| program(1.0) |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.13.0+cu130"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] |
| { |
| func main<ios17>(tensor<int32, [16]> boundary_kind, tensor<int32, [16]> language_id, tensor<fp16, [16, 1, 40, 81]> mel, tensor<int32, [16, 32]> text_bytes) { |
| tensor<int32, []> var_12 = const()[name = tensor<string, []>("op_12"), val = tensor<int32, []>(1)]; |
| tensor<string, []> input_1_pad_type_0 = const()[name = tensor<string, []>("input_1_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_1_pad_0 = const()[name = tensor<string, []>("input_1_pad_0"), val = tensor<int32, [4]>([2, 2, 2, 2])]; |
| tensor<int32, [2]> input_1_strides_0 = const()[name = tensor<string, []>("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_1_dilations_0 = const()[name = tensor<string, []>("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_1_groups_0 = const()[name = tensor<string, []>("input_1_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [16, 1, 5, 5]> const_3_to_fp16 = const()[name = tensor<string, []>("const_3_to_fp16"), val = tensor<fp16, [16, 1, 5, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; |
| tensor<fp16, [16]> const_4_to_fp16 = const()[name = tensor<string, []>("const_4_to_fp16"), val = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(960)))]; |
| tensor<fp16, [16, 16, 40, 81]> input_3_cast_fp16 = conv(bias = const_4_to_fp16, dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = const_3_to_fp16, x = mel)[name = tensor<string, []>("input_3_cast_fp16")]; |
| tensor<fp16, [16, 16, 40, 81]> input_5_cast_fp16 = silu(x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")]; |
| tensor<int32, [2]> var_45 = const()[name = tensor<string, []>("op_45"), val = tensor<int32, [2]>([2, 1])]; |
| tensor<int32, [2]> var_46 = const()[name = tensor<string, []>("op_46"), val = tensor<int32, [2]>([2, 1])]; |
| 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]>([0, 0, 0, 0])]; |
| tensor<bool, []> input_7_ceil_mode_0 = const()[name = tensor<string, []>("input_7_ceil_mode_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [16, 16, 20, 81]> input_7_cast_fp16 = max_pool(ceil_mode = input_7_ceil_mode_0, kernel_sizes = var_45, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_46, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")]; |
| tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [32, 16, 3, 3]> const_5_to_fp16 = const()[name = tensor<string, []>("const_5_to_fp16"), val = tensor<fp16, [32, 16, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1088)))]; |
| tensor<fp16, [32]> const_6_to_fp16 = const()[name = tensor<string, []>("const_6_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10368)))]; |
| tensor<fp16, [16, 32, 20, 81]> input_11_cast_fp16 = conv(bias = const_6_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_5_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")]; |
| tensor<fp16, [16, 32, 20, 81]> input_13_cast_fp16 = silu(x = input_11_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")]; |
| tensor<int32, [2]> var_63 = const()[name = tensor<string, []>("op_63"), val = tensor<int32, [2]>([2, 1])]; |
| tensor<int32, [2]> var_64 = const()[name = tensor<string, []>("op_64"), val = tensor<int32, [2]>([2, 1])]; |
| tensor<string, []> input_15_pad_type_0 = const()[name = tensor<string, []>("input_15_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_15_pad_0 = const()[name = tensor<string, []>("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<bool, []> input_15_ceil_mode_0 = const()[name = tensor<string, []>("input_15_ceil_mode_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [16, 32, 10, 81]> input_15_cast_fp16 = max_pool(ceil_mode = input_15_ceil_mode_0, kernel_sizes = var_63, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = var_64, x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")]; |
| tensor<string, []> input_17_pad_type_0 = const()[name = tensor<string, []>("input_17_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_17_strides_0 = const()[name = tensor<string, []>("input_17_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_17_dilations_0 = const()[name = tensor<string, []>("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_17_groups_0 = const()[name = tensor<string, []>("input_17_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [48, 32, 3, 3]> const_7_to_fp16 = const()[name = tensor<string, []>("const_7_to_fp16"), val = tensor<fp16, [48, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10496)))]; |
