp32, [?, 128, 20, 250]> input_171 = relu(x = input_169)[name = tensor<string, []>("input_171")]; |
| tensor<string, []> input_173_pad_type_0 = const()[name = tensor<string, []>("input_173_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_173_pad_0 = const()[name = tensor<string, []>("input_173_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_173_strides_0 = const()[name = tensor<string, []>("input_173_strides_0"), val = tensor<int32, [2]>([2, 2])]; |
| tensor<int32, [2]> input_173_dilations_0 = const()[name = tensor<string, []>("input_173_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_173_groups_0 = const()[name = tensor<string, []>("input_173_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [256, 128, 3, 3]> const_63_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_63_quantized"), quantized_data = tensor<int8, [256, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3831744))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4126720))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1764224)))]; |
| tensor<fp32, [256]> const_64 = const()[name = tensor<string, []>("const_64"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4127808)))]; |
| tensor<fp32, [?, 256, 10, 125]> input_175 = conv(bias = const_64, dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = const_63_quantized, x = input_171)[name = tensor<string, []>("input_175")]; |
| tensor<fp32, [?, 256, 10, 125]> input_177 = relu(x = input_175)[name = tensor<string, []>("input_177")]; |
| tensor<string, []> input_179_pad_type_0 = const()[name = tensor<string, []>("input_179_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_179_pad_0 = const()[name = tensor<string, []>("input_179_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_179_strides_0 = const()[name = tensor<string, []>("input_179_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_179_dilations_0 = const()[name = tensor<string, []>("input_179_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_179_groups_0 = const()[name = tensor<string, []>("input_179_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [256, 256, 3, 3]> const_65_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_65_quantized"), quantized_data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4128896))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4718784))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1764224)))]; |
| tensor<fp32, [256]> const_66 = const()[name = tensor<string, []>("const_66"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4719872)))]; |
| tensor<fp32, [?, 256, 10, 125]> out_27 = conv(bias = const_66, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = const_65_quantized, x = input_177)[name = tensor<string, []>("out_27")]; |
| tensor<string, []> input_181_pad_type_0 = const()[name = tensor<string, []>("input_181_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [2]> input_181_strides_0 = const()[name = tensor<string, []>("input_181_strides_0"), val = tensor<int32, [2]>([2, 2])]; |
| tensor<int32, [4]> input_181_pad_0 = const()[name = tensor<string, []>("input_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_181_dilations_0 = const()[name = tensor<string, []>("input_181_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_181_groups_0 = const()[name = tensor<string, []>("input_181_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [256, 128, 1, 1]> const_67_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_67_quantized"), quantized_data = tensor<int8, [256, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4720960))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4753792))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1764224)))]; |
| tensor<fp32, [256]> const_68 = const()[name = tensor<string, []>("const_68"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4754880)))]; |
| tensor<fp32, [?, 256, 10, 125]> var_666 = conv(bias = const_68, dilations = input_181_dilations_0, groups = input_181_groups_0, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = input_181_strides_0, weight = const_67_quantized, x = input_171)[name = tensor<string, []>("op_666")]; |
| tensor<fp32, [?, 256, 10, 125]> input_183 = add(x = out_27, y = var_666)[name = tensor<string, []>("input_183")]; |
| tensor<fp32, [?, 256, 10, 125]> input_185 = relu(x = input_183)[name = tensor<string, []>("input_185")]; |
