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WeSpeaker-ResNet34-LM-CoreML

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leduclinh aufklarer commited on
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Duplicate from aufklarer/WeSpeaker-ResNet34-LM-CoreML

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Co-authored-by: Ivan <aufklarer@users.noreply.huggingface.co>

Files changed (7) hide show
  1. .gitattributes +35 -0
  2. config.json +19 -0
  3. wespeaker.mlmodelc/analytics/coremldata.bin +3 -0
  4. wespeaker.mlmodelc/coremldata.bin +3 -0
  5. wespeaker.mlmodelc/metadata.json +81 -0
  6. wespeaker.mlmodelc/model.mil +387 -0
  7. wespeaker.mlmodelc/weights/weight.bin +3 -0
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+ program(1.0)
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+ [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
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+ {
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+ func main<ios17>(tensor<fp16, [1, 1, ?, 80]> mel) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>>>((("DefaultShapes", {{"mel", [1, 1, 20, 80]}}), ("EnumeratedShapes", {{"mel_1_1_1_1000_80_", {{"mel", [1, 1, 1000, 80]}}}, {"mel_1_1_1_100_80_", {{"mel", [1, 1, 100, 80]}}}, {"mel_1_1_1_1500_80_", {{"mel", [1, 1, 1500, 80]}}}, {"mel_1_1_1_2000_80_", {{"mel", [1, 1, 2000, 80]}}}, {"mel_1_1_1_200_80_", {{"mel", [1, 1, 200, 80]}}}, {"mel_1_1_1_20_80_", {{"mel", [1, 1, 20, 80]}}}, {"mel_1_1_1_300_80_", {{"mel", [1, 1, 300, 80]}}}, {"mel_1_1_1_500_80_", {{"mel", [1, 1, 500, 80]}}}, {"mel_1_1_1_50_80_", {{"mel", [1, 1, 50, 80]}}}, {"mel_1_1_1_750_80_", {{"mel", [1, 1, 750, 80]}}}})))] {
5
+ tensor<string, []> input_1_pad_type_0 = const()[name = tensor<string, []>("input_1_pad_type_0"), val = tensor<string, []>("custom")];
6
+ tensor<int32, [4]> input_1_pad_0 = const()[name = tensor<string, []>("input_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
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+ tensor<int32, [2]> input_1_strides_0 = const()[name = tensor<string, []>("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
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+ tensor<int32, [2]> input_1_dilations_0 = const()[name = tensor<string, []>("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
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+ tensor<int32, []> input_1_groups_0 = const()[name = tensor<string, []>("input_1_groups_0"), val = tensor<int32, []>(1)];
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+ tensor<fp16, [32, 1, 3, 3]> conv1_weight_to_fp16 = const()[name = tensor<string, []>("conv1_weight_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
11
+ tensor<fp16, [32]> conv1_bias_to_fp16 = const()[name = tensor<string, []>("conv1_bias_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704)))];
12
+ tensor<fp16, [1, 32, ?, 80]> input_1_cast_fp16 = conv(bias = conv1_bias_to_fp16, dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = conv1_weight_to_fp16, x = mel)[name = tensor<string, []>("input_1_cast_fp16")];
13
+ tensor<fp16, [1, 32, ?, 80]> input_3_cast_fp16 = relu(x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
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+ tensor<string, []> input_5_pad_type_0 = const()[name = tensor<string, []>("input_5_pad_type_0"), val = tensor<string, []>("custom")];
15
+ tensor<int32, [4]> input_5_pad_0 = const()[name = tensor<string, []>("input_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
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+ tensor<int32, [2]> input_5_strides_0 = const()[name = tensor<string, []>("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
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+ tensor<int32, [2]> input_5_dilations_0 = const()[name = tensor<string, []>("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
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+ tensor<int32, []> input_5_groups_0 = const()[name = tensor<string, []>("input_5_groups_0"), val = tensor<int32, []>(1)];
19
+ tensor<fp16, [32, 32, 3, 3]> layer1_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer1_0_conv1_weight_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(832)))];
20
+ tensor<fp16, [32]> layer1_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer1_0_conv1_bias_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19328)))];
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+ tensor<fp16, [1, 32, ?, 80]> input_5_cast_fp16 = conv(bias = layer1_0_conv1_bias_to_fp16, dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = layer1_0_conv1_weight_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