| tensor<fp16, [48]> const_8_to_fp16 = const()[name = tensor<string, []>("const_8_to_fp16"), val = tensor<fp16, [48]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38208)))]; |
| tensor<fp16, [16, 48, 10, 81]> input_19_cast_fp16 = conv(bias = const_8_to_fp16, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = const_7_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("input_19_cast_fp16")]; |
| tensor<fp16, [16, 48, 10, 81]> input_21_cast_fp16 = silu(x = input_19_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")]; |
| tensor<int32, [2]> var_81 = const()[name = tensor<string, []>("op_81"), val = tensor<int32, [2]>([2, 1])]; |
| tensor<int32, [2]> var_82 = const()[name = tensor<string, []>("op_82"), val = tensor<int32, [2]>([2, 1])]; |
| tensor<string, []> x_pad_type_0 = const()[name = tensor<string, []>("x_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> x_pad_0 = const()[name = tensor<string, []>("x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<bool, []> x_ceil_mode_0 = const()[name = tensor<string, []>("x_ceil_mode_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [16, 48, 5, 81]> x_cast_fp16 = max_pool(ceil_mode = x_ceil_mode_0, kernel_sizes = var_81, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_82, x = input_21_cast_fp16)[name = tensor<string, []>("x_cast_fp16")]; |
| tensor<int32, [1]> audio_1_axes_0 = const()[name = tensor<string, []>("audio_1_axes_0"), val = tensor<int32, [1]>([2])]; |
| tensor<bool, []> audio_1_keep_dims_0 = const()[name = tensor<string, []>("audio_1_keep_dims_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [16, 48, 81]> audio_1_cast_fp16 = reduce_mean(axes = audio_1_axes_0, keep_dims = audio_1_keep_dims_0, x = x_cast_fp16)[name = tensor<string, []>("audio_1_cast_fp16")]; |
| tensor<int32, []> e_1_batch_dims_0 = const()[name = tensor<string, []>("e_1_batch_dims_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> e_1_validate_indices_0 = const()[name = tensor<string, []>("e_1_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [257, 12]> m_lex_byte_emb_weight_to_fp16 = const()[name = tensor<string, []>("m_lex_byte_emb_weight_to_fp16"), val = tensor<fp16, [257, 12]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38400)))]; |
| tensor<string, []> text_bytes_to_int16_dtype_0 = const()[name = tensor<string, []>("text_bytes_to_int16_dtype_0"), val = tensor<string, []>("int16")]; |
| tensor<string, []> cast_5_dtype_0 = const()[name = tensor<string, []>("cast_5_dtype_0"), val = tensor<string, []>("int32")]; |
| tensor<int32, []> greater_equal_0_y_0 = const()[name = tensor<string, []>("greater_equal_0_y_0"), val = tensor<int32, []>(0)]; |
| tensor<int16, [16, 32]> text_bytes_to_int16 = cast(dtype = text_bytes_to_int16_dtype_0, x = text_bytes)[name = tensor<string, []>("cast_10")]; |
| tensor<int32, [16, 32]> cast_5 = cast(dtype = cast_5_dtype_0, x = text_bytes_to_int16)[name = tensor<string, []>("cast_9")]; |
| tensor<bool, [16, 32]> greater_equal_0 = greater_equal(x = cast_5, y = greater_equal_0_y_0)[name = tensor<string, []>("greater_equal_0")]; |
| tensor<int32, []> slice_by_index_0 = const()[name = tensor<string, []>("slice_by_index_0"), val = tensor<int32, []>(257)]; |
| tensor<int32, [16, 32]> add_0 = add(x = cast_5, y = slice_by_index_0)[name = tensor<string, []>("add_0")]; |
| tensor<int32, [16, 32]> select_0 = select(a = cast_5, b = add_0, cond = greater_equal_0)[name = tensor<string, []>("select_0")]; |
| tensor<int32, []> e_1_cast_fp16_cast_uint16_axis_0 = const()[name = tensor<string, []>("e_1_cast_fp16_cast_uint16_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<string, []> select_0_to_int16_dtype_0 = const()[name = tensor<string, []>("select_0_to_int16_dtype_0"), val = tensor<string, []>("int16")]; |
| tensor<int16, [16, 32]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = tensor<string, []>("cast_8")]; |
| tensor<fp16, [16, 32, 12]> e_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = e_1_cast_fp16_cast_uint16_axis_0, batch_dims = e_1_batch_dims_0, indices = select_0_to_int16, validate_indices = e_1_validate_indices_0, x = m_lex_byte_emb_weight_to_fp16)[name = tensor<string, []>("e_1_cast_fp16_cast_uint16_cast_uint16")]; |
| tensor<int32, [2]> var_94 = const()[name = tensor<string, []>("op_94"), val = tensor<int32, [2]>([16, -1])]; |
| tensor<fp16, [16, 384]> e_cast_fp16 = reshape(shape = var_94, x = e_1_cast_fp16_cast_uint16_cast_uint16)[name = tensor<string, []>("e_cast_fp16")]; |