| tensor<string, []> input_187_pad_type_0 = const()[name = tensor<string, []>("input_187_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_187_pad_0 = const()[name = tensor<string, []>("input_187_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_187_strides_0 = const()[name = tensor<string, []>("input_187_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_187_dilations_0 = const()[name = tensor<string, []>("input_187_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_187_groups_0 = const()[name = tensor<string, []>("input_187_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [256, 256, 3, 3]> const_69_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_69_quantized"), quantized_data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4755968))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5345856))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1764224)))]; |
| tensor<fp32, [256]> const_70 = const()[name = tensor<string, []>("const_70"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5346944)))]; |
| tensor<fp32, [?, 256, 10, 125]> input_189 = conv(bias = const_70, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = const_69_quantized, x = input_185)[name = tensor<string, []>("input_189")]; |
| tensor<fp32, [?, 256, 10, 125]> input_191 = relu(x = input_189)[name = tensor<string, []>("input_191")]; |
| tensor<string, []> input_193_pad_type_0 = const()[name = tensor<string, []>("input_193_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_193_pad_0 = const()[name = tensor<string, []>("input_193_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_193_strides_0 = const()[name = tensor<string, []>("input_193_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_193_dilations_0 = const()[name = tensor<string, []>("input_193_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_193_groups_0 = const()[name = tensor<string, []>("input_193_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [256, 256, 3, 3]> const_71_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_71_quantized"), quantized_data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5348032))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5937920))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1764224)))]; |
| tensor<fp32, [256]> const_72 = const()[name = tensor<string, []>("const_72"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5939008)))]; |
| tensor<fp32, [?, 256, 10, 125]> out_29 = conv(bias = const_72, dilations = input_193_dilations_0, groups = input_193_groups_0, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = input_193_strides_0, weight = const_71_quantized, x = input_191)[name = tensor<string, []>("out_29")]; |
| tensor<fp32, [?, 256, 10, 125]> input_195 = add(x = out_29, y = input_185)[name = tensor<string, []>("input_195")]; |
| tensor<fp32, [?, 256, 10, 125]> input_197 = relu(x = input_195)[name = tensor<string, []>("input_197")]; |
| tensor<string, []> input_199_pad_type_0 = const()[name = tensor<string, []>("input_199_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_199_pad_0 = const()[name = tensor<string, []>("input_199_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_199_strides_0 = const()[name = tensor<string, []>("input_199_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_199_dilations_0 = const()[name = tensor<string, []>("input_199_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_199_groups_0 = const()[name = tensor<string, []>("input_199_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [256, 256, 3, 3]> const_73_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_73_quantized"), quantized_data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5940096))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6529984))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1764224)))]; |
| tensor<fp32, [256]> const_74 = const()[name = tensor<string, []>("const_74"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6531072)))]; |
| tensor<fp32, [?, 256, 10, 125]> input_201 = conv(bias = const_74, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = const_73_quantized, x = input_197)[name = tensor<string, []>("input_201")]; |
| tensor<fp32, [?, 256, 10, 125]> input_203 = relu(x = input_201)[name = tensor<string, []>("input_203")]; |