22
+ tensor<fp16, [1, 32, ?, 80]> input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
23
+ tensor<string, []> out_1_pad_type_0 = const()[name = tensor<string, []>("out_1_pad_type_0"), val = tensor<string, []>("custom")];
24
+ tensor<int32, [4]> out_1_pad_0 = const()[name = tensor<string, []>("out_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
25
+ tensor<int32, [2]> out_1_strides_0 = const()[name = tensor<string, []>("out_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
26
+ tensor<int32, [2]> out_1_dilations_0 = const()[name = tensor<string, []>("out_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
27
+ tensor<int32, []> out_1_groups_0 = const()[name = tensor<string, []>("out_1_groups_0"), val = tensor<int32, []>(1)];
28
+ tensor<fp16, [32, 32, 3, 3]> layer1_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer1_0_conv2_weight_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19456)))];
29
+ tensor<fp16, [32]> layer1_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer1_0_conv2_bias_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37952)))];
30
+ tensor<fp16, [1, 32, ?, 80]> out_1_cast_fp16 = conv(bias = layer1_0_conv2_bias_to_fp16, dilations = out_1_dilations_0, groups = out_1_groups_0, pad = out_1_pad_0, pad_type = out_1_pad_type_0, strides = out_1_strides_0, weight = layer1_0_conv2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("out_1_cast_fp16")];
31
+ tensor<fp16, [1, 32, ?, 80]> input_9_cast_fp16 = add(x = out_1_cast_fp16, y = input_3_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
32
+ tensor<fp16, [1, 32, ?, 80]> input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
33
+ tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")];
34
+ tensor<int32, [4]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
35
+ tensor<int32, [2]> input_13_strides_0 = const()[name = tensor<string, []>("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
36
+ tensor<int32, [2]> input_13_dilations_0 = const()[name = tensor<string, []>("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
37
+ tensor<int32, []> input_13_groups_0 = const()[name = tensor<string, []>("input_13_groups_0"), val = tensor<int32, []>(1)];
38
+ tensor<fp16, [32, 32, 3, 3]> layer1_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer1_1_conv1_weight_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38080)))];
39
+ tensor<fp16, [32]> layer1_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer1_1_conv1_bias_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56576)))];
40
+ tensor<fp16, [1, 32, ?, 80]> input_13_cast_fp16 = conv(bias = layer1_1_conv1_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = layer1_1_conv1_weight_to_fp16, x = input_11_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
41
+ tensor<fp16, [1, 32, ?, 80]> input_15_cast_fp16 = relu(x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
42
+ tensor<string, []> out_3_pad_type_0 = const()[name = tensor<string, []>("out_3_pad_type_0"), val = tensor<string, []>("custom")];
43
+ tensor<int32, [4]> out_3_pad_0 = const()[name = tensor<string, []>("out_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
44
+ tensor<int32, [2]> out_3_strides_0 = const()[name = tensor<string, []>("out_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
45
+ tensor<int32, [2]> out_3_dilations_0 = const()[name = tensor<string, []>("out_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
46
+ tensor<int32, []> out_3_groups_0 = const()[name = tensor<string, []>("out_3_groups_0"), val = tensor<int32, []>(1)];
47
+ tensor<fp16, [32, 32, 3, 3]> layer1_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer1_1_conv2_weight_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56704)))];
48
+ tensor<fp16, [32]> layer1_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer1_1_conv2_bias_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75200)))];
49
+ tensor<fp16, [1, 32, ?, 80]> out_3_cast_fp16 = conv(bias = layer1_1_conv2_bias_to_fp16, dilations = out_3_dilations_0, groups = out_3_groups_0, pad = out_3_pad_0, pad_type = out_3_pad_type_0, strides = out_3_strides_0, weight = layer1_1_conv2_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("out_3_cast_fp16")];
50
+ tensor<fp16, [1, 32, ?, 80]> input_17_cast_fp16 = add(x = out_3_cast_fp16, y = input_11_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
51
+ tensor<fp16, [1, 32, ?, 80]> input_19_cast_fp16 = relu(x = input_17_cast_fp16)[name = tensor<string, []>("input_19_cast_fp16")];
52
+ tensor<string, []> input_21_pad_type_0 = const()[name = tensor<string, []>("input_21_pad_type_0"), val = tensor<string, []>("custom")];
53
