| program(1.0) |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.12.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] |
| { |
| func main<ios17>(tensor<fp32, [1, 100, 282]> mel) { |
| tensor<string, []> x_1_pad_type_0 = const()[name = tensor<string, []>("x_1_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_1_pad_0 = const()[name = tensor<string, []>("x_1_pad_0"), val = tensor<int32, [2]>([3, 3])]; |
| tensor<int32, [1]> x_1_strides_0 = const()[name = tensor<string, []>("x_1_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_1_dilations_0 = const()[name = tensor<string, []>("x_1_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> x_1_groups_0 = const()[name = tensor<string, []>("x_1_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<string, []> mel_to_fp16_dtype_0 = const()[name = tensor<string, []>("mel_to_fp16_dtype_0"), val = tensor<string, []>("fp16")]; |
| tensor<fp16, [512, 100, 7]> backbone_embed_weight_to_fp16 = const()[name = tensor<string, []>("backbone_embed_weight_to_fp16"), val = tensor<fp16, [512, 100, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; |
| tensor<fp16, [512]> backbone_embed_bias_to_fp16 = const()[name = tensor<string, []>("backbone_embed_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(716928)))]; |
| tensor<fp16, [1, 100, 282]> mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = tensor<string, []>("cast_10")]; |
| tensor<fp16, [1, 512, 282]> x_1_cast_fp16 = conv(bias = backbone_embed_bias_to_fp16, dilations = x_1_dilations_0, groups = x_1_groups_0, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = x_1_strides_0, weight = backbone_embed_weight_to_fp16, x = mel_to_fp16)[name = tensor<string, []>("x_1_cast_fp16")]; |
| tensor<int32, [3]> input_1_perm_0 = const()[name = tensor<string, []>("input_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> x_3_axes_0 = const()[name = tensor<string, []>("x_3_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [512]> backbone_norm_weight_to_fp16 = const()[name = tensor<string, []>("backbone_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(718016)))]; |
| tensor<fp16, [512]> backbone_norm_bias_to_fp16 = const()[name = tensor<string, []>("backbone_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(719104)))]; |
| tensor<fp16, []> var_10_to_fp16 = const()[name = tensor<string, []>("op_10_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)]; |
| tensor<fp16, [1, 282, 512]> input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = x_1_cast_fp16)[name = tensor<string, []>("transpose_31")]; |
| tensor<fp16, [1, 282, 512]> x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, beta = backbone_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_norm_weight_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("x_3_cast_fp16")]; |
| tensor<string, []> x_5_pad_type_0 = const()[name = tensor<string, []>("x_5_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_5_pad_0 = const()[name = tensor<string, []>("x_5_pad_0"), val = tensor<int32, [2]>([3, 3])]; |
| tensor<int32, []> x_5_groups_0 = const()[name = tensor<string, []>("x_5_groups_0"), val = tensor<int32, []>(512)]; |
| tensor<int32, [1]> x_5_strides_0 = const()[name = tensor<string, []>("x_5_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_5_dilations_0 = const()[name = tensor<string, []>("x_5_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [3]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 1, 7]> backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720192)))]; |
| tensor<fp16, [512]> backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(727424)))]; |
| tensor<fp16, [1, 512, 282]> transpose_4_cast_fp16 = transpose(perm = transpose_4_perm_0, x = x_3_cast_fp16)[name = tensor<string, []>("transpose_30")]; |
| tensor<fp16, [1, 512, 282]> x_5_cast_fp16 = conv(bias = backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = backbone_convnext_0_dwconv_weight_to_fp16, x = transpose_4_cast_fp16)[name = tensor<string, []>("x_5_cast_fp16")]; |
| tensor<int32, [3]> input_5_perm_0 = const()[name = tensor<string, []>("input_5_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> input_7_axes_0 = const()[name = tensor<string, []>("input_7_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [512]> backbone_convnext_0_norm_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_0_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(728512)))]; |
| tensor<fp16, [512]> backbone_convnext_0_norm_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_0_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(729600)))]; |
| tensor<fp16, [1, 282, 512]> input_5_cast_fp16 = transpose(perm = input_5_perm_0, x = x_5_cast_fp16)[name = tensor<string, []>("transpose_29")]; |
| tensor<fp16, [1, 282, 512]> input_7_cast_fp16 = layer_norm(axes = input_7_axes_0, beta = backbone_convnext_0_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_0_norm_weight_to_fp16, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")]; |
| tensor<fp16, [1536, 512]> backbone_convnext_0_pwconv1_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(730688)))]; |
| tensor<fp16, [1536]> backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2303616)))]; |
| tensor<fp16, [1, 282, 1536]> linear_0_cast_fp16 = linear(bias = backbone_convnext_0_pwconv1_bias_to_fp16, weight = backbone_convnext_0_pwconv1_weight_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")]; |
| tensor<string, []> input_11_mode_0 = const()[name = tensor<string, []>("input_11_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 282, 1536]> input_11_cast_fp16 = gelu(mode = input_11_mode_0, x = linear_0_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")]; |
| tensor<fp16, [512, 1536]> backbone_convnext_0_pwconv2_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_0_pwconv2_weight_to_fp16"), val = tensor<fp16, [512, 1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2306752)))]; |
| tensor<fp16, [512]> backbone_convnext_0_pwconv2_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_0_pwconv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3879680)))]; |
| tensor<fp16, [1, 282, 512]> linear_1_cast_fp16 = linear(bias = backbone_convnext_0_pwconv2_bias_to_fp16, weight = backbone_convnext_0_pwconv2_weight_to_fp16, x = input_11_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")]; |
| tensor<fp16, [512]> backbone_convnext_0_gamma_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_0_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3880768)))]; |
| tensor<fp16, [1, 282, 512]> x_9_cast_fp16 = mul(x = backbone_convnext_0_gamma_to_fp16, y = linear_1_cast_fp16)[name = tensor<string, []>("x_9_cast_fp16")]; |
| tensor<fp16, [1, 282, 512]> input_13_cast_fp16 = add(x = x_3_cast_fp16, y = x_9_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")]; |
| tensor<string, []> x_13_pad_type_0 = const()[name = tensor<string, []>("x_13_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_13_pad_0 = const()[name = tensor<string, []>("x_13_pad_0"), val = tensor<int32, [2]>([3, 3])]; |
| tensor<int32, []> x_13_groups_0 = const()[name = tensor<string, []>("x_13_groups_0"), val = tensor<int32, []>(512)]; |
| tensor<int32, [1]> x_13_strides_0 = const()[name = tensor<string, []>("x_13_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_13_dilations_0 = const()[name = tensor<string, []>("x_13_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [3]> transpose_5_perm_0 = const()[name = tensor<string, []>("transpose_5_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 1, 7]> backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3881856)))]; |
| tensor<fp16, [512]> backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3889088)))]; |
| tensor<fp16, [1, 512, 282]> transpose_5_cast_fp16 = transpose(perm = transpose_5_perm_0, x = input_13_cast_fp16)[name = tensor<string, []>("transpose_28")]; |
| tensor<fp16, [1, 512, 282]> x_13_cast_fp16 = conv(bias = backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_13_dilations_0, groups = x_13_groups_0, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = x_13_strides_0, weight = backbone_convnext_1_dwconv_weight_to_fp16, x = transpose_5_cast_fp16)[name = tensor<string, []>("x_13_cast_fp16")]; |
| tensor<int32, [3]> input_15_perm_0 = const()[name = tensor<string, []>("input_15_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> input_17_axes_0 = const()[name = tensor<string, []>("input_17_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [512]> backbone_convnext_1_norm_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_1_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3890176)))]; |
| tensor<fp16, [512]> backbone_convnext_1_norm_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_1_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3891264)))]; |
| tensor<fp16, [1, 282, 512]> input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = x_13_cast_fp16)[name = tensor<string, []>("transpose_27")]; |
| tensor<fp16, [1, 282, 512]> input_17_cast_fp16 = layer_norm(axes = input_17_axes_0, beta = backbone_convnext_1_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_1_norm_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")]; |
| tensor<fp16, [1536, 512]> backbone_convnext_1_pwconv1_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3892352)))]; |
| tensor<fp16, [1536]> backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5465280)))]; |
| tensor<fp16, [1, 282, 1536]> linear_2_cast_fp16 = linear(bias = backbone_convnext_1_pwconv1_bias_to_fp16, weight = backbone_convnext_1_pwconv1_weight_to_fp16, x = input_17_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")]; |
| tensor<string, []> input_21_mode_0 = const()[name = tensor<string, []>("input_21_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 282, 1536]> input_21_cast_fp16 = gelu(mode = input_21_mode_0, x = linear_2_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")]; |
| tensor<fp16, [512, 1536]> backbone_convnext_1_pwconv2_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_1_pwconv2_weight_to_fp16"), val = tensor<fp16, [512, 1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5468416)))]; |
| tensor<fp16, [512]> backbone_convnext_1_pwconv2_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_1_pwconv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7041344)))]; |
| tensor<fp16, [1, 282, 512]> linear_3_cast_fp16 = linear(bias = backbone_convnext_1_pwconv2_bias_to_fp16, weight = backbone_convnext_1_pwconv2_weight_to_fp16, x = input_21_cast_fp16)[name = tensor<string, []>("linear_3_cast_fp16")]; |
| tensor<fp16, [512]> backbone_convnext_1_gamma_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7042432)))]; |
| tensor<fp16, [1, 282, 512]> x_17_cast_fp16 = mul(x = backbone_convnext_1_gamma_to_fp16, y = linear_3_cast_fp16)[name = tensor<string, []>("x_17_cast_fp16")]; |
| tensor<fp16, [1, 282, 512]> input_23_cast_fp16 = add(x = input_13_cast_fp16, y = x_17_cast_fp16)[name = tensor<string, []>("input_23_cast_fp16")]; |
| tensor<string, []> x_21_pad_type_0 = const()[name = tensor<string, []>("x_21_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_21_pad_0 = const()[name = tensor<string, []>("x_21_pad_0"), val = tensor<int32, [2]>([3, 3])]; |
| tensor<int32, []> x_21_groups_0 = const()[name = tensor<string, []>("x_21_groups_0"), val = tensor<int32, []>(512)]; |