| tensor<string, []> input_205_pad_type_0 = const()[name = tensor<string, []>("input_205_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_205_pad_0 = const()[name = tensor<string, []>("input_205_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_205_strides_0 = const()[name = tensor<string, []>("input_205_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_205_dilations_0 = const()[name = tensor<string, []>("input_205_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_205_groups_0 = const()[name = tensor<string, []>("input_205_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [256, 256, 3, 3]> const_75_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_75_quantized"), quantized_data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6532160))), scale = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7122048))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1764224)))]; |
| tensor<fp32, [256]> const_76 = const()[name = tensor<string, []>("const_76"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7123136)))]; |
| tensor<fp32, [?, 256, 10, 125]> out = conv(bias = const_76, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_75_quantized, x = input_203)[name = tensor<string, []>("out")]; |
| tensor<fp32, [?, 256, 10, 125]> input_207 = add(x = out, y = input_197)[name = tensor<string, []>("input_207")]; |
| tensor<fp32, [?, 256, 10, 125]> features = relu(x = input_207)[name = tensor<string, []>("features")]; |
| tensor<int32, [3]> concat_4x = const()[name = tensor<string, []>("concat_4x"), val = tensor<int32, [3]>([-1, 2560, 125])]; |
| tensor<fp32, [?, 2560, 125]> sequences = reshape(shape = concat_4x, x = features)[name = tensor<string, []>("sequences")]; |
| tensor<int32, [1]> weights_5_axes_0 = const()[name = tensor<string, []>("weights_5_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp32, [?, 1, 125]> weights_5 = expand_dims(axes = weights_5_axes_0, x = weights_3)[name = tensor<string, []>("weights_5")]; |
| tensor<int32, [1]> weights_axes_0 = const()[name = tensor<string, []>("weights_axes_0"), val = tensor<int32, [1]>([2])]; |
| tensor<fp32, [?, 1, 1, 125]> weights = expand_dims(axes = weights_axes_0, x = weights_5)[name = tensor<string, []>("weights")]; |
| tensor<int32, [1]> var_740_axes_0 = const()[name = tensor<string, []>("op_740_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<bool, []> var_740_keep_dims_0 = const()[name = tensor<string, []>("op_740_keep_dims_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [?, 1, 1]> var_740 = reduce_sum(axes = var_740_axes_0, keep_dims = var_740_keep_dims_0, x = weights)[name = tensor<string, []>("op_740")]; |
| tensor<fp32, []> var_741 = const()[name = tensor<string, []>("op_741"), val = tensor<fp32, []>(0x1.5798eep-27)]; |
| tensor<fp32, [?, 1, 1]> v1 = add(x = var_740, y = var_741)[name = tensor<string, []>("v1")]; |
| tensor<int32, [1]> var_743_axes_0 = const()[name = tensor<string, []>("op_743_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp32, [?, 1, 2560, 125]> var_743 = expand_dims(axes = var_743_axes_0, x = sequences)[name = tensor<string, []>("op_743")]; |
| tensor<fp32, [?, 1, 2560, 125]> weighted = mul(x = var_743, y = weights)[name = tensor<string, []>("weighted")]; |
| tensor<int32, [1]> var_746_axes_0 = const()[name = tensor<string, []>("op_746_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<bool, []> var_746_keep_dims_0 = const()[name = tensor<string, []>("op_746_keep_dims_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [?, 1, 2560]> var_746 = reduce_sum(axes = var_746_axes_0, keep_dims = var_746_keep_dims_0, x = weighted)[name = tensor<string, []>("op_746")]; |
| tensor<fp32, [?, 1, 2560]> mean = real_div(x = var_746, y = v1)[name = tensor<string, []>("mean")]; |
| tensor<int32, [1]> var_749_axes_0 = const()[name = tensor<string, []>("op_749_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [?, 1, 2560, 1]> var_749 = expand_dims(axes = var_749_axes_0, x = mean)[name = tensor<string, []>("op_749")]; |
| tensor<fp32, [?, 1, 2560, 125]> diff = sub(x = var_743, y = var_749)[name = tensor<string, []>("diff")]; |
| tensor<fp32, [?, 1, 1, 125]> var_751 = mul(x = weights, y = weights)[name = tensor<string, []>("op_751")]; |
| tensor<int32, [1]> v2_axes_0 = const()[name = tensor<string, []>("v2_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<bool, []> v2_keep_dims_0 = const()[name = tensor<string, []>("v2_keep_dims_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [?, 1, 1]> v2 = reduce_sum(axes = v2_axes_0, keep_dims = v2_keep_dims_0, x = var_751)[name = tensor<string, []>("v2")]; |
| tensor<fp32, [?, 1, 1]> var_754 = real_div(x = v2, y = v1)[name = tensor<string, []>("op_754")]; |