+ tensor<int32, [4]> input_21_pad_0 = const()[name = tensor<string, []>("input_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
54
+ tensor<int32, [2]> input_21_strides_0 = const()[name = tensor<string, []>("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
55
+ tensor<int32, [2]> input_21_dilations_0 = const()[name = tensor<string, []>("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
56
+ tensor<int32, []> input_21_groups_0 = const()[name = tensor<string, []>("input_21_groups_0"), val = tensor<int32, []>(1)];
57
+ tensor<fp16, [32, 32, 3, 3]> layer1_2_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer1_2_conv1_weight_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75328)))];
58
+ tensor<fp16, [32]> layer1_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer1_2_conv1_bias_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93824)))];
59
+ tensor<fp16, [1, 32, ?, 80]> input_21_cast_fp16 = conv(bias = layer1_2_conv1_bias_to_fp16, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = layer1_2_conv1_weight_to_fp16, x = input_19_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
60
+ tensor<fp16, [1, 32, ?, 80]> input_23_cast_fp16 = relu(x = input_21_cast_fp16)[name = tensor<string, []>("input_23_cast_fp16")];
61
+ tensor<string, []> out_5_pad_type_0 = const()[name = tensor<string, []>("out_5_pad_type_0"), val = tensor<string, []>("custom")];
62
+ tensor<int32, [4]> out_5_pad_0 = const()[name = tensor<string, []>("out_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
63
+ tensor<int32, [2]> out_5_strides_0 = const()[name = tensor<string, []>("out_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
64
+ tensor<int32, [2]> out_5_dilations_0 = const()[name = tensor<string, []>("out_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
65
+ tensor<int32, []> out_5_groups_0 = const()[name = tensor<string, []>("out_5_groups_0"), val = tensor<int32, []>(1)];
66
+ tensor<fp16, [32, 32, 3, 3]> layer1_2_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer1_2_conv2_weight_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93952)))];
67
+ tensor<fp16, [32]> layer1_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer1_2_conv2_bias_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112448)))];
68
+ tensor<fp16, [1, 32, ?, 80]> out_5_cast_fp16 = conv(bias = layer1_2_conv2_bias_to_fp16, dilations = out_5_dilations_0, groups = out_5_groups_0, pad = out_5_pad_0, pad_type = out_5_pad_type_0, strides = out_5_strides_0, weight = layer1_2_conv2_weight_to_fp16, x = input_23_cast_fp16)[name = tensor<string, []>("out_5_cast_fp16")];
69
+ tensor<fp16, [1, 32, ?, 80]> input_25_cast_fp16 = add(x = out_5_cast_fp16, y = input_19_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")];
70
+ tensor<fp16, [1, 32, ?, 80]> input_27_cast_fp16 = relu(x = input_25_cast_fp16)[name = tensor<string, []>("input_27_cast_fp16")];
71
+ tensor<string, []> input_29_pad_type_0 = const()[name = tensor<string, []>("input_29_pad_type_0"), val = tensor<string, []>("custom")];
72
+ tensor<int32, [4]> input_29_pad_0 = const()[name = tensor<string, []>("input_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
73
+ tensor<int32, [2]> input_29_strides_0 = const()[name = tensor<string, []>("input_29_strides_0"), val = tensor<int32, [2]>([2, 2])];
74
+ tensor<int32, [2]> input_29_dilations_0 = const()[name = tensor<string, []>("input_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
75
+ tensor<int32, []> input_29_groups_0 = const()[name = tensor<string, []>("input_29_groups_0"), val = tensor<int32, []>(1)];
76
+ tensor<fp16, [64, 32, 3, 3]> layer2_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer2_0_conv1_weight_to_fp16"), val = tensor<fp16, [64, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112576)))];
77
+ tensor<fp16, [64]> layer2_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer2_0_conv1_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149504)))];
78
+ tensor<fp16, [1, 64, ?, 40]> input_29_cast_fp16 = conv(bias = layer2_0_conv1_bias_to_fp16, dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = layer2_0_conv1_weight_to_fp16, x = input_27_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
79
+ tensor<fp16, [1, 64, ?, 40]> input_31_cast_fp16 = relu(x = input_29_cast_fp16)[name = tensor<string, []>("input_31_cast_fp16")];
80
+ tensor<string, []> out_7_pad_type_0 = const()[name = tensor<string, []>("out_7_pad_type_0"), val = tensor<string, []>("custom")];
81
+ tensor<int32, [4]> out_7_pad_0 = const()[name = tensor<string, []>("out_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
82
+ tensor<int32, [2]> out_7_strides_0 = const()[name = tensor<string, []>("out_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