| tensor<int32, [1]> x_21_strides_0 = const()[name = tensor<string, []>("x_21_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_21_dilations_0 = const()[name = tensor<string, []>("x_21_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [3]> transpose_6_perm_0 = const()[name = tensor<string, []>("transpose_6_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 1, 7]> backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7043520)))]; |
| tensor<fp16, [512]> backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7050752)))]; |
| tensor<fp16, [1, 512, 282]> transpose_6_cast_fp16 = transpose(perm = transpose_6_perm_0, x = input_23_cast_fp16)[name = tensor<string, []>("transpose_26")]; |
| tensor<fp16, [1, 512, 282]> x_21_cast_fp16 = conv(bias = backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = backbone_convnext_2_dwconv_weight_to_fp16, x = transpose_6_cast_fp16)[name = tensor<string, []>("x_21_cast_fp16")]; |
| tensor<int32, [3]> input_25_perm_0 = const()[name = tensor<string, []>("input_25_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> input_27_axes_0 = const()[name = tensor<string, []>("input_27_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [512]> backbone_convnext_2_norm_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_2_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7051840)))]; |
| tensor<fp16, [512]> backbone_convnext_2_norm_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_2_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7052928)))]; |
| tensor<fp16, [1, 282, 512]> input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_21_cast_fp16)[name = tensor<string, []>("transpose_25")]; |
| tensor<fp16, [1, 282, 512]> input_27_cast_fp16 = layer_norm(axes = input_27_axes_0, beta = backbone_convnext_2_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_2_norm_weight_to_fp16, x = input_25_cast_fp16)[name = tensor<string, []>("input_27_cast_fp16")]; |
| tensor<fp16, [1536, 512]> backbone_convnext_2_pwconv1_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7054016)))]; |
| tensor<fp16, [1536]> backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8626944)))]; |
| tensor<fp16, [1, 282, 1536]> linear_4_cast_fp16 = linear(bias = backbone_convnext_2_pwconv1_bias_to_fp16, weight = backbone_convnext_2_pwconv1_weight_to_fp16, x = input_27_cast_fp16)[name = tensor<string, []>("linear_4_cast_fp16")]; |
| tensor<string, []> input_31_mode_0 = const()[name = tensor<string, []>("input_31_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 282, 1536]> input_31_cast_fp16 = gelu(mode = input_31_mode_0, x = linear_4_cast_fp16)[name = tensor<string, []>("input_31_cast_fp16")]; |
| tensor<fp16, [512, 1536]> backbone_convnext_2_pwconv2_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_2_pwconv2_weight_to_fp16"), val = tensor<fp16, [512, 1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8630080)))]; |
| tensor<fp16, [512]> backbone_convnext_2_pwconv2_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_2_pwconv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10203008)))]; |
| tensor<fp16, [1, 282, 512]> linear_5_cast_fp16 = linear(bias = backbone_convnext_2_pwconv2_bias_to_fp16, weight = backbone_convnext_2_pwconv2_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("linear_5_cast_fp16")]; |
| tensor<fp16, [512]> backbone_convnext_2_gamma_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_2_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10204096)))]; |
| tensor<fp16, [1, 282, 512]> x_25_cast_fp16 = mul(x = backbone_convnext_2_gamma_to_fp16, y = linear_5_cast_fp16)[name = tensor<string, []>("x_25_cast_fp16")]; |
| tensor<fp16, [1, 282, 512]> input_33_cast_fp16 = add(x = input_23_cast_fp16, y = x_25_cast_fp16)[name = tensor<string, []>("input_33_cast_fp16")]; |
| tensor<string, []> x_29_pad_type_0 = const()[name = tensor<string, []>("x_29_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_29_pad_0 = const()[name = tensor<string, []>("x_29_pad_0"), val = tensor<int32, [2]>([3, 3])]; |
| tensor<int32, []> x_29_groups_0 = const()[name = tensor<string, []>("x_29_groups_0"), val = tensor<int32, []>(512)]; |
| tensor<int32, [1]> x_29_strides_0 = const()[name = tensor<string, []>("x_29_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_29_dilations_0 = const()[name = tensor<string, []>("x_29_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [3]> transpose_7_perm_0 = const()[name = tensor<string, []>("transpose_7_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 1, 7]> backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10205184)))]; |
| tensor<fp16, [512]> backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10212416)))]; |
| tensor<fp16, [1, 512, 282]> transpose_7_cast_fp16 = transpose(perm = transpose_7_perm_0, x = input_33_cast_fp16)[name = tensor<string, []>("transpose_24")]; |
| tensor<fp16, [1, 512, 282]> x_29_cast_fp16 = conv(bias = backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = backbone_convnext_3_dwconv_weight_to_fp16, x = transpose_7_cast_fp16)[name = tensor<string, []>("x_29_cast_fp16")]; |
| tensor<int32, [3]> input_35_perm_0 = const()[name = tensor<string, []>("input_35_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> input_37_axes_0 = const()[name = tensor<string, []>("input_37_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [512]> backbone_convnext_3_norm_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_3_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10213504)))]; |
| tensor<fp16, [512]> backbone_convnext_3_norm_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_3_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10214592)))]; |
| tensor<fp16, [1, 282, 512]> input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = x_29_cast_fp16)[name = tensor<string, []>("transpose_23")]; |
| tensor<fp16, [1, 282, 512]> input_37_cast_fp16 = layer_norm(axes = input_37_axes_0, beta = backbone_convnext_3_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_3_norm_weight_to_fp16, x = input_35_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")]; |
| tensor<fp16, [1536, 512]> backbone_convnext_3_pwconv1_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10215680)))]; |
| tensor<fp16, [1536]> backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11788608)))]; |
| tensor<fp16, [1, 282, 1536]> linear_6_cast_fp16 = linear(bias = backbone_convnext_3_pwconv1_bias_to_fp16, weight = backbone_convnext_3_pwconv1_weight_to_fp16, x = input_37_cast_fp16)[name = tensor<string, []>("linear_6_cast_fp16")]; |
| tensor<string, []> input_41_mode_0 = const()[name = tensor<string, []>("input_41_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 282, 1536]> input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = linear_6_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")]; |
| tensor<fp16, [512, 1536]> backbone_convnext_3_pwconv2_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_3_pwconv2_weight_to_fp16"), val = tensor<fp16, [512, 1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11791744)))]; |
| tensor<fp16, [512]> backbone_convnext_3_pwconv2_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_3_pwconv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13364672)))]; |
| tensor<fp16, [1, 282, 512]> linear_7_cast_fp16 = linear(bias = backbone_convnext_3_pwconv2_bias_to_fp16, weight = backbone_convnext_3_pwconv2_weight_to_fp16, x = input_41_cast_fp16)[name = tensor<string, []>("linear_7_cast_fp16")]; |
| tensor<fp16, [512]> backbone_convnext_3_gamma_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_3_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13365760)))]; |
| tensor<fp16, [1, 282, 512]> x_33_cast_fp16 = mul(x = backbone_convnext_3_gamma_to_fp16, y = linear_7_cast_fp16)[name = tensor<string, []>("x_33_cast_fp16")]; |
| tensor<fp16, [1, 282, 512]> input_43_cast_fp16 = add(x = input_33_cast_fp16, y = x_33_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")]; |
| tensor<string, []> x_37_pad_type_0 = const()[name = tensor<string, []>("x_37_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_37_pad_0 = const()[name = tensor<string, []>("x_37_pad_0"), val = tensor<int32, [2]>([3, 3])]; |
| tensor<int32, []> x_37_groups_0 = const()[name = tensor<string, []>("x_37_groups_0"), val = tensor<int32, []>(512)]; |
| tensor<int32, [1]> x_37_strides_0 = const()[name = tensor<string, []>("x_37_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_37_dilations_0 = const()[name = tensor<string, []>("x_37_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [3]> transpose_8_perm_0 = const()[name = tensor<string, []>("transpose_8_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 1, 7]> backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13366848)))]; |
| tensor<fp16, [512]> backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13374080)))]; |
| tensor<fp16, [1, 512, 282]> transpose_8_cast_fp16 = transpose(perm = transpose_8_perm_0, x = input_43_cast_fp16)[name = tensor<string, []>("transpose_22")]; |
| tensor<fp16, [1, 512, 282]> x_37_cast_fp16 = conv(bias = backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_37_dilations_0, groups = x_37_groups_0, pad = x_37_pad_0, pad_type = x_37_pad_type_0, strides = x_37_strides_0, weight = backbone_convnext_4_dwconv_weight_to_fp16, x = transpose_8_cast_fp16)[name = tensor<string, []>("x_37_cast_fp16")]; |
| tensor<int32, [3]> input_45_perm_0 = const()[name = tensor<string, []>("input_45_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> input_47_axes_0 = const()[name = tensor<string, []>("input_47_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [512]> backbone_convnext_4_norm_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_4_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13375168)))]; |
| tensor<fp16, [512]> backbone_convnext_4_norm_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_4_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13376256)))]; |
| tensor<fp16, [1, 282, 512]> input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_37_cast_fp16)[name = tensor<string, []>("transpose_21")]; |
| tensor<fp16, [1, 282, 512]> input_47_cast_fp16 = layer_norm(axes = input_47_axes_0, beta = backbone_convnext_4_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_4_norm_weight_to_fp16, x = input_45_cast_fp16)[name = tensor<string, []>("input_47_cast_fp16")]; |
| tensor<fp16, [1536, 512]> backbone_convnext_4_pwconv1_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_4_pwconv1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13377344)))]; |
| tensor<fp16, [1536]> backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14950272)))]; |
| tensor<fp16, [1, 282, 1536]> linear_8_cast_fp16 = linear(bias = backbone_convnext_4_pwconv1_bias_to_fp16, weight = backbone_convnext_4_pwconv1_weight_to_fp16, x = input_47_cast_fp16)[name = tensor<string, []>("linear_8_cast_fp16")]; |
| tensor<string, []> input_51_mode_0 = const()[name = tensor<string, []>("input_51_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 282, 1536]> input_51_cast_fp16 = gelu(mode = input_51_mode_0, x = linear_8_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")]; |
| tensor<fp16, [512, 1536]> backbone_convnext_4_pwconv2_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_4_pwconv2_weight_to_fp16"), val = tensor<fp16, [512, 1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14953408)))]; |
| tensor<fp16, [512]> backbone_convnext_4_pwconv2_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_4_pwconv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16526336)))]; |