| tensor<int32, []> lang_axis_0 = const()[name = tensor<string, []>("lang_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<int32, []> lang_batch_dims_0 = const()[name = tensor<string, []>("lang_batch_dims_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> lang_validate_indices_0 = const()[name = tensor<string, []>("lang_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [9, 8]> m_lex_lang_emb_weight_to_fp16 = const()[name = tensor<string, []>("m_lex_lang_emb_weight_to_fp16"), val = tensor<fp16, [9, 8]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44672)))]; |
| tensor<string, []> language_id_to_uint16_dtype_0 = const()[name = tensor<string, []>("language_id_to_uint16_dtype_0"), val = tensor<string, []>("uint16")]; |
| tensor<uint16, [16]> language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = tensor<string, []>("cast_7")]; |
| tensor<fp16, [16, 8]> lang_cast_fp16_cast_uint16 = gather(axis = lang_axis_0, batch_dims = lang_batch_dims_0, indices = language_id_to_uint16, validate_indices = lang_validate_indices_0, x = m_lex_lang_emb_weight_to_fp16)[name = tensor<string, []>("lang_cast_fp16_cast_uint16")]; |
| tensor<int32, []> var_98_one_hot_vector_size_0 = const()[name = tensor<string, []>("op_98_one_hot_vector_size_0"), val = tensor<int32, []>(2)]; |
| tensor<int32, []> var_98_axis_0 = const()[name = tensor<string, []>("op_98_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<int32, []> var_98_on_value_0 = const()[name = tensor<string, []>("op_98_on_value_0"), val = tensor<int32, []>(1)]; |
| tensor<int32, []> var_98_off_value_0 = const()[name = tensor<string, []>("op_98_off_value_0"), val = tensor<int32, []>(0)]; |
| tensor<int32, [16, 2]> var_98 = one_hot(axis = var_98_axis_0, indices = boundary_kind, off_value = var_98_off_value_0, on_value = var_98_on_value_0, one_hot_vector_size = var_98_one_hot_vector_size_0)[name = tensor<string, []>("op_98")]; |
| tensor<bool, []> input_23_interleave_0 = const()[name = tensor<string, []>("input_23_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<string, []> kind_to_fp16_dtype_0 = const()[name = tensor<string, []>("kind_to_fp16_dtype_0"), val = tensor<string, []>("fp16")]; |
| tensor<fp16, [16, 2]> var_98_to_fp16 = cast(dtype = kind_to_fp16_dtype_0, x = var_98)[name = tensor<string, []>("cast_6")]; |
| tensor<fp16, [16, 394]> input_23_cast_fp16 = concat(axis = var_12, interleave = input_23_interleave_0, values = (e_cast_fp16, lang_cast_fp16_cast_uint16, var_98_to_fp16))[name = tensor<string, []>("input_23_cast_fp16")]; |
| tensor<fp16, [64, 394]> m_lex_head_0_weight_to_fp16 = const()[name = tensor<string, []>("m_lex_head_0_weight_to_fp16"), val = tensor<fp16, [64, 394]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44928)))]; |
| tensor<fp16, [64]> m_lex_head_0_bias_to_fp16 = const()[name = tensor<string, []>("m_lex_head_0_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95424)))]; |
| tensor<fp16, [16, 64]> linear_0_cast_fp16 = linear(bias = m_lex_head_0_bias_to_fp16, weight = m_lex_head_0_weight_to_fp16, x = input_23_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")]; |
| tensor<fp16, [16, 64]> input_27_cast_fp16 = silu(x = linear_0_cast_fp16)[name = tensor<string, []>("input_27_cast_fp16")]; |
| tensor<fp16, [48, 64]> m_lex_head_3_weight_to_fp16 = const()[name = tensor<string, []>("m_lex_head_3_weight_to_fp16"), val = tensor<fp16, [48, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95616)))]; |
| tensor<fp16, [48]> m_lex_head_3_bias_to_fp16 = const()[name = tensor<string, []>("m_lex_head_3_bias_to_fp16"), val = tensor<fp16, [48]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101824)))]; |
| tensor<fp16, [16, 48]> linear_1_cast_fp16 = linear(bias = m_lex_head_3_bias_to_fp16, weight = m_lex_head_3_weight_to_fp16, x = input_27_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")]; |
| tensor<fp16, [16, 48]> var_112_cast_fp16 = silu(x = linear_1_cast_fp16)[name = tensor<string, []>("op_112_cast_fp16")]; |
| tensor<int32, [1]> var_113_axes_0 = const()[name = tensor<string, []>("op_113_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [16, 48, 1]> var_113_cast_fp16 = expand_dims(axes = var_113_axes_0, x = var_112_cast_fp16)[name = tensor<string, []>("op_113_cast_fp16")]; |
| tensor<int32, [3]> lexv_reps_0 = const()[name = tensor<string, []>("lexv_reps_0"), val = tensor<int32, [3]>([1, 1, 81])]; |