83
+ tensor<int32, [2]> out_7_dilations_0 = const()[name = tensor<string, []>("out_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
84
+ tensor<int32, []> out_7_groups_0 = const()[name = tensor<string, []>("out_7_groups_0"), val = tensor<int32, []>(1)];
85
+ tensor<fp16, [64, 64, 3, 3]> layer2_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer2_0_conv2_weight_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149696)))];
86
+ tensor<fp16, [64]> layer2_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer2_0_conv2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(223488)))];
87
+ tensor<fp16, [1, 64, ?, 40]> out_7_cast_fp16 = conv(bias = layer2_0_conv2_bias_to_fp16, dilations = out_7_dilations_0, groups = out_7_groups_0, pad = out_7_pad_0, pad_type = out_7_pad_type_0, strides = out_7_strides_0, weight = layer2_0_conv2_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("out_7_cast_fp16")];
88
+ tensor<string, []> residual_1_pad_type_0 = const()[name = tensor<string, []>("residual_1_pad_type_0"), val = tensor<string, []>("valid")];
89
+ tensor<int32, [2]> residual_1_strides_0 = const()[name = tensor<string, []>("residual_1_strides_0"), val = tensor<int32, [2]>([2, 2])];
90
+ tensor<int32, [4]> residual_1_pad_0 = const()[name = tensor<string, []>("residual_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
91
+ tensor<int32, [2]> residual_1_dilations_0 = const()[name = tensor<string, []>("residual_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
92
+ tensor<int32, []> residual_1_groups_0 = const()[name = tensor<string, []>("residual_1_groups_0"), val = tensor<int32, []>(1)];
93
+ tensor<fp16, [64, 32, 1, 1]> layer2_0_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("layer2_0_shortcut_weight_to_fp16"), val = tensor<fp16, [64, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(223680)))];
94
+ tensor<fp16, [64]> layer2_0_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("layer2_0_shortcut_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227840)))];
95
+ tensor<fp16, [1, 64, ?, 40]> residual_1_cast_fp16 = conv(bias = layer2_0_shortcut_bias_to_fp16, dilations = residual_1_dilations_0, groups = residual_1_groups_0, pad = residual_1_pad_0, pad_type = residual_1_pad_type_0, strides = residual_1_strides_0, weight = layer2_0_shortcut_weight_to_fp16, x = input_27_cast_fp16)[name = tensor<string, []>("residual_1_cast_fp16")];
96
+ tensor<fp16, [1, 64, ?, 40]> input_33_cast_fp16 = add(x = out_7_cast_fp16, y = residual_1_cast_fp16)[name = tensor<string, []>("input_33_cast_fp16")];
97
+ tensor<fp16, [1, 64, ?, 40]> input_35_cast_fp16 = relu(x = input_33_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
98
+ tensor<string, []> input_37_pad_type_0 = const()[name = tensor<string, []>("input_37_pad_type_0"), val = tensor<string, []>("custom")];
99
+ tensor<int32, [4]> input_37_pad_0 = const()[name = tensor<string, []>("input_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
100
+ tensor<int32, [2]> input_37_strides_0 = const()[name = tensor<string, []>("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
101
+ tensor<int32, [2]> input_37_dilations_0 = const()[name = tensor<string, []>("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
102
+ tensor<int32, []> input_37_groups_0 = const()[name = tensor<string, []>("input_37_groups_0"), val = tensor<int32, []>(1)];
103
+ tensor<fp16, [64, 64, 3, 3]> layer2_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer2_1_conv1_weight_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228032)))];
104
+ tensor<fp16, [64]> layer2_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer2_1_conv1_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301824)))];
105
+ tensor<fp16, [1, 64, ?, 40]> input_37_cast_fp16 = conv(bias = layer2_1_conv1_bias_to_fp16, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = layer2_1_conv1_weight_to_fp16, x = input_35_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")];
106
+ tensor<fp16, [1, 64, ?, 40]> input_39_cast_fp16 = relu(x = input_37_cast_fp16)[name = tensor<string, []>("input_39_cast_fp16")];
107
+ tensor<string, []> out_9_pad_type_0 = const()[name = tensor<string, []>("out_9_pad_type_0"), val = tensor<string, []>("custom")];
108
+ tensor<int32, [4]> out_9_pad_0 = const()[name = tensor<string, []>("out_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
109
+ tensor<int32, [2]> out_9_strides_0 = const()[name = tensor<string, []>("out_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
110
+ tensor<int32, [2]> out_9_dilations_0 = const()[name = tensor<string, []>("out_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
111