| tensor<fp16, [1, 282, 512]> linear_9_cast_fp16 = linear(bias = backbone_convnext_4_pwconv2_bias_to_fp16, weight = backbone_convnext_4_pwconv2_weight_to_fp16, x = input_51_cast_fp16)[name = tensor<string, []>("linear_9_cast_fp16")]; |
| tensor<fp16, [512]> backbone_convnext_4_gamma_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_4_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16527424)))]; |
| tensor<fp16, [1, 282, 512]> x_41_cast_fp16 = mul(x = backbone_convnext_4_gamma_to_fp16, y = linear_9_cast_fp16)[name = tensor<string, []>("x_41_cast_fp16")]; |
| tensor<fp16, [1, 282, 512]> input_53_cast_fp16 = add(x = input_43_cast_fp16, y = x_41_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")]; |
| tensor<string, []> x_45_pad_type_0 = const()[name = tensor<string, []>("x_45_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_45_pad_0 = const()[name = tensor<string, []>("x_45_pad_0"), val = tensor<int32, [2]>([3, 3])]; |
| tensor<int32, []> x_45_groups_0 = const()[name = tensor<string, []>("x_45_groups_0"), val = tensor<int32, []>(512)]; |
| tensor<int32, [1]> x_45_strides_0 = const()[name = tensor<string, []>("x_45_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_45_dilations_0 = const()[name = tensor<string, []>("x_45_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [3]> transpose_9_perm_0 = const()[name = tensor<string, []>("transpose_9_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 1, 7]> backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16528512)))]; |
| tensor<fp16, [512]> backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16535744)))]; |
| tensor<fp16, [1, 512, 282]> transpose_9_cast_fp16 = transpose(perm = transpose_9_perm_0, x = input_53_cast_fp16)[name = tensor<string, []>("transpose_20")]; |
| tensor<fp16, [1, 512, 282]> x_45_cast_fp16 = conv(bias = backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = backbone_convnext_5_dwconv_weight_to_fp16, x = transpose_9_cast_fp16)[name = tensor<string, []>("x_45_cast_fp16")]; |
| tensor<int32, [3]> input_55_perm_0 = const()[name = tensor<string, []>("input_55_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> input_57_axes_0 = const()[name = tensor<string, []>("input_57_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [512]> backbone_convnext_5_norm_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_5_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16536832)))]; |
| tensor<fp16, [512]> backbone_convnext_5_norm_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_5_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16537920)))]; |
| tensor<fp16, [1, 282, 512]> input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_45_cast_fp16)[name = tensor<string, []>("transpose_19")]; |
| tensor<fp16, [1, 282, 512]> input_57_cast_fp16 = layer_norm(axes = input_57_axes_0, beta = backbone_convnext_5_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_5_norm_weight_to_fp16, x = input_55_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")]; |
| tensor<fp16, [1536, 512]> backbone_convnext_5_pwconv1_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_5_pwconv1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16539008)))]; |
| tensor<fp16, [1536]> backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18111936)))]; |
| tensor<fp16, [1, 282, 1536]> linear_10_cast_fp16 = linear(bias = backbone_convnext_5_pwconv1_bias_to_fp16, weight = backbone_convnext_5_pwconv1_weight_to_fp16, x = input_57_cast_fp16)[name = tensor<string, []>("linear_10_cast_fp16")]; |
| tensor<string, []> input_61_mode_0 = const()[name = tensor<string, []>("input_61_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 282, 1536]> input_61_cast_fp16 = gelu(mode = input_61_mode_0, x = linear_10_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")]; |
| tensor<fp16, [512, 1536]> backbone_convnext_5_pwconv2_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_5_pwconv2_weight_to_fp16"), val = tensor<fp16, [512, 1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18115072)))]; |
| tensor<fp16, [512]> backbone_convnext_5_pwconv2_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_5_pwconv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19688000)))]; |
| tensor<fp16, [1, 282, 512]> linear_11_cast_fp16 = linear(bias = backbone_convnext_5_pwconv2_bias_to_fp16, weight = backbone_convnext_5_pwconv2_weight_to_fp16, x = input_61_cast_fp16)[name = tensor<string, []>("linear_11_cast_fp16")]; |
| tensor<fp16, [512]> backbone_convnext_5_gamma_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_5_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19689088)))]; |
| tensor<fp16, [1, 282, 512]> x_49_cast_fp16 = mul(x = backbone_convnext_5_gamma_to_fp16, y = linear_11_cast_fp16)[name = tensor<string, []>("x_49_cast_fp16")]; |
| tensor<fp16, [1, 282, 512]> input_63_cast_fp16 = add(x = input_53_cast_fp16, y = x_49_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")]; |
| tensor<string, []> x_53_pad_type_0 = const()[name = tensor<string, []>("x_53_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_53_pad_0 = const()[name = tensor<string, []>("x_53_pad_0"), val = tensor<int32, [2]>([3, 3])]; |
| tensor<int32, []> x_53_groups_0 = const()[name = tensor<string, []>("x_53_groups_0"), val = tensor<int32, []>(512)]; |
| tensor<int32, [1]> x_53_strides_0 = const()[name = tensor<string, []>("x_53_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_53_dilations_0 = const()[name = tensor<string, []>("x_53_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [3]> transpose_10_perm_0 = const()[name = tensor<string, []>("transpose_10_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 1, 7]> backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19690176)))]; |
| tensor<fp16, [512]> backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19697408)))]; |
| tensor<fp16, [1, 512, 282]> transpose_10_cast_fp16 = transpose(perm = transpose_10_perm_0, x = input_63_cast_fp16)[name = tensor<string, []>("transpose_18")]; |
| tensor<fp16, [1, 512, 282]> x_53_cast_fp16 = conv(bias = backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = backbone_convnext_6_dwconv_weight_to_fp16, x = transpose_10_cast_fp16)[name = tensor<string, []>("x_53_cast_fp16")]; |
| tensor<int32, [3]> input_65_perm_0 = const()[name = tensor<string, []>("input_65_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> input_67_axes_0 = const()[name = tensor<string, []>("input_67_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [512]> backbone_convnext_6_norm_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_6_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19698496)))]; |
| tensor<fp16, [512]> backbone_convnext_6_norm_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_6_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19699584)))]; |
| tensor<fp16, [1, 282, 512]> input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = tensor<string, []>("transpose_17")]; |
| tensor<fp16, [1, 282, 512]> input_67_cast_fp16 = layer_norm(axes = input_67_axes_0, beta = backbone_convnext_6_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_6_norm_weight_to_fp16, x = input_65_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")]; |
| tensor<fp16, [1536, 512]> backbone_convnext_6_pwconv1_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_6_pwconv1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19700672)))]; |
| tensor<fp16, [1536]> backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21273600)))]; |
| tensor<fp16, [1, 282, 1536]> linear_12_cast_fp16 = linear(bias = backbone_convnext_6_pwconv1_bias_to_fp16, weight = backbone_convnext_6_pwconv1_weight_to_fp16, x = input_67_cast_fp16)[name = tensor<string, []>("linear_12_cast_fp16")]; |
| tensor<string, []> input_71_mode_0 = const()[name = tensor<string, []>("input_71_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 282, 1536]> input_71_cast_fp16 = gelu(mode = input_71_mode_0, x = linear_12_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")]; |
| tensor<fp16, [512, 1536]> backbone_convnext_6_pwconv2_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_6_pwconv2_weight_to_fp16"), val = tensor<fp16, [512, 1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21276736)))]; |
| tensor<fp16, [512]> backbone_convnext_6_pwconv2_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_6_pwconv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22849664)))]; |
| tensor<fp16, [1, 282, 512]> linear_13_cast_fp16 = linear(bias = backbone_convnext_6_pwconv2_bias_to_fp16, weight = backbone_convnext_6_pwconv2_weight_to_fp16, x = input_71_cast_fp16)[name = tensor<string, []>("linear_13_cast_fp16")]; |
| tensor<fp16, [512]> backbone_convnext_6_gamma_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_6_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22850752)))]; |
| tensor<fp16, [1, 282, 512]> x_57_cast_fp16 = mul(x = backbone_convnext_6_gamma_to_fp16, y = linear_13_cast_fp16)[name = tensor<string, []>("x_57_cast_fp16")]; |
| tensor<fp16, [1, 282, 512]> input_73_cast_fp16 = add(x = input_63_cast_fp16, y = x_57_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")]; |
| tensor<string, []> x_61_pad_type_0 = const()[name = tensor<string, []>("x_61_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_61_pad_0 = const()[name = tensor<string, []>("x_61_pad_0"), val = tensor<int32, [2]>([3, 3])]; |
| tensor<int32, []> x_61_groups_0 = const()[name = tensor<string, []>("x_61_groups_0"), val = tensor<int32, []>(512)]; |
| tensor<int32, [1]> x_61_strides_0 = const()[name = tensor<string, []>("x_61_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_61_dilations_0 = const()[name = tensor<string, []>("x_61_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [3]> transpose_11_perm_0 = const()[name = tensor<string, []>("transpose_11_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 1, 7]> backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22851840)))]; |
| tensor<fp16, [512]> backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22859072)))]; |
| tensor<fp16, [1, 512, 282]> transpose_11_cast_fp16 = transpose(perm = transpose_11_perm_0, x = input_73_cast_fp16)[name = tensor<string, []>("transpose_16")]; |
| tensor<fp16, [1, 512, 282]> x_61_cast_fp16 = conv(bias = backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_61_dilations_0, groups = x_61_groups_0, pad = x_61_pad_0, pad_type = x_61_pad_type_0, strides = x_61_strides_0, weight = backbone_convnext_7_dwconv_weight_to_fp16, x = transpose_11_cast_fp16)[name = tensor<string, []>("x_61_cast_fp16")]; |
| tensor<int32, [3]> input_75_perm_0 = const()[name = tensor<string, []>("input_75_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> input_77_axes_0 = const()[name = tensor<string, []>("input_77_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [512]> backbone_convnext_7_norm_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_7_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22860160)))]; |
| tensor<fp16, [512]> backbone_convnext_7_norm_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_7_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22861248)))]; |
| tensor<fp16, [1, 282, 512]> input_75_cast_fp16 = transpose(perm = input_75_perm_0, x = x_61_cast_fp16)[name = tensor<string, []>("transpose_15")]; |
| tensor<fp16, [1, 282, 512]> input_77_cast_fp16 = layer_norm(axes = input_77_axes_0, beta = backbone_convnext_7_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_7_norm_weight_to_fp16, x = input_75_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")]; |