| tensor<fp16, [16, 48, 81]> lexv_cast_fp16 = tile(reps = lexv_reps_0, x = var_113_cast_fp16)[name = tensor<string, []>("lexv_cast_fp16")]; |
| tensor<bool, []> input_33_interleave_0 = const()[name = tensor<string, []>("input_33_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [16, 2, 81]> pos_1_to_fp16 = const()[name = tensor<string, []>("pos_1_to_fp16"), val = tensor<fp16, [16, 2, 81]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102016)))]; |
| tensor<fp16, [16, 98, 81]> input_33_cast_fp16 = concat(axis = var_12, interleave = input_33_interleave_0, values = (audio_1_cast_fp16, lexv_cast_fp16, pos_1_to_fp16))[name = tensor<string, []>("input_33_cast_fp16")]; |
| tensor<string, []> input_35_pad_type_0 = const()[name = tensor<string, []>("input_35_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> input_35_pad_0 = const()[name = tensor<string, []>("input_35_pad_0"), val = tensor<int32, [2]>([2, 2])]; |
| tensor<int32, [1]> input_35_strides_0 = const()[name = tensor<string, []>("input_35_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> input_35_dilations_0 = const()[name = tensor<string, []>("input_35_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_35_groups_0 = const()[name = tensor<string, []>("input_35_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [96, 98, 5]> const_9_to_fp16 = const()[name = tensor<string, []>("const_9_to_fp16"), val = tensor<fp16, [96, 98, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107264)))]; |
| tensor<fp16, [96]> const_10_to_fp16 = const()[name = tensor<string, []>("const_10_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201408)))]; |
| tensor<fp16, [16, 96, 81]> input_37_cast_fp16 = conv(bias = const_10_to_fp16, dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = const_9_to_fp16, x = input_33_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")]; |
| tensor<fp16, [16, 96, 81]> input_39_cast_fp16 = silu(x = input_37_cast_fp16)[name = tensor<string, []>("input_39_cast_fp16")]; |
| tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [2]>([2, 2])]; |
| tensor<int32, [1]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [48, 96, 5]> m_temporal_net_4_weight_to_fp16 = const()[name = tensor<string, []>("m_temporal_net_4_weight_to_fp16"), val = tensor<fp16, [48, 96, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201664)))]; |
| tensor<fp16, [48]> m_temporal_net_4_bias_to_fp16 = const()[name = tensor<string, []>("m_temporal_net_4_bias_to_fp16"), val = tensor<fp16, [48]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(247808)))]; |
| tensor<fp16, [16, 48, 81]> input_43_cast_fp16 = conv(bias = m_temporal_net_4_bias_to_fp16, 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 = m_temporal_net_4_weight_to_fp16, x = input_39_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")]; |
| tensor<fp16, [16, 48, 81]> input_cast_fp16 = silu(x = input_43_cast_fp16)[name = tensor<string, []>("input_cast_fp16")]; |
| tensor<string, []> var_154_pad_type_0 = const()[name = tensor<string, []>("op_154_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> var_154_strides_0 = const()[name = tensor<string, []>("op_154_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_154_pad_0 = const()[name = tensor<string, []>("op_154_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_154_dilations_0 = const()[name = tensor<string, []>("op_154_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> var_154_groups_0 = const()[name = tensor<string, []>("op_154_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [1, 48, 1]> m_temporal_net_6_weight_to_fp16 = const()[name = tensor<string, []>("m_temporal_net_6_weight_to_fp16"), val = tensor<fp16, [1, 48, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248000)))]; |
| tensor<fp16, [1]> m_temporal_net_6_bias_to_fp16 = const()[name = tensor<string, []>("m_temporal_net_6_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.69cp+3])]; |
| tensor<fp16, [16, 1, 81]> var_154_cast_fp16 = conv(bias = m_temporal_net_6_bias_to_fp16, dilations = var_154_dilations_0, groups = var_154_groups_0, pad = var_154_pad_0, pad_type = var_154_pad_type_0, strides = var_154_strides_0, weight = m_temporal_net_6_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("op_154_cast_fp16")]; |
| tensor<int32, [1]> var_155_axes_0 = const()[name = tensor<string, []>("op_155_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp16, [16, 81]> var_155 = squeeze(axes = var_155_axes_0, x = var_154_cast_fp16)[name = tensor<string, []>("op_155_cast_fp16")]; |
| } -> (var_155); |
| } |