+ tensor<int32, []> out_9_groups_0 = const()[name = tensor<string, []>("out_9_groups_0"), val = tensor<int32, []>(1)];
112
+ tensor<fp16, [64, 64, 3, 3]> layer2_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer2_1_conv2_weight_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302016)))];
113
+ tensor<fp16, [64]> layer2_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer2_1_conv2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(375808)))];
114
+ tensor<fp16, [1, 64, ?, 40]> out_9_cast_fp16 = conv(bias = layer2_1_conv2_bias_to_fp16, dilations = out_9_dilations_0, groups = out_9_groups_0, pad = out_9_pad_0, pad_type = out_9_pad_type_0, strides = out_9_strides_0, weight = layer2_1_conv2_weight_to_fp16, x = input_39_cast_fp16)[name = tensor<string, []>("out_9_cast_fp16")];
115
+ tensor<fp16, [1, 64, ?, 40]> input_41_cast_fp16 = add(x = out_9_cast_fp16, y = input_35_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")];
116
+ tensor<fp16, [1, 64, ?, 40]> input_43_cast_fp16 = relu(x = input_41_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")];
117
+ tensor<string, []> input_45_pad_type_0 = const()[name = tensor<string, []>("input_45_pad_type_0"), val = tensor<string, []>("custom")];
118
+ tensor<int32, [4]> input_45_pad_0 = const()[name = tensor<string, []>("input_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
119
+ tensor<int32, [2]> input_45_strides_0 = const()[name = tensor<string, []>("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
120
+ tensor<int32, [2]> input_45_dilations_0 = const()[name = tensor<string, []>("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
121
+ tensor<int32, []> input_45_groups_0 = const()[name = tensor<string, []>("input_45_groups_0"), val = tensor<int32, []>(1)];
122
+ tensor<fp16, [64, 64, 3, 3]> layer2_2_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer2_2_conv1_weight_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376000)))];
123
+ tensor<fp16, [64]> layer2_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer2_2_conv1_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449792)))];
124
+ tensor<fp16, [1, 64, ?, 40]> input_45_cast_fp16 = conv(bias = layer2_2_conv1_bias_to_fp16, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = layer2_2_conv1_weight_to_fp16, x = input_43_cast_fp16)[name = tensor<string, []>("input_45_cast_fp16")];
125
+ tensor<fp16, [1, 64, ?, 40]> input_47_cast_fp16 = relu(x = input_45_cast_fp16)[name = tensor<string, []>("input_47_cast_fp16")];
126
+ tensor<string, []> out_11_pad_type_0 = const()[name = tensor<string, []>("out_11_pad_type_0"), val = tensor<string, []>("custom")];
127
+ tensor<int32, [4]> out_11_pad_0 = const()[name = tensor<string, []>("out_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
128
+ tensor<int32, [2]> out_11_strides_0 = const()[name = tensor<string, []>("out_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
129
+ tensor<int32, [2]> out_11_dilations_0 = const()[name = tensor<string, []>("out_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
130
+ tensor<int32, []> out_11_groups_0 = const()[name = tensor<string, []>("out_11_groups_0"), val = tensor<int32, []>(1)];
131
+ tensor<fp16, [64, 64, 3, 3]> layer2_2_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer2_2_conv2_weight_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449984)))];
132
+ tensor<fp16, [64]> layer2_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer2_2_conv2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523776)))];
133
+ tensor<fp16, [1, 64, ?, 40]> out_11_cast_fp16 = conv(bias = layer2_2_conv2_bias_to_fp16, dilations = out_11_dilations_0, groups = out_11_groups_0, pad = out_11_pad_0, pad_type = out_11_pad_type_0, strides = out_11_strides_0, weight = layer2_2_conv2_weight_to_fp16, x = input_47_cast_fp16)[name = tensor<string, []>("out_11_cast_fp16")];
134
+ tensor<fp16, [1, 64, ?, 40]> input_49_cast_fp16 = add(x = out_11_cast_fp16, y = input_43_cast_fp16)[name = tensor<string, []>("input_49_cast_fp16")];
135
+ tensor<fp16, [1, 64, ?, 40]> input_51_cast_fp16 = relu(x = input_49_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")];
136
+ tensor<string, []> input_53_pad_type_0 = const()[name = tensor<string, []>("input_53_pad_type_0"), val = tensor<string, []>("custom")];
137
+ tensor<int32, [4]> input_53_pad_0 = const()[name = tensor<string, []>("input_53_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
138
+ tensor<int32, [2]> input_53_strides_0 = const()[name = tensor<string, []>("input_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
139
+ tensor<int32, [2]> input_53_dilations_0 = const()[name = tensor<string, []>("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