| tensor<fp16, [1536, 512]> backbone_convnext_7_pwconv1_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_7_pwconv1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22862336)))]; |
| tensor<fp16, [1536]> backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24435264)))]; |
| tensor<fp16, [1, 282, 1536]> linear_14_cast_fp16 = linear(bias = backbone_convnext_7_pwconv1_bias_to_fp16, weight = backbone_convnext_7_pwconv1_weight_to_fp16, x = input_77_cast_fp16)[name = tensor<string, []>("linear_14_cast_fp16")]; |
| tensor<string, []> input_81_mode_0 = const()[name = tensor<string, []>("input_81_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp16, [1, 282, 1536]> input_81_cast_fp16 = gelu(mode = input_81_mode_0, x = linear_14_cast_fp16)[name = tensor<string, []>("input_81_cast_fp16")]; |
| tensor<fp16, [512, 1536]> backbone_convnext_7_pwconv2_weight_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_7_pwconv2_weight_to_fp16"), val = tensor<fp16, [512, 1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24438400)))]; |
| tensor<fp16, [512]> backbone_convnext_7_pwconv2_bias_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_7_pwconv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26011328)))]; |
| tensor<fp16, [1, 282, 512]> linear_15_cast_fp16 = linear(bias = backbone_convnext_7_pwconv2_bias_to_fp16, weight = backbone_convnext_7_pwconv2_weight_to_fp16, x = input_81_cast_fp16)[name = tensor<string, []>("linear_15_cast_fp16")]; |
| tensor<fp16, [512]> backbone_convnext_7_gamma_to_fp16 = const()[name = tensor<string, []>("backbone_convnext_7_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26012416)))]; |
| tensor<fp16, [1, 282, 512]> x_65_cast_fp16 = mul(x = backbone_convnext_7_gamma_to_fp16, y = linear_15_cast_fp16)[name = tensor<string, []>("x_65_cast_fp16")]; |
| tensor<fp16, [1, 282, 512]> x_69_cast_fp16 = add(x = input_73_cast_fp16, y = x_65_cast_fp16)[name = tensor<string, []>("x_69_cast_fp16")]; |
| tensor<int32, [1]> features_axes_0 = const()[name = tensor<string, []>("features_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [512]> backbone_final_layer_norm_weight_to_fp16 = const()[name = tensor<string, []>("backbone_final_layer_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26013504)))]; |
| tensor<fp16, [512]> backbone_final_layer_norm_bias_to_fp16 = const()[name = tensor<string, []>("backbone_final_layer_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26014592)))]; |
| tensor<fp16, [1, 282, 512]> features_cast_fp16 = layer_norm(axes = features_axes_0, beta = backbone_final_layer_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_final_layer_norm_weight_to_fp16, x = x_69_cast_fp16)[name = tensor<string, []>("features_cast_fp16")]; |
| tensor<int32, [3]> input_85_perm_0 = const()[name = tensor<string, []>("input_85_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<string, []> input_87_pad_type_0 = const()[name = tensor<string, []>("input_87_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> input_87_pad_0 = const()[name = tensor<string, []>("input_87_pad_0"), val = tensor<int32, [2]>([3, 3])]; |
| tensor<int32, [1]> input_87_strides_0 = const()[name = tensor<string, []>("input_87_strides_0"), val = tensor<int32, [1]>([2])]; |
| tensor<int32, [1]> input_87_dilations_0 = const()[name = tensor<string, []>("input_87_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_87_groups_0 = const()[name = tensor<string, []>("input_87_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<int32, [3]> input_87_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("input_87_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 256, 564])]; |
| tensor<fp16, [512, 256, 8]> upsampler_upsample_layers_0_weight_to_fp16 = const()[name = tensor<string, []>("upsampler_upsample_layers_0_weight_to_fp16"), val = tensor<fp16, [512, 256, 8]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26015680)))]; |
| tensor<fp16, [256]> upsampler_upsample_layers_0_bias_to_fp16 = const()[name = tensor<string, []>("upsampler_upsample_layers_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28112896)))]; |
| tensor<fp16, [1, 512, 282]> input_85_cast_fp16 = transpose(perm = input_85_perm_0, x = features_cast_fp16)[name = tensor<string, []>("transpose_14")]; |
| tensor<fp16, [1, 256, 564]> input_87_has_output_shape_cast_fp16 = conv_transpose(bias = upsampler_upsample_layers_0_bias_to_fp16, dilations = input_87_dilations_0, groups = input_87_groups_0, output_shape = input_87_has_output_shape_output_shape_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = upsampler_upsample_layers_0_weight_to_fp16, x = input_85_cast_fp16)[name = tensor<string, []>("input_87_has_output_shape_cast_fp16")]; |
| tensor<int32, [4]> reshape_0_shape_0 = const()[name = tensor<string, []>("reshape_0_shape_0"), val = tensor<int32, [4]>([1, 32, 8, 564])]; |
| tensor<fp16, [1, 32, 8, 564]> reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_87_has_output_shape_cast_fp16)[name = tensor<string, []>("reshape_0_cast_fp16")]; |
| tensor<int32, [2]> reduce_mean_0_axes_0 = const()[name = tensor<string, []>("reduce_mean_0_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| tensor<bool, []> reduce_mean_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_0_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp16, [1, 32, 1, 1]> reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor<string, []>("reduce_mean_0_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 564]> sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor<string, []>("sub_0_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 564]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor<string, []>("square_0_cast_fp16")]; |
| tensor<int32, [2]> reduce_mean_2_axes_0 = const()[name = tensor<string, []>("reduce_mean_2_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| tensor<bool, []> reduce_mean_2_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_2_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp16, [1, 32, 1, 1]> reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor<string, []>("reduce_mean_2_cast_fp16")]; |
| tensor<fp16, []> add_0_y_0_to_fp16 = const()[name = tensor<string, []>("add_0_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1]> add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor<string, []>("add_0_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1]> sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor<string, []>("sqrt_0_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 564]> real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor<string, []>("real_div_0_cast_fp16")]; |
| tensor<int32, [3]> reshape_1_shape_0 = const()[name = tensor<string, []>("reshape_1_shape_0"), val = tensor<int32, [3]>([1, 256, 564])]; |
| tensor<fp16, [1, 256, 564]> reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor<string, []>("reshape_1_cast_fp16")]; |
| tensor<fp16, [1, 256, 1]> reshape_2_to_fp16 = const()[name = tensor<string, []>("reshape_2_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28113472)))]; |
| tensor<fp16, [1, 256, 564]> mul_0_cast_fp16 = mul(x = reshape_1_cast_fp16, y = reshape_2_to_fp16)[name = tensor<string, []>("mul_0_cast_fp16")]; |
| tensor<fp16, [1, 256, 1]> reshape_3_to_fp16 = const()[name = tensor<string, []>("reshape_3_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28114048)))]; |
| tensor<fp16, [1, 256, 564]> add_1_cast_fp16 = add(x = mul_0_cast_fp16, y = reshape_3_to_fp16)[name = tensor<string, []>("add_1_cast_fp16")]; |
| tensor<fp16, [1, 256, 1]> upsampler_resnet_blocks_0_snake1_alpha_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_0_snake1_alpha_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28114624)))]; |
| tensor<fp16, [1, 256, 564]> var_312_cast_fp16 = mul(x = upsampler_resnet_blocks_0_snake1_alpha_to_fp16, y = add_1_cast_fp16)[name = tensor<string, []>("op_312_cast_fp16")]; |
| tensor<fp16, [1, 256, 564]> var_313_cast_fp16 = sin(x = var_312_cast_fp16)[name = tensor<string, []>("op_313_cast_fp16")]; |
| tensor<fp16, []> var_276_promoted_to_fp16 = const()[name = tensor<string, []>("op_276_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)]; |
| tensor<fp16, [1, 256, 564]> var_314_cast_fp16 = pow(x = var_313_cast_fp16, y = var_276_promoted_to_fp16)[name = tensor<string, []>("op_314_cast_fp16")]; |
| tensor<fp16, [1, 256, 1]> var_311_to_fp16 = const()[name = tensor<string, []>("op_311_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28115200)))]; |
| tensor<fp16, [1, 256, 564]> var_315_cast_fp16 = mul(x = var_311_to_fp16, y = var_314_cast_fp16)[name = tensor<string, []>("op_315_cast_fp16")]; |
| tensor<fp16, [1, 256, 564]> input_89_cast_fp16 = add(x = add_1_cast_fp16, y = var_315_cast_fp16)[name = tensor<string, []>("input_89_cast_fp16")]; |
| tensor<string, []> input_91_pad_type_0 = const()[name = tensor<string, []>("input_91_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> input_91_pad_0 = const()[name = tensor<string, []>("input_91_pad_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [1]> input_91_strides_0 = const()[name = tensor<string, []>("input_91_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> input_91_dilations_0 = const()[name = tensor<string, []>("input_91_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_91_groups_0 = const()[name = tensor<string, []>("input_91_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [256, 256, 3]> upsampler_resnet_blocks_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_0_conv1_weight_to_fp16"), val = tensor<fp16, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28115776)))]; |
| tensor<fp16, [256]> upsampler_resnet_blocks_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_0_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28509056)))]; |
| tensor<fp16, [1, 256, 564]> input_91_cast_fp16 = conv(bias = upsampler_resnet_blocks_0_conv1_bias_to_fp16, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = upsampler_resnet_blocks_0_conv1_weight_to_fp16, x = input_89_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")]; |
| tensor<int32, [4]> reshape_4_shape_0 = const()[name = tensor<string, []>("reshape_4_shape_0"), val = tensor<int32, [4]>([1, 32, 8, 564])]; |
| tensor<fp16, [1, 32, 8, 564]> reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_91_cast_fp16)[name = tensor<string, []>("reshape_4_cast_fp16")]; |
| tensor<int32, [2]> reduce_mean_3_axes_0 = const()[name = tensor<string, []>("reduce_mean_3_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| tensor<bool, []> reduce_mean_3_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_3_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp16, [1, 32, 1, 1]> reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor<string, []>("reduce_mean_3_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 564]> sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor<string, []>("sub_2_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 564]> square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor<string, []>("square_1_cast_fp16")]; |
| tensor<int32, [2]> reduce_mean_5_axes_0 = const()[name = tensor<string, []>("reduce_mean_5_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| tensor<bool, []> reduce_mean_5_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_5_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp16, [1, 32, 1, 1]> reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor<string, []>("reduce_mean_5_cast_fp16")]; |