| tensor<fp32, [?, 1, 1]> var_755 = sub(x = v1, y = var_754)[name = tensor<string, []>("op_755")]; |
| tensor<fp32, []> var_756 = const()[name = tensor<string, []>("op_756"), val = tensor<fp32, []>(0x1.5798eep-27)]; |
| tensor<fp32, [?, 1, 1]> denom = add(x = var_755, y = var_756)[name = tensor<string, []>("denom")]; |
| tensor<fp32, [?, 1, 2560, 125]> var_758 = mul(x = diff, y = diff)[name = tensor<string, []>("op_758")]; |
| tensor<fp32, [?, 1, 2560, 125]> var_759 = mul(x = var_758, y = weights)[name = tensor<string, []>("op_759")]; |
| tensor<int32, [1]> var_761_axes_0 = const()[name = tensor<string, []>("op_761_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<bool, []> var_761_keep_dims_0 = const()[name = tensor<string, []>("op_761_keep_dims_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [?, 1, 2560]> var_761 = reduce_sum(axes = var_761_axes_0, keep_dims = var_761_keep_dims_0, x = var_759)[name = tensor<string, []>("op_761")]; |
| tensor<fp32, [?, 1, 2560]> var = real_div(x = var_761, y = denom)[name = tensor<string, []>("var")]; |
| tensor<fp32, []> const_3 = const()[name = tensor<string, []>("const_3"), val = tensor<fp32, []>(0x1.fffffep+127)]; |
| tensor<fp32, [?, 1, 2560]> clip_1 = clip(alpha = var_177, beta = const_3, x = var)[name = tensor<string, []>("clip_1")]; |
| tensor<fp32, [?, 1, 2560]> std = sqrt(x = clip_1)[name = tensor<string, []>("std")]; |
| tensor<bool, []> output_interleave_0 = const()[name = tensor<string, []>("output_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [?, 1, 5120]> output = concat(axis = var_176, interleave = output_interleave_0, values = (mean, std))[name = tensor<string, []>("output")]; |
| tensor<int32, [1]> stats_axes_0 = const()[name = tensor<string, []>("stats_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp32, [?, 5120]> stats = squeeze(axes = stats_axes_0, x = output)[name = tensor<string, []>("stats")]; |
| tensor<int32, [1]> var_768_axes_0 = const()[name = tensor<string, []>("op_768_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [?, 5120, 1]> var_768 = expand_dims(axes = var_768_axes_0, x = stats)[name = tensor<string, []>("op_768")]; |
| tensor<int32, [1]> input_209_axes_0 = const()[name = tensor<string, []>("input_209_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [?, 5120, 1, 1]> input_209 = expand_dims(axes = input_209_axes_0, x = var_768)[name = tensor<string, []>("input_209")]; |
| tensor<string, []> var_776_pad_type_0 = const()[name = tensor<string, []>("op_776_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [2]> var_776_strides_0 = const()[name = tensor<string, []>("op_776_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> var_776_pad_0 = const()[name = tensor<string, []>("op_776_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> var_776_dilations_0 = const()[name = tensor<string, []>("op_776_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> var_776_groups_0 = const()[name = tensor<string, []>("op_776_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [?, 256, 1, 1]> var_776 = conv(bias = resnet_seg_1_bias, dilations = var_776_dilations_0, groups = var_776_groups_0, pad = var_776_pad_0, pad_type = var_776_pad_type_0, strides = var_776_strides_0, weight = resnet_seg_1_weight_quantized, x = input_209)[name = tensor<string, []>("op_776")]; |
| tensor<int32, [2]> concat_5x = const()[name = tensor<string, []>("concat_5x"), val = tensor<int32, [2]>([-1, 256])]; |
| tensor<fp32, [?, 256]> input = reshape(shape = concat_5x, x = var_776)[name = tensor<string, []>("input")]; |
| tensor<int32, [1]> var_780 = const()[name = tensor<string, []>("op_780"), val = tensor<int32, [1]>([-1])]; |
| tensor<bool, []> var_781 = const()[name = tensor<string, []>("op_781"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [?, 1]> norms_1 = reduce_l2_norm(axes = var_780, keep_dims = var_781, x = input)[name = tensor<string, []>("norms_1")]; |
| tensor<fp32, []> var_784 = const()[name = tensor<string, []>("op_784"), val = tensor<fp32, []>(0x1.197998p-40)]; |
| tensor<fp32, []> const_4 = const()[name = tensor<string, []>("const_4"), val = tensor<fp32, []>(0x1.fffffep+127)]; |
| tensor<fp32, [?, 1]> clip_2 = clip(alpha = var_784, beta = const_4, x = norms_1)[name = tensor<string, []>("clip_2")]; |
| tensor<fp32, [?, 256]> embedding = real_div(x = input, y = clip_2)[name = tensor<string, []>("op_787")]; |
| } -> (embedding); |
| } |