140
+ tensor<int32, []> input_53_groups_0 = const()[name = tensor<string, []>("input_53_groups_0"), val = tensor<int32, []>(1)];
141
+ tensor<fp16, [64, 64, 3, 3]> layer2_3_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer2_3_conv1_weight_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523968)))];
142
+ tensor<fp16, [64]> layer2_3_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer2_3_conv1_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597760)))];
143
+ tensor<fp16, [1, 64, ?, 40]> input_53_cast_fp16 = conv(bias = layer2_3_conv1_bias_to_fp16, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = layer2_3_conv1_weight_to_fp16, x = input_51_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")];
144
+ tensor<fp16, [1, 64, ?, 40]> input_55_cast_fp16 = relu(x = input_53_cast_fp16)[name = tensor<string, []>("input_55_cast_fp16")];
145
+ tensor<string, []> out_13_pad_type_0 = const()[name = tensor<string, []>("out_13_pad_type_0"), val = tensor<string, []>("custom")];
146
+ tensor<int32, [4]> out_13_pad_0 = const()[name = tensor<string, []>("out_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
147
+ tensor<int32, [2]> out_13_strides_0 = const()[name = tensor<string, []>("out_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
148
+ tensor<int32, [2]> out_13_dilations_0 = const()[name = tensor<string, []>("out_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
149
+ tensor<int32, []> out_13_groups_0 = const()[name = tensor<string, []>("out_13_groups_0"), val = tensor<int32, []>(1)];
150
+ tensor<fp16, [64, 64, 3, 3]> layer2_3_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer2_3_conv2_weight_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597952)))];
151
+ tensor<fp16, [64]> layer2_3_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer2_3_conv2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671744)))];
152
+ tensor<fp16, [1, 64, ?, 40]> out_13_cast_fp16 = conv(bias = layer2_3_conv2_bias_to_fp16, dilations = out_13_dilations_0, groups = out_13_groups_0, pad = out_13_pad_0, pad_type = out_13_pad_type_0, strides = out_13_strides_0, weight = layer2_3_conv2_weight_to_fp16, x = input_55_cast_fp16)[name = tensor<string, []>("out_13_cast_fp16")];
153
+ tensor<fp16, [1, 64, ?, 40]> input_57_cast_fp16 = add(x = out_13_cast_fp16, y = input_51_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
154
+ tensor<fp16, [1, 64, ?, 40]> input_59_cast_fp16 = relu(x = input_57_cast_fp16)[name = tensor<string, []>("input_59_cast_fp16")];
155
+ tensor<string, []> input_61_pad_type_0 = const()[name = tensor<string, []>("input_61_pad_type_0"), val = tensor<string, []>("custom")];
156
+ tensor<int32, [4]> input_61_pad_0 = const()[name = tensor<string, []>("input_61_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
157
+ tensor<int32, [2]> input_61_strides_0 = const()[name = tensor<string, []>("input_61_strides_0"), val = tensor<int32, [2]>([2, 2])];
158
+ tensor<int32, [2]> input_61_dilations_0 = const()[name = tensor<string, []>("input_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
159
+ tensor<int32, []> input_61_groups_0 = const()[name = tensor<string, []>("input_61_groups_0"), val = tensor<int32, []>(1)];
160
+ tensor<fp16, [128, 64, 3, 3]> layer3_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer3_0_conv1_weight_to_fp16"), val = tensor<fp16, [128, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671936)))];
161
+ tensor<fp16, [128]> layer3_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer3_0_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(819456)))];
162
+ tensor<fp16, [1, 128, ?, 20]> input_61_cast_fp16 = conv(bias = layer3_0_conv1_bias_to_fp16, dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = layer3_0_conv1_weight_to_fp16, x = input_59_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")];
163
+ tensor<fp16, [1, 128, ?, 20]> input_63_cast_fp16 = relu(x = input_61_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")];
164
+ tensor<string, []> out_15_pad_type_0 = const()[name = tensor<string, []>("out_15_pad_type_0"), val = tensor<string, []>("custom")];
165
+ tensor<int32, [4]> out_15_pad_0 = const()[name = tensor<string, []>("out_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
166
+ tensor<int32, [2]> out_15_strides_0 = const()[name = tensor<string, []>("out_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
167
+ tensor<int32, [2]> out_15_dilations_0 = const()[name = tensor<string, []>("out_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
168
+ tensor<int32, []> out_15_groups_0 = const()[name = tensor<string, []>("out_15_groups_0"), val = tensor<int32, []>(1)];
169