| tensor<fp16, []> add_2_y_0_to_fp16 = const()[name = tensor<string, []>("add_2_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1]> add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor<string, []>("add_2_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1]> sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor<string, []>("sqrt_1_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 564]> real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor<string, []>("real_div_1_cast_fp16")]; |
| tensor<int32, [3]> reshape_5_shape_0 = const()[name = tensor<string, []>("reshape_5_shape_0"), val = tensor<int32, [3]>([1, 256, 564])]; |
| tensor<fp16, [1, 256, 564]> reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor<string, []>("reshape_5_cast_fp16")]; |
| tensor<fp16, [1, 256, 1]> reshape_6_to_fp16 = const()[name = tensor<string, []>("reshape_6_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28509632)))]; |
| tensor<fp16, [1, 256, 564]> mul_1_cast_fp16 = mul(x = reshape_5_cast_fp16, y = reshape_6_to_fp16)[name = tensor<string, []>("mul_1_cast_fp16")]; |
| tensor<fp16, [1, 256, 1]> reshape_7_to_fp16 = const()[name = tensor<string, []>("reshape_7_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28510208)))]; |
| tensor<fp16, [1, 256, 564]> add_3_cast_fp16 = add(x = mul_1_cast_fp16, y = reshape_7_to_fp16)[name = tensor<string, []>("add_3_cast_fp16")]; |
| tensor<fp16, [1, 256, 1]> upsampler_resnet_blocks_0_snake2_alpha_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_0_snake2_alpha_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28510784)))]; |
| tensor<fp16, [1, 256, 564]> var_331_cast_fp16 = mul(x = upsampler_resnet_blocks_0_snake2_alpha_to_fp16, y = add_3_cast_fp16)[name = tensor<string, []>("op_331_cast_fp16")]; |
| tensor<fp16, [1, 256, 564]> var_332_cast_fp16 = sin(x = var_331_cast_fp16)[name = tensor<string, []>("op_332_cast_fp16")]; |
| tensor<fp16, []> var_276_promoted_1_to_fp16 = const()[name = tensor<string, []>("op_276_promoted_1_to_fp16"), val = tensor<fp16, []>(0x1p+1)]; |
| tensor<fp16, [1, 256, 564]> var_333_cast_fp16 = pow(x = var_332_cast_fp16, y = var_276_promoted_1_to_fp16)[name = tensor<string, []>("op_333_cast_fp16")]; |
| tensor<fp16, [1, 256, 1]> var_330_to_fp16 = const()[name = tensor<string, []>("op_330_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28511360)))]; |
| tensor<fp16, [1, 256, 564]> var_334_cast_fp16 = mul(x = var_330_to_fp16, y = var_333_cast_fp16)[name = tensor<string, []>("op_334_cast_fp16")]; |
| tensor<fp16, [1, 256, 564]> input_93_cast_fp16 = add(x = add_3_cast_fp16, y = var_334_cast_fp16)[name = tensor<string, []>("input_93_cast_fp16")]; |
| tensor<string, []> h_1_pad_type_0 = const()[name = tensor<string, []>("h_1_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> h_1_pad_0 = const()[name = tensor<string, []>("h_1_pad_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [1]> h_1_strides_0 = const()[name = tensor<string, []>("h_1_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> h_1_dilations_0 = const()[name = tensor<string, []>("h_1_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> h_1_groups_0 = const()[name = tensor<string, []>("h_1_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [256, 256, 3]> upsampler_resnet_blocks_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_0_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28511936)))]; |
| tensor<fp16, [256]> upsampler_resnet_blocks_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_0_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28905216)))]; |
| tensor<fp16, [1, 256, 564]> h_1_cast_fp16 = conv(bias = upsampler_resnet_blocks_0_conv2_bias_to_fp16, dilations = h_1_dilations_0, groups = h_1_groups_0, pad = h_1_pad_0, pad_type = h_1_pad_type_0, strides = h_1_strides_0, weight = upsampler_resnet_blocks_0_conv2_weight_to_fp16, x = input_93_cast_fp16)[name = tensor<string, []>("h_1_cast_fp16")]; |
| tensor<fp16, [1, 256, 564]> input_97_cast_fp16 = add(x = input_87_has_output_shape_cast_fp16, y = h_1_cast_fp16)[name = tensor<string, []>("input_97_cast_fp16")]; |
| tensor<string, []> input_99_pad_type_0 = const()[name = tensor<string, []>("input_99_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> input_99_pad_0 = const()[name = tensor<string, []>("input_99_pad_0"), val = tensor<int32, [2]>([3, 3])]; |
| tensor<int32, [1]> input_99_strides_0 = const()[name = tensor<string, []>("input_99_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> input_99_dilations_0 = const()[name = tensor<string, []>("input_99_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_99_groups_0 = const()[name = tensor<string, []>("input_99_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<int32, [3]> input_99_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("input_99_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 128, 565])]; |
| tensor<fp16, [256, 128, 8]> upsampler_upsample_layers_1_weight_to_fp16 = const()[name = tensor<string, []>("upsampler_upsample_layers_1_weight_to_fp16"), val = tensor<fp16, [256, 128, 8]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28905792)))]; |
| tensor<fp16, [128]> upsampler_upsample_layers_1_bias_to_fp16 = const()[name = tensor<string, []>("upsampler_upsample_layers_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29430144)))]; |
| tensor<fp16, [1, 128, 565]> input_99_has_output_shape_cast_fp16 = conv_transpose(bias = upsampler_upsample_layers_1_bias_to_fp16, dilations = input_99_dilations_0, groups = input_99_groups_0, output_shape = input_99_has_output_shape_output_shape_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = upsampler_upsample_layers_1_weight_to_fp16, x = input_97_cast_fp16)[name = tensor<string, []>("input_99_has_output_shape_cast_fp16")]; |
| tensor<int32, [4]> reshape_8_shape_0 = const()[name = tensor<string, []>("reshape_8_shape_0"), val = tensor<int32, [4]>([1, 32, 4, 565])]; |
| tensor<fp16, [1, 32, 4, 565]> reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = input_99_has_output_shape_cast_fp16)[name = tensor<string, []>("reshape_8_cast_fp16")]; |
| tensor<int32, [2]> reduce_mean_6_axes_0 = const()[name = tensor<string, []>("reduce_mean_6_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| tensor<bool, []> reduce_mean_6_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_6_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp16, [1, 32, 1, 1]> reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor<string, []>("reduce_mean_6_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 565]> sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor<string, []>("sub_4_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 565]> square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor<string, []>("square_2_cast_fp16")]; |
| tensor<int32, [2]> reduce_mean_8_axes_0 = const()[name = tensor<string, []>("reduce_mean_8_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| tensor<bool, []> reduce_mean_8_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_8_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp16, [1, 32, 1, 1]> reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor<string, []>("reduce_mean_8_cast_fp16")]; |
| tensor<fp16, []> add_4_y_0_to_fp16 = const()[name = tensor<string, []>("add_4_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1]> add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor<string, []>("add_4_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1]> sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor<string, []>("sqrt_2_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 565]> real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor<string, []>("real_div_2_cast_fp16")]; |
| tensor<int32, [3]> reshape_9_shape_0 = const()[name = tensor<string, []>("reshape_9_shape_0"), val = tensor<int32, [3]>([1, 128, 565])]; |
| tensor<fp16, [1, 128, 565]> reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor<string, []>("reshape_9_cast_fp16")]; |
| tensor<fp16, [1, 128, 1]> reshape_10_to_fp16 = const()[name = tensor<string, []>("reshape_10_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29430464)))]; |
| tensor<fp16, [1, 128, 565]> mul_2_cast_fp16 = mul(x = reshape_9_cast_fp16, y = reshape_10_to_fp16)[name = tensor<string, []>("mul_2_cast_fp16")]; |
| tensor<fp16, [1, 128, 1]> reshape_11_to_fp16 = const()[name = tensor<string, []>("reshape_11_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29430784)))]; |
| tensor<fp16, [1, 128, 565]> add_5_cast_fp16 = add(x = mul_2_cast_fp16, y = reshape_11_to_fp16)[name = tensor<string, []>("add_5_cast_fp16")]; |
| tensor<fp16, [1, 128, 1]> upsampler_resnet_blocks_1_snake1_alpha_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_1_snake1_alpha_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29431104)))]; |
| tensor<fp16, [1, 128, 565]> var_365_cast_fp16 = mul(x = upsampler_resnet_blocks_1_snake1_alpha_to_fp16, y = add_5_cast_fp16)[name = tensor<string, []>("op_365_cast_fp16")]; |
| tensor<fp16, [1, 128, 565]> var_366_cast_fp16 = sin(x = var_365_cast_fp16)[name = tensor<string, []>("op_366_cast_fp16")]; |
| tensor<fp16, []> var_276_promoted_2_to_fp16 = const()[name = tensor<string, []>("op_276_promoted_2_to_fp16"), val = tensor<fp16, []>(0x1p+1)]; |
| tensor<fp16, [1, 128, 565]> var_367_cast_fp16 = pow(x = var_366_cast_fp16, y = var_276_promoted_2_to_fp16)[name = tensor<string, []>("op_367_cast_fp16")]; |
| tensor<fp16, [1, 128, 1]> var_364_to_fp16 = const()[name = tensor<string, []>("op_364_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29431424)))]; |
| tensor<fp16, [1, 128, 565]> var_368_cast_fp16 = mul(x = var_364_to_fp16, y = var_367_cast_fp16)[name = tensor<string, []>("op_368_cast_fp16")]; |
| tensor<fp16, [1, 128, 565]> input_101_cast_fp16 = add(x = add_5_cast_fp16, y = var_368_cast_fp16)[name = tensor<string, []>("input_101_cast_fp16")]; |
| tensor<string, []> input_103_pad_type_0 = const()[name = tensor<string, []>("input_103_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> input_103_pad_0 = const()[name = tensor<string, []>("input_103_pad_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [1]> input_103_strides_0 = const()[name = tensor<string, []>("input_103_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> input_103_dilations_0 = const()[name = tensor<string, []>("input_103_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_103_groups_0 = const()[name = tensor<string, []>("input_103_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [128, 128, 3]> upsampler_resnet_blocks_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_1_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29431744)))]; |
| tensor<fp16, [128]> upsampler_resnet_blocks_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_1_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29530112)))]; |
| tensor<fp16, [1, 128, 565]> input_103_cast_fp16 = conv(bias = upsampler_resnet_blocks_1_conv1_bias_to_fp16, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = upsampler_resnet_blocks_1_conv1_weight_to_fp16, x = input_101_cast_fp16)[name = tensor<string, []>("input_103_cast_fp16")]; |
| tensor<int32, [4]> reshape_12_shape_0 = const()[name = tensor<string, []>("reshape_12_shape_0"), val = tensor<int32, [4]>([1, 32, 4, 565])]; |
| tensor<fp16, [1, 32, 4, 565]> reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = input_103_cast_fp16)[name = tensor<string, []>("reshape_12_cast_fp16")]; |
| tensor<int32, [2]> reduce_mean_9_axes_0 = const()[name = tensor<string, []>("reduce_mean_9_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| tensor<bool, []> reduce_mean_9_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_9_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp16, [1, 32, 1, 1]> reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor<string, []>("reduce_mean_9_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 565]> sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor<string, []>("sub_6_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 565]> square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor<string, []>("square_3_cast_fp16")]; |
| tensor<int32, [2]> reduce_mean_11_axes_0 = const()[name = tensor<string, []>("reduce_mean_11_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| tensor<bool, []> reduce_mean_11_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_11_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp16, [1, 32, 1, 1]> reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor<string, []>("reduce_mean_11_cast_fp16")]; |
| tensor<fp16, []> add_6_y_0_to_fp16 = const()[name = tensor<string, []>("add_6_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1]> add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor<string, []>("add_6_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1]> sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor<string, []>("sqrt_3_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 565]> real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor<string, []>("real_div_3_cast_fp16")]; |
| tensor<int32, [3]> reshape_13_shape_0 = const()[name = tensor<string, []>("reshape_13_shape_0"), val = tensor<int32, [3]>([1, 128, 565])]; |
| tensor<fp16, [1, 128, 565]> reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor<string, []>("reshape_13_cast_fp16")]; |
| tensor<fp16, [1, 128, 1]> reshape_14_to_fp16 = const()[name = tensor<string, []>("reshape_14_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29530432)))]; |
| tensor<fp16, [1, 128, 565]> mul_3_cast_fp16 = mul(x = reshape_13_cast_fp16, y = reshape_14_to_fp16)[name = tensor<string, []>("mul_3_cast_fp16")]; |
| tensor<fp16, [1, 128, 1]> reshape_15_to_fp16 = const()[name = tensor<string, []>("reshape_15_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29530752)))]; |
| tensor<fp16, [1, 128, 565]> add_7_cast_fp16 = add(x = mul_3_cast_fp16, y = reshape_15_to_fp16)[name = tensor<string, []>("add_7_cast_fp16")]; |
| tensor<fp16, [1, 128, 1]> upsampler_resnet_blocks_1_snake2_alpha_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_1_snake2_alpha_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29531072)))]; |
| tensor<fp16, [1, 128, 565]> var_384_cast_fp16 = mul(x = upsampler_resnet_blocks_1_snake2_alpha_to_fp16, y = add_7_cast_fp16)[name = tensor<string, []>("op_384_cast_fp16")]; |
| tensor<fp16, [1, 128, 565]> var_385_cast_fp16 = sin(x = var_384_cast_fp16)[name = tensor<string, []>("op_385_cast_fp16")]; |
| tensor<fp16, []> var_276_promoted_3_to_fp16 = const()[name = tensor<string, []>("op_276_promoted_3_to_fp16"), val = tensor<fp16, []>(0x1p+1)]; |
| tensor<fp16, [1, 128, 565]> var_386_cast_fp16 = pow(x = var_385_cast_fp16, y = var_276_promoted_3_to_fp16)[name = tensor<string, []>("op_386_cast_fp16")]; |
| tensor<fp16, [1, 128, 1]> var_383_to_fp16 = const()[name = tensor<string, []>("op_383_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29531392)))]; |
| tensor<fp16, [1, 128, 565]> var_387_cast_fp16 = mul(x = var_383_to_fp16, y = var_386_cast_fp16)[name = tensor<string, []>("op_387_cast_fp16")]; |
| tensor<fp16, [1, 128, 565]> input_105_cast_fp16 = add(x = add_7_cast_fp16, y = var_387_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")]; |
| tensor<string, []> h_pad_type_0 = const()[name = tensor<string, []>("h_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> h_pad_0 = const()[name = tensor<string, []>("h_pad_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [1]> h_strides_0 = const()[name = tensor<string, []>("h_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> h_dilations_0 = const()[name = tensor<string, []>("h_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> h_groups_0 = const()[name = tensor<string, []>("h_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [128, 128, 3]> upsampler_resnet_blocks_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_1_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29531712)))]; |
| tensor<fp16, [128]> upsampler_resnet_blocks_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("upsampler_resnet_blocks_1_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29630080)))]; |
| tensor<fp16, [1, 128, 565]> h_cast_fp16 = conv(bias = upsampler_resnet_blocks_1_conv2_bias_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = upsampler_resnet_blocks_1_conv2_weight_to_fp16, x = input_105_cast_fp16)[name = tensor<string, []>("h_cast_fp16")]; |
| tensor<fp16, [1, 128, 565]> x_79_cast_fp16 = add(x = input_99_has_output_shape_cast_fp16, y = h_cast_fp16)[name = tensor<string, []>("x_79_cast_fp16")]; |
| tensor<int32, [3]> input_109_perm_0 = const()[name = tensor<string, []>("input_109_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 128]> upsampler_out_proj_weight_to_fp16 = const()[name = tensor<string, []>("upsampler_out_proj_weight_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29630400)))]; |
| tensor<fp16, [512]> upsampler_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("upsampler_out_proj_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29761536)))]; |
| tensor<fp16, [1, 565, 128]> input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_79_cast_fp16)[name = tensor<string, []>("transpose_13")]; |
| tensor<fp16, [1, 565, 512]> linear_16_cast_fp16 = linear(bias = upsampler_out_proj_bias_to_fp16, weight = upsampler_out_proj_weight_to_fp16, x = input_109_cast_fp16)[name = tensor<string, []>("linear_16_cast_fp16")]; |
| tensor<fp16, [1, 1, 512]> const_11_to_fp16 = const()[name = tensor<string, []>("const_11_to_fp16"), val = tensor<fp16, [1, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29762624)))]; |
| tensor<fp16, [1, 565, 512]> var_407_cast_fp16 = mul(x = const_11_to_fp16, y = linear_16_cast_fp16)[name = tensor<string, []>("op_407_cast_fp16")]; |
| tensor<fp16, [1, 565, 512]> var_408_cast_fp16 = sin(x = var_407_cast_fp16)[name = tensor<string, []>("op_408_cast_fp16")]; |
| tensor<fp16, []> var_276_promoted_4_to_fp16 = const()[name = tensor<string, []>("op_276_promoted_4_to_fp16"), val = tensor<fp16, []>(0x1p+1)]; |
| tensor<fp16, [1, 565, 512]> var_409_cast_fp16 = pow(x = var_408_cast_fp16, y = var_276_promoted_4_to_fp16)[name = tensor<string, []>("op_409_cast_fp16")]; |
| tensor<fp16, [1, 1, 512]> const_12_to_fp16 = const()[name = tensor<string, []>("const_12_to_fp16"), val = tensor<fp16, [1, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29763712)))]; |
| tensor<fp16, [1, 565, 512]> var_410_cast_fp16 = mul(x = const_12_to_fp16, y = var_409_cast_fp16)[name = tensor<string, []>("op_410_cast_fp16")]; |
| tensor<fp16, [1, 565, 512]> upsampled_cast_fp16 = add(x = linear_16_cast_fp16, y = var_410_cast_fp16)[name = tensor<string, []>("upsampled_cast_fp16")]; |
| tensor<fp16, [1026, 512]> head_48k_out_weight_to_fp16 = const()[name = tensor<string, []>("head_48k_out_weight_to_fp16"), val = tensor<fp16, [1026, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29764800)))]; |
| tensor<fp16, [1026]> head_48k_out_bias_to_fp16 = const()[name = tensor<string, []>("head_48k_out_bias_to_fp16"), val = tensor<fp16, [1026]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30815488)))]; |
| tensor<fp16, [1, 565, 1026]> linear_17_cast_fp16 = linear(bias = head_48k_out_bias_to_fp16, weight = head_48k_out_weight_to_fp16, x = upsampled_cast_fp16)[name = tensor<string, []>("linear_17_cast_fp16")]; |
| tensor<int32, [3]> var_438_begin_0 = const()[name = tensor<string, []>("op_438_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> var_438_end_0 = const()[name = tensor<string, []>("op_438_end_0"), val = tensor<int32, [3]>([1, 565, 513])]; |
| tensor<bool, [3]> var_438_end_mask_0 = const()[name = tensor<string, []>("op_438_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 565, 513]> var_438_cast_fp16 = slice_by_index(begin = var_438_begin_0, end = var_438_end_0, end_mask = var_438_end_mask_0, x = linear_17_cast_fp16)[name = tensor<string, []>("op_438_cast_fp16")]; |
| tensor<fp16, []> const_0_to_fp16 = const()[name = tensor<string, []>("const_0_to_fp16"), val = tensor<fp16, []>(-inf)]; |
| tensor<fp16, []> var_422_to_fp16 = const()[name = tensor<string, []>("op_422_to_fp16"), val = tensor<fp16, []>(0x1.26cp+2)]; |
| tensor<fp16, [1, 565, 513]> clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_422_to_fp16, x = var_438_cast_fp16)[name = tensor<string, []>("clip_0_cast_fp16")]; |
| tensor<int32, [3]> phase_1_begin_0 = const()[name = tensor<string, []>("phase_1_begin_0"), val = tensor<int32, [3]>([0, 0, 513])]; |
| tensor<int32, [3]> phase_1_end_0 = const()[name = tensor<string, []>("phase_1_end_0"), val = tensor<int32, [3]>([1, 565, 1026])]; |
| tensor<bool, [3]> phase_1_end_mask_0 = const()[name = tensor<string, []>("phase_1_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp16, [1, 565, 513]> phase_1_cast_fp16 = slice_by_index(begin = phase_1_begin_0, end = phase_1_end_0, end_mask = phase_1_end_mask_0, x = linear_17_cast_fp16)[name = tensor<string, []>("phase_1_cast_fp16")]; |
| tensor<fp16, [1, 565, 513]> mag_1_cast_fp16 = exp(x = clip_0_cast_fp16)[name = tensor<string, []>("mag_1_cast_fp16")]; |
| tensor<fp16, [1, 565, 513]> var_444_cast_fp16 = cos(x = phase_1_cast_fp16)[name = tensor<string, []>("op_444_cast_fp16")]; |
| tensor<fp16, [1, 565, 513]> real_1_cast_fp16 = mul(x = mag_1_cast_fp16, y = var_444_cast_fp16)[name = tensor<string, []>("real_1_cast_fp16")]; |
| tensor<fp16, [1, 565, 513]> var_446_cast_fp16 = sin(x = phase_1_cast_fp16)[name = tensor<string, []>("op_446_cast_fp16")]; |
| tensor<fp16, [1, 565, 513]> imag_1_cast_fp16 = mul(x = mag_1_cast_fp16, y = var_446_cast_fp16)[name = tensor<string, []>("imag_1_cast_fp16")]; |
| tensor<fp16, [1024, 513]> transpose_0_to_fp16 = const()[name = tensor<string, []>("transpose_0_to_fp16"), val = tensor<fp16, [1024, 513]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30817664)))]; |
| tensor<fp16, [1024]> var_448_bias_0_to_fp16 = const()[name = tensor<string, []>("op_448_bias_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31868352)))]; |
| tensor<fp16, [1, 565, 1024]> var_448_cast_fp16 = linear(bias = var_448_bias_0_to_fp16, weight = transpose_0_to_fp16, x = real_1_cast_fp16)[name = tensor<string, []>("op_448_cast_fp16")]; |
| tensor<fp16, [1024, 513]> transpose_1_to_fp16 = const()[name = tensor<string, []>("transpose_1_to_fp16"), val = tensor<fp16, [1024, 513]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31870464)))]; |
| tensor<fp16, [1, 565, 1024]> var_449_cast_fp16 = linear(bias = var_448_bias_0_to_fp16, weight = transpose_1_to_fp16, x = imag_1_cast_fp16)[name = tensor<string, []>("op_449_cast_fp16")]; |
| tensor<fp16, [1, 565, 1024]> frames_1_cast_fp16 = add(x = var_448_cast_fp16, y = var_449_cast_fp16)[name = tensor<string, []>("frames_1_cast_fp16")]; |
| tensor<int32, [3]> var_453_begin_0 = const()[name = tensor<string, []>("op_453_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> var_453_end_0 = const()[name = tensor<string, []>("op_453_end_0"), val = tensor<int32, [3]>([1, 565, 256])]; |