+ tensor<fp16, [128, 128, 3, 3]> layer3_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer3_0_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(819776)))];
170
+ tensor<fp16, [128]> layer3_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer3_0_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1114752)))];
171
+ tensor<fp16, [1, 128, ?, 20]> out_15_cast_fp16 = conv(bias = layer3_0_conv2_bias_to_fp16, dilations = out_15_dilations_0, groups = out_15_groups_0, pad = out_15_pad_0, pad_type = out_15_pad_type_0, strides = out_15_strides_0, weight = layer3_0_conv2_weight_to_fp16, x = input_63_cast_fp16)[name = tensor<string, []>("out_15_cast_fp16")];
172
+ tensor<string, []> residual_3_pad_type_0 = const()[name = tensor<string, []>("residual_3_pad_type_0"), val = tensor<string, []>("valid")];
173
+ tensor<int32, [2]> residual_3_strides_0 = const()[name = tensor<string, []>("residual_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
174
+ tensor<int32, [4]> residual_3_pad_0 = const()[name = tensor<string, []>("residual_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
175
+ tensor<int32, [2]> residual_3_dilations_0 = const()[name = tensor<string, []>("residual_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
176
+ tensor<int32, []> residual_3_groups_0 = const()[name = tensor<string, []>("residual_3_groups_0"), val = tensor<int32, []>(1)];
177
+ tensor<fp16, [128, 64, 1, 1]> layer3_0_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("layer3_0_shortcut_weight_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1115072)))];
178
+ tensor<fp16, [128]> layer3_0_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("layer3_0_shortcut_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1131520)))];
179
+ tensor<fp16, [1, 128, ?, 20]> residual_3_cast_fp16 = conv(bias = layer3_0_shortcut_bias_to_fp16, dilations = residual_3_dilations_0, groups = residual_3_groups_0, pad = residual_3_pad_0, pad_type = residual_3_pad_type_0, strides = residual_3_strides_0, weight = layer3_0_shortcut_weight_to_fp16, x = input_59_cast_fp16)[name = tensor<string, []>("residual_3_cast_fp16")];
180
+ tensor<fp16, [1, 128, ?, 20]> input_65_cast_fp16 = add(x = out_15_cast_fp16, y = residual_3_cast_fp16)[name = tensor<string, []>("input_65_cast_fp16")];
181
+ tensor<fp16, [1, 128, ?, 20]> input_67_cast_fp16 = relu(x = input_65_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")];
182
+ tensor<string, []> input_69_pad_type_0 = const()[name = tensor<string, []>("input_69_pad_type_0"), val = tensor<string, []>("custom")];
183
+ tensor<int32, [4]> input_69_pad_0 = const()[name = tensor<string, []>("input_69_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
184
+ tensor<int32, [2]> input_69_strides_0 = const()[name = tensor<string, []>("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
185
+ tensor<int32, [2]> input_69_dilations_0 = const()[name = tensor<string, []>("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
186
+ tensor<int32, []> input_69_groups_0 = const()[name = tensor<string, []>("input_69_groups_0"), val = tensor<int32, []>(1)];
187
+ tensor<fp16, [128, 128, 3, 3]> layer3_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer3_1_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1131840)))];
188
+ tensor<fp16, [128]> layer3_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer3_1_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1426816)))];
189
+ tensor<fp16, [1, 128, ?, 20]> input_69_cast_fp16 = conv(bias = layer3_1_conv1_bias_to_fp16, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = layer3_1_conv1_weight_to_fp16, x = input_67_cast_fp16)[name = tensor<string, []>("input_69_cast_fp16")];
190
+ tensor<fp16, [1, 128, ?, 20]> input_71_cast_fp16 = relu(x = input_69_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")];
191
+ tensor<string, []> out_17_pad_type_0 = const()[name = tensor<string, []>("out_17_pad_type_0"), val = tensor<string, []>("custom")];
192
+ tensor<int32, [4]> out_17_pad_0 = const()[name = tensor<string, []>("out_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
193
+ tensor<int32, [2]> out_17_strides_0 = const()[name = tensor<string, []>("out_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
194
+ tensor<int32, [2]> out_17_dilations_0 = const()[name = tensor<string, []>("out_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
195
+ tensor<int32, []> out_17_groups_0 = const()[name = tensor<string, []>("out_17_groups_0"), val = tensor<int32, []>(1)];
196
+ tensor<fp16, [128, 128, 3, 3]> layer3_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer3_1_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1427136)))];
197