| tensor<bool, [3]> var_453_end_mask_0 = const()[name = tensor<string, []>("op_453_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 565, 256]> var_453_cast_fp16 = slice_by_index(begin = var_453_begin_0, end = var_453_end_0, end_mask = var_453_end_mask_0, x = frames_1_cast_fp16)[name = tensor<string, []>("op_453_cast_fp16")]; |
| tensor<int32, [2]> var_454 = const()[name = tensor<string, []>("op_454"), val = tensor<int32, [2]>([1, 144640])]; |
| tensor<fp16, [1, 144640]> input_113_cast_fp16 = reshape(shape = var_454, x = var_453_cast_fp16)[name = tensor<string, []>("input_113_cast_fp16")]; |
| tensor<int32, [4]> y_1_pad_0 = const()[name = tensor<string, []>("y_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 768])]; |
| tensor<string, []> y_1_mode_0 = const()[name = tensor<string, []>("y_1_mode_0"), val = tensor<string, []>("constant")]; |
| tensor<fp16, []> const_1_to_fp16 = const()[name = tensor<string, []>("const_1_to_fp16"), val = tensor<fp16, []>(0x0p+0)]; |
| tensor<fp16, [1, 145408]> y_1_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = y_1_mode_0, pad = y_1_pad_0, x = input_113_cast_fp16)[name = tensor<string, []>("y_1_cast_fp16")]; |
| tensor<int32, [3]> var_460_begin_0 = const()[name = tensor<string, []>("op_460_begin_0"), val = tensor<int32, [3]>([0, 0, 256])]; |
| tensor<int32, [3]> var_460_end_0 = const()[name = tensor<string, []>("op_460_end_0"), val = tensor<int32, [3]>([1, 565, 512])]; |
| tensor<bool, [3]> var_460_end_mask_0 = const()[name = tensor<string, []>("op_460_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 565, 256]> var_460_cast_fp16 = slice_by_index(begin = var_460_begin_0, end = var_460_end_0, end_mask = var_460_end_mask_0, x = frames_1_cast_fp16)[name = tensor<string, []>("op_460_cast_fp16")]; |
| tensor<int32, [2]> var_461 = const()[name = tensor<string, []>("op_461"), val = tensor<int32, [2]>([1, 144640])]; |
| tensor<fp16, [1, 144640]> input_115_cast_fp16 = reshape(shape = var_461, x = var_460_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")]; |
| tensor<int32, [4]> part_1_pad_0 = const()[name = tensor<string, []>("part_1_pad_0"), val = tensor<int32, [4]>([0, 0, 256, 512])]; |
| tensor<string, []> part_1_mode_0 = const()[name = tensor<string, []>("part_1_mode_0"), val = tensor<string, []>("constant")]; |
| tensor<fp16, []> const_2_to_fp16 = const()[name = tensor<string, []>("const_2_to_fp16"), val = tensor<fp16, []>(0x0p+0)]; |
| tensor<fp16, [1, 145408]> part_1_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = part_1_mode_0, pad = part_1_pad_0, x = input_115_cast_fp16)[name = tensor<string, []>("part_1_cast_fp16")]; |
| tensor<fp16, [1, 145408]> y_3_cast_fp16 = add(x = y_1_cast_fp16, y = part_1_cast_fp16)[name = tensor<string, []>("y_3_cast_fp16")]; |
| tensor<int32, [3]> var_468_begin_0 = const()[name = tensor<string, []>("op_468_begin_0"), val = tensor<int32, [3]>([0, 0, 512])]; |
| tensor<int32, [3]> var_468_end_0 = const()[name = tensor<string, []>("op_468_end_0"), val = tensor<int32, [3]>([1, 565, 768])]; |
| tensor<bool, [3]> var_468_end_mask_0 = const()[name = tensor<string, []>("op_468_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 565, 256]> var_468_cast_fp16 = slice_by_index(begin = var_468_begin_0, end = var_468_end_0, end_mask = var_468_end_mask_0, x = frames_1_cast_fp16)[name = tensor<string, []>("op_468_cast_fp16")]; |
| tensor<int32, [2]> var_469 = const()[name = tensor<string, []>("op_469"), val = tensor<int32, [2]>([1, 144640])]; |
| tensor<fp16, [1, 144640]> input_117_cast_fp16 = reshape(shape = var_469, x = var_468_cast_fp16)[name = tensor<string, []>("input_117_cast_fp16")]; |
| tensor<int32, [4]> part_3_pad_0 = const()[name = tensor<string, []>("part_3_pad_0"), val = tensor<int32, [4]>([0, 0, 512, 256])]; |
| tensor<string, []> part_3_mode_0 = const()[name = tensor<string, []>("part_3_mode_0"), val = tensor<string, []>("constant")]; |
| tensor<fp16, []> const_3_to_fp16 = const()[name = tensor<string, []>("const_3_to_fp16"), val = tensor<fp16, []>(0x0p+0)]; |
| tensor<fp16, [1, 145408]> part_3_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = part_3_mode_0, pad = part_3_pad_0, x = input_117_cast_fp16)[name = tensor<string, []>("part_3_cast_fp16")]; |
| tensor<fp16, [1, 145408]> y_5_cast_fp16 = add(x = y_3_cast_fp16, y = part_3_cast_fp16)[name = tensor<string, []>("y_5_cast_fp16")]; |
| tensor<int32, [3]> var_476_begin_0 = const()[name = tensor<string, []>("op_476_begin_0"), val = tensor<int32, [3]>([0, 0, 768])]; |
| tensor<int32, [3]> var_476_end_0 = const()[name = tensor<string, []>("op_476_end_0"), val = tensor<int32, [3]>([1, 565, 1])]; |
| tensor<bool, [3]> var_476_end_mask_0 = const()[name = tensor<string, []>("op_476_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp16, [1, 565, 256]> var_476_cast_fp16 = slice_by_index(begin = var_476_begin_0, end = var_476_end_0, end_mask = var_476_end_mask_0, x = frames_1_cast_fp16)[name = tensor<string, []>("op_476_cast_fp16")]; |
| tensor<int32, [2]> var_477 = const()[name = tensor<string, []>("op_477"), val = tensor<int32, [2]>([1, 144640])]; |
| tensor<fp16, [1, 144640]> input_119_cast_fp16 = reshape(shape = var_477, x = var_476_cast_fp16)[name = tensor<string, []>("input_119_cast_fp16")]; |
| tensor<int32, [4]> part_5_pad_0 = const()[name = tensor<string, []>("part_5_pad_0"), val = tensor<int32, [4]>([0, 0, 768, 0])]; |
| tensor<string, []> part_5_mode_0 = const()[name = tensor<string, []>("part_5_mode_0"), val = tensor<string, []>("constant")]; |
| tensor<fp16, []> const_4_to_fp16 = const()[name = tensor<string, []>("const_4_to_fp16"), val = tensor<fp16, []>(0x0p+0)]; |
| tensor<fp16, [1, 145408]> part_5_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = part_5_mode_0, pad = part_5_pad_0, x = input_119_cast_fp16)[name = tensor<string, []>("part_5_cast_fp16")]; |
| tensor<fp16, [1, 145408]> y_7_cast_fp16 = add(x = y_5_cast_fp16, y = part_5_cast_fp16)[name = tensor<string, []>("y_7_cast_fp16")]; |
| tensor<int32, [2]> y_9_begin_0 = const()[name = tensor<string, []>("y_9_begin_0"), val = tensor<int32, [2]>([0, 512])]; |
| tensor<int32, [2]> y_9_end_0 = const()[name = tensor<string, []>("y_9_end_0"), val = tensor<int32, [2]>([1, 144896])]; |
| tensor<bool, [2]> y_9_end_mask_0 = const()[name = tensor<string, []>("y_9_end_mask_0"), val = tensor<bool, [2]>([true, false])]; |
| tensor<fp16, [1, 144384]> y_9_cast_fp16 = slice_by_index(begin = y_9_begin_0, end = y_9_end_0, end_mask = y_9_end_mask_0, x = y_7_cast_fp16)[name = tensor<string, []>("y_9_cast_fp16")]; |
| tensor<fp16, [1, 144384]> head_48k_env_inv_to_fp16 = const()[name = tensor<string, []>("head_48k_env_inv_to_fp16"), val = tensor<fp16, [1, 144384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32921152)))]; |
| tensor<fp16, [1, 144384]> audio_hi_cast_fp16 = mul(x = y_9_cast_fp16, y = head_48k_env_inv_to_fp16)[name = tensor<string, []>("audio_hi_cast_fp16")]; |
| tensor<fp16, [1026, 512]> head_24k_out_weight_to_fp16 = const()[name = tensor<string, []>("head_24k_out_weight_to_fp16"), val = tensor<fp16, [1026, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33209984)))]; |
| tensor<fp16, [1026]> head_24k_out_bias_to_fp16 = const()[name = tensor<string, []>("head_24k_out_bias_to_fp16"), val = tensor<fp16, [1026]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34260672)))]; |
| tensor<fp16, [1, 282, 1026]> linear_18_cast_fp16 = linear(bias = head_24k_out_bias_to_fp16, weight = head_24k_out_weight_to_fp16, x = features_cast_fp16)[name = tensor<string, []>("linear_18_cast_fp16")]; |
| tensor<int32, [3]> var_508_begin_0 = const()[name = tensor<string, []>("op_508_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> var_508_end_0 = const()[name = tensor<string, []>("op_508_end_0"), val = tensor<int32, [3]>([1, 282, 513])]; |
| tensor<bool, [3]> var_508_end_mask_0 = const()[name = tensor<string, []>("op_508_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 282, 513]> var_508_cast_fp16 = slice_by_index(begin = var_508_begin_0, end = var_508_end_0, end_mask = var_508_end_mask_0, x = linear_18_cast_fp16)[name = tensor<string, []>("op_508_cast_fp16")]; |
| tensor<fp16, []> const_5_to_fp16 = const()[name = tensor<string, []>("const_5_to_fp16"), val = tensor<fp16, []>(-inf)]; |
| tensor<fp16, []> var_492_to_fp16 = const()[name = tensor<string, []>("op_492_to_fp16"), val = tensor<fp16, []>(0x1.26cp+2)]; |
| tensor<fp16, [1, 282, 513]> clip_1_cast_fp16 = clip(alpha = const_5_to_fp16, beta = var_492_to_fp16, x = var_508_cast_fp16)[name = tensor<string, []>("clip_1_cast_fp16")]; |
| tensor<int32, [3]> phase_begin_0 = const()[name = tensor<string, []>("phase_begin_0"), val = tensor<int32, [3]>([0, 0, 513])]; |
| tensor<int32, [3]> phase_end_0 = const()[name = tensor<string, []>("phase_end_0"), val = tensor<int32, [3]>([1, 282, 1026])]; |
| tensor<bool, [3]> phase_end_mask_0 = const()[name = tensor<string, []>("phase_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp16, [1, 282, 513]> phase_cast_fp16 = slice_by_index(begin = phase_begin_0, end = phase_end_0, end_mask = phase_end_mask_0, x = linear_18_cast_fp16)[name = tensor<string, []>("phase_cast_fp16")]; |
| tensor<fp16, [1, 282, 513]> mag_cast_fp16 = exp(x = clip_1_cast_fp16)[name = tensor<string, []>("mag_cast_fp16")]; |
| tensor<fp16, [1, 282, 513]> var_514_cast_fp16 = cos(x = phase_cast_fp16)[name = tensor<string, []>("op_514_cast_fp16")]; |
| tensor<fp16, [1, 282, 513]> real_cast_fp16 = mul(x = mag_cast_fp16, y = var_514_cast_fp16)[name = tensor<string, []>("real_cast_fp16")]; |
| tensor<fp16, [1, 282, 513]> var_516_cast_fp16 = sin(x = phase_cast_fp16)[name = tensor<string, []>("op_516_cast_fp16")]; |
| tensor<fp16, [1, 282, 513]> imag_cast_fp16 = mul(x = mag_cast_fp16, y = var_516_cast_fp16)[name = tensor<string, []>("imag_cast_fp16")]; |
| tensor<fp16, [1, 282, 1024]> var_518_cast_fp16 = linear(bias = var_448_bias_0_to_fp16, weight = transpose_0_to_fp16, x = real_cast_fp16)[name = tensor<string, []>("op_518_cast_fp16")]; |
| tensor<fp16, [1, 282, 1024]> var_519_cast_fp16 = linear(bias = var_448_bias_0_to_fp16, weight = transpose_1_to_fp16, x = imag_cast_fp16)[name = tensor<string, []>("op_519_cast_fp16")]; |
| tensor<fp16, [1, 282, 1024]> frames_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = tensor<string, []>("frames_cast_fp16")]; |
| tensor<int32, [3]> var_523_begin_0 = const()[name = tensor<string, []>("op_523_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> var_523_end_0 = const()[name = tensor<string, []>("op_523_end_0"), val = tensor<int32, [3]>([1, 282, 256])]; |
| tensor<bool, [3]> var_523_end_mask_0 = const()[name = tensor<string, []>("op_523_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 282, 256]> var_523_cast_fp16 = slice_by_index(begin = var_523_begin_0, end = var_523_end_0, end_mask = var_523_end_mask_0, x = frames_cast_fp16)[name = tensor<string, []>("op_523_cast_fp16")]; |
| tensor<int32, [2]> var_524 = const()[name = tensor<string, []>("op_524"), val = tensor<int32, [2]>([1, 72192])]; |
| tensor<fp16, [1, 72192]> input_121_cast_fp16 = reshape(shape = var_524, x = var_523_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")]; |
| tensor<int32, [4]> y_11_pad_0 = const()[name = tensor<string, []>("y_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 768])]; |
| tensor<string, []> y_11_mode_0 = const()[name = tensor<string, []>("y_11_mode_0"), val = tensor<string, []>("constant")]; |
| tensor<fp16, []> const_6_to_fp16 = const()[name = tensor<string, []>("const_6_to_fp16"), val = tensor<fp16, []>(0x0p+0)]; |
| tensor<fp16, [1, 72960]> y_11_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = y_11_mode_0, pad = y_11_pad_0, x = input_121_cast_fp16)[name = tensor<string, []>("y_11_cast_fp16")]; |
| tensor<int32, [3]> var_530_begin_0 = const()[name = tensor<string, []>("op_530_begin_0"), val = tensor<int32, [3]>([0, 0, 256])]; |
| tensor<int32, [3]> var_530_end_0 = const()[name = tensor<string, []>("op_530_end_0"), val = tensor<int32, [3]>([1, 282, 512])]; |
| tensor<bool, [3]> var_530_end_mask_0 = const()[name = tensor<string, []>("op_530_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 282, 256]> var_530_cast_fp16 = slice_by_index(begin = var_530_begin_0, end = var_530_end_0, end_mask = var_530_end_mask_0, x = frames_cast_fp16)[name = tensor<string, []>("op_530_cast_fp16")]; |
| tensor<int32, [2]> var_531 = const()[name = tensor<string, []>("op_531"), val = tensor<int32, [2]>([1, 72192])]; |
| tensor<fp16, [1, 72192]> input_123_cast_fp16 = reshape(shape = var_531, x = var_530_cast_fp16)[name = tensor<string, []>("input_123_cast_fp16")]; |
| tensor<int32, [4]> part_7_pad_0 = const()[name = tensor<string, []>("part_7_pad_0"), val = tensor<int32, [4]>([0, 0, 256, 512])]; |
| tensor<string, []> part_7_mode_0 = const()[name = tensor<string, []>("part_7_mode_0"), val = tensor<string, []>("constant")]; |
| tensor<fp16, []> const_7_to_fp16 = const()[name = tensor<string, []>("const_7_to_fp16"), val = tensor<fp16, []>(0x0p+0)]; |
| tensor<fp16, [1, 72960]> part_7_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = part_7_mode_0, pad = part_7_pad_0, x = input_123_cast_fp16)[name = tensor<string, []>("part_7_cast_fp16")]; |
| tensor<fp16, [1, 72960]> y_13_cast_fp16 = add(x = y_11_cast_fp16, y = part_7_cast_fp16)[name = tensor<string, []>("y_13_cast_fp16")]; |
| tensor<int32, [3]> var_538_begin_0 = const()[name = tensor<string, []>("op_538_begin_0"), val = tensor<int32, [3]>([0, 0, 512])]; |
| tensor<int32, [3]> var_538_end_0 = const()[name = tensor<string, []>("op_538_end_0"), val = tensor<int32, [3]>([1, 282, 768])]; |
| tensor<bool, [3]> var_538_end_mask_0 = const()[name = tensor<string, []>("op_538_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 282, 256]> var_538_cast_fp16 = slice_by_index(begin = var_538_begin_0, end = var_538_end_0, end_mask = var_538_end_mask_0, x = frames_cast_fp16)[name = tensor<string, []>("op_538_cast_fp16")]; |
| tensor<int32, [2]> var_539 = const()[name = tensor<string, []>("op_539"), val = tensor<int32, [2]>([1, 72192])]; |
| tensor<fp16, [1, 72192]> input_125_cast_fp16 = reshape(shape = var_539, x = var_538_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")]; |
| tensor<int32, [4]> part_9_pad_0 = const()[name = tensor<string, []>("part_9_pad_0"), val = tensor<int32, [4]>([0, 0, 512, 256])]; |
| tensor<string, []> part_9_mode_0 = const()[name = tensor<string, []>("part_9_mode_0"), val = tensor<string, []>("constant")]; |
| tensor<fp16, []> const_8_to_fp16 = const()[name = tensor<string, []>("const_8_to_fp16"), val = tensor<fp16, []>(0x0p+0)]; |
| tensor<fp16, [1, 72960]> part_9_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = part_9_mode_0, pad = part_9_pad_0, x = input_125_cast_fp16)[name = tensor<string, []>("part_9_cast_fp16")]; |
| tensor<fp16, [1, 72960]> y_15_cast_fp16 = add(x = y_13_cast_fp16, y = part_9_cast_fp16)[name = tensor<string, []>("y_15_cast_fp16")]; |
| tensor<int32, [3]> var_546_begin_0 = const()[name = tensor<string, []>("op_546_begin_0"), val = tensor<int32, [3]>([0, 0, 768])]; |
| tensor<int32, [3]> var_546_end_0 = const()[name = tensor<string, []>("op_546_end_0"), val = tensor<int32, [3]>([1, 282, 1])]; |
| tensor<bool, [3]> var_546_end_mask_0 = const()[name = tensor<string, []>("op_546_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp16, [1, 282, 256]> var_546_cast_fp16 = slice_by_index(begin = var_546_begin_0, end = var_546_end_0, end_mask = var_546_end_mask_0, x = frames_cast_fp16)[name = tensor<string, []>("op_546_cast_fp16")]; |
| tensor<int32, [2]> var_547 = const()[name = tensor<string, []>("op_547"), val = tensor<int32, [2]>([1, 72192])]; |
| tensor<fp16, [1, 72192]> input_127_cast_fp16 = reshape(shape = var_547, x = var_546_cast_fp16)[name = tensor<string, []>("input_127_cast_fp16")]; |
| tensor<int32, [4]> part_pad_0 = const()[name = tensor<string, []>("part_pad_0"), val = tensor<int32, [4]>([0, 0, 768, 0])]; |
| tensor<string, []> part_mode_0 = const()[name = tensor<string, []>("part_mode_0"), val = tensor<string, []>("constant")]; |
| tensor<fp16, []> const_9_to_fp16 = const()[name = tensor<string, []>("const_9_to_fp16"), val = tensor<fp16, []>(0x0p+0)]; |
| tensor<fp16, [1, 72960]> part_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = part_mode_0, pad = part_pad_0, x = input_127_cast_fp16)[name = tensor<string, []>("part_cast_fp16")]; |
| tensor<fp16, [1, 72960]> y_17_cast_fp16 = add(x = y_15_cast_fp16, y = part_cast_fp16)[name = tensor<string, []>("y_17_cast_fp16")]; |
| tensor<int32, [2]> y_19_begin_0 = const()[name = tensor<string, []>("y_19_begin_0"), val = tensor<int32, [2]>([0, 512])]; |
| tensor<int32, [2]> y_19_end_0 = const()[name = tensor<string, []>("y_19_end_0"), val = tensor<int32, [2]>([1, 72448])]; |
| tensor<bool, [2]> y_19_end_mask_0 = const()[name = tensor<string, []>("y_19_end_mask_0"), val = tensor<bool, [2]>([true, false])]; |
| tensor<fp16, [1, 71936]> y_19_cast_fp16 = slice_by_index(begin = y_19_begin_0, end = y_19_end_0, end_mask = y_19_end_mask_0, x = y_17_cast_fp16)[name = tensor<string, []>("y_19_cast_fp16")]; |
| tensor<fp16, [1, 71936]> head_24k_env_inv_to_fp16 = const()[name = tensor<string, []>("head_24k_env_inv_to_fp16"), val = tensor<fp16, [1, 71936]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34262848)))]; |
| tensor<fp16, [1, 71936]> wav_cast_fp16 = mul(x = y_19_cast_fp16, y = head_24k_env_inv_to_fp16)[name = tensor<string, []>("wav_cast_fp16")]; |
| tensor<int32, [1]> input_axes_0 = const()[name = tensor<string, []>("input_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp16, [1, 1, 71936]> input_cast_fp16 = expand_dims(axes = input_axes_0, x = wav_cast_fp16)[name = tensor<string, []>("input_cast_fp16")]; |
| tensor<int32, [6]> x_pad_0 = const()[name = tensor<string, []>("x_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 7, 8])]; |
| tensor<string, []> x_mode_0 = const()[name = tensor<string, []>("x_mode_0"), val = tensor<string, []>("constant")]; |
| tensor<fp16, []> const_10_to_fp16 = const()[name = tensor<string, []>("const_10_to_fp16"), val = tensor<fp16, []>(0x0p+0)]; |
| tensor<fp16, [1, 1, 71951]> x_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = x_mode_0, pad = x_pad_0, x = input_cast_fp16)[name = tensor<string, []>("x_cast_fp16")]; |
| tensor<string, []> y_21_pad_type_0 = const()[name = tensor<string, []>("y_21_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> y_21_strides_0 = const()[name = tensor<string, []>("y_21_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> y_21_pad_0 = const()[name = tensor<string, []>("y_21_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> y_21_dilations_0 = const()[name = tensor<string, []>("y_21_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> y_21_groups_0 = const()[name = tensor<string, []>("y_21_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [2, 1, 15]> resample_kernel_to_fp16 = const()[name = tensor<string, []>("resample_kernel_to_fp16"), val = tensor<fp16, [2, 1, 15]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34406784)))]; |
| tensor<fp16, [1, 2, 71937]> y_21_cast_fp16 = conv(dilations = y_21_dilations_0, groups = y_21_groups_0, pad = y_21_pad_0, pad_type = y_21_pad_type_0, strides = y_21_strides_0, weight = resample_kernel_to_fp16, x = x_cast_fp16)[name = tensor<string, []>("y_21_cast_fp16")]; |
| tensor<int32, [3]> var_576_perm_0 = const()[name = tensor<string, []>("op_576_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [2]> var_577 = const()[name = tensor<string, []>("op_577"), val = tensor<int32, [2]>([1, -1])]; |
| tensor<fp16, [1, 71937, 2]> var_576_cast_fp16 = transpose(perm = var_576_perm_0, x = y_21_cast_fp16)[name = tensor<string, []>("transpose_12")]; |
| tensor<fp16, [1, 143874]> y_cast_fp16 = reshape(shape = var_577, x = var_576_cast_fp16)[name = tensor<string, []>("y_cast_fp16")]; |
| tensor<int32, [2]> low_begin_0 = const()[name = tensor<string, []>("low_begin_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [2]> low_end_0 = const()[name = tensor<string, []>("low_end_0"), val = tensor<int32, [2]>([1, 143872])]; |
| tensor<bool, [2]> low_end_mask_0 = const()[name = tensor<string, []>("low_end_mask_0"), val = tensor<bool, [2]>([true, false])]; |
| tensor<fp16, [1, 143872]> low_cast_fp16 = slice_by_index(begin = low_begin_0, end = low_end_0, end_mask = low_end_mask_0, x = y_cast_fp16)[name = tensor<string, []>("low_cast_fp16")]; |
| tensor<int32, [2]> high_begin_0 = const()[name = tensor<string, []>("high_begin_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [2]> high_end_0 = const()[name = tensor<string, []>("high_end_0"), val = tensor<int32, [2]>([1, 143872])]; |
| tensor<bool, [2]> high_end_mask_0 = const()[name = tensor<string, []>("high_end_mask_0"), val = tensor<bool, [2]>([true, false])]; |
| tensor<fp16, [1, 143872]> high_cast_fp16 = slice_by_index(begin = high_begin_0, end = high_end_0, end_mask = high_end_mask_0, x = audio_hi_cast_fp16)[name = tensor<string, []>("high_cast_fp16")]; |
| tensor<fp16, [1, 143872]> var_598_cast_fp16 = sub(x = high_cast_fp16, y = low_cast_fp16)[name = tensor<string, []>("op_598_cast_fp16")]; |
| tensor<int32, [1]> d_axes_0 = const()[name = tensor<string, []>("d_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp16, [1, 1, 143872]> d_cast_fp16 = expand_dims(axes = d_axes_0, x = var_598_cast_fp16)[name = tensor<string, []>("d_cast_fp16")]; |
| tensor<string, []> hp_pad_type_0 = const()[name = tensor<string, []>("hp_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> hp_pad_0 = const()[name = tensor<string, []>("hp_pad_0"), val = tensor<int32, [2]>([255, 255])]; |
| tensor<int32, [1]> hp_strides_0 = const()[name = tensor<string, []>("hp_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> hp_dilations_0 = const()[name = tensor<string, []>("hp_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> hp_groups_0 = const()[name = tensor<string, []>("hp_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [1, 1, 511]> crossover_weight_to_fp16 = const()[name = tensor<string, []>("crossover_weight_to_fp16"), val = tensor<fp16, [1, 1, 511]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34406912)))]; |
| tensor<fp16, [1, 1, 143872]> hp_cast_fp16 = conv(dilations = hp_dilations_0, groups = hp_groups_0, pad = hp_pad_0, pad_type = hp_pad_type_0, strides = hp_strides_0, weight = crossover_weight_to_fp16, x = d_cast_fp16)[name = tensor<string, []>("hp_cast_fp16")]; |
| tensor<int32, [1]> var_605_axes_0 = const()[name = tensor<string, []>("op_605_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp16, [1, 143872]> var_605_cast_fp16 = squeeze(axes = var_605_axes_0, x = hp_cast_fp16)[name = tensor<string, []>("op_605_cast_fp16")]; |
| tensor<fp16, [1, 143872]> var_606_cast_fp16 = add(x = low_cast_fp16, y = var_605_cast_fp16)[name = tensor<string, []>("op_606_cast_fp16")]; |
| tensor<string, []> var_606_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_606_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")]; |
| tensor<fp32, [1, 143872]> audio = cast(dtype = var_606_cast_fp16_to_fp32_dtype_0, x = var_606_cast_fp16)[name = tensor<string, []>("cast_9")]; |
| } -> (audio); |
| } |