+ tensor<fp16, [128]> layer3_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer3_1_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1722112)))];
198
+ tensor<fp16, [1, 128, ?, 20]> out_17_cast_fp16 = conv(bias = layer3_1_conv2_bias_to_fp16, dilations = out_17_dilations_0, groups = out_17_groups_0, pad = out_17_pad_0, pad_type = out_17_pad_type_0, strides = out_17_strides_0, weight = layer3_1_conv2_weight_to_fp16, x = input_71_cast_fp16)[name = tensor<string, []>("out_17_cast_fp16")];
199
+ tensor<fp16, [1, 128, ?, 20]> input_73_cast_fp16 = add(x = out_17_cast_fp16, y = input_67_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
200
+ tensor<fp16, [1, 128, ?, 20]> input_75_cast_fp16 = relu(x = input_73_cast_fp16)[name = tensor<string, []>("input_75_cast_fp16")];
201
+ tensor<string, []> input_77_pad_type_0 = const()[name = tensor<string, []>("input_77_pad_type_0"), val = tensor<string, []>("custom")];
202
+ tensor<int32, [4]> input_77_pad_0 = const()[name = tensor<string, []>("input_77_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
203
+ tensor<int32, [2]> input_77_strides_0 = const()[name = tensor<string, []>("input_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
204
+ tensor<int32, [2]> input_77_dilations_0 = const()[name = tensor<string, []>("input_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
205
+ tensor<int32, []> input_77_groups_0 = const()[name = tensor<string, []>("input_77_groups_0"), val = tensor<int32, []>(1)];
206
+ tensor<fp16, [128, 128, 3, 3]> layer3_2_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer3_2_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1722432)))];
207
+ tensor<fp16, [128]> layer3_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer3_2_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2017408)))];
208
+ tensor<fp16, [1, 128, ?, 20]> input_77_cast_fp16 = conv(bias = layer3_2_conv1_bias_to_fp16, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = layer3_2_conv1_weight_to_fp16, x = input_75_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")];
209
+ tensor<fp16, [1, 128, ?, 20]> input_79_cast_fp16 = relu(x = input_77_cast_fp16)[name = tensor<string, []>("input_79_cast_fp16")];
210
+ tensor<string, []> out_19_pad_type_0 = const()[name = tensor<string, []>("out_19_pad_type_0"), val = tensor<string, []>("custom")];
211
+ tensor<int32, [4]> out_19_pad_0 = const()[name = tensor<string, []>("out_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
212
+ tensor<int32, [2]> out_19_strides_0 = const()[name = tensor<string, []>("out_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
213
+ tensor<int32, [2]> out_19_dilations_0 = const()[name = tensor<string, []>("out_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
214
+ tensor<int32, []> out_19_groups_0 = const()[name = tensor<string, []>("out_19_groups_0"), val = tensor<int32, []>(1)];
215
+ tensor<fp16, [128, 128, 3, 3]> layer3_2_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer3_2_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2017728)))];
216
+ tensor<fp16, [128]> layer3_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer3_2_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2312704)))];
217
+ tensor<fp16, [1, 128, ?, 20]> out_19_cast_fp16 = conv(bias = layer3_2_conv2_bias_to_fp16, dilations = out_19_dilations_0, groups = out_19_groups_0, pad = out_19_pad_0, pad_type = out_19_pad_type_0, strides = out_19_strides_0, weight = layer3_2_conv2_weight_to_fp16, x = input_79_cast_fp16)[name = tensor<string, []>("out_19_cast_fp16")];
218
+ tensor<fp16, [1, 128, ?, 20]> input_81_cast_fp16 = add(x = out_19_cast_fp16, y = input_75_cast_fp16)[name = tensor<string, []>("input_81_cast_fp16")];
219
+ tensor<fp16, [1, 128, ?, 20]> input_83_cast_fp16 = relu(x = input_81_cast_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
220
+ tensor<string, []> input_85_pad_type_0 = const()[name = tensor<string, []>("input_85_pad_type_0"), val = tensor<string, []>("custom")];
221
+ tensor<int32, [4]> input_85_pad_0 = const()[name = tensor<string, []>("input_85_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
222
+ tensor<int32, [2]> input_85_strides_0 = const()[name = tensor<string, []>("input_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
223
+ tensor<int32, [2]> input_85_dilations_0 = const()[name = tensor<string, []>("input_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
224
+ tensor<int32, []> input_85_groups_0 = const()[name = tensor<string, []>("input_85_groups_0"), val = tensor<int32, []>(1)];
225
+ tensor<fp16, [128, 128, 3, 3]> layer3_3_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer3_3_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2313024)))];
226
+ tensor<fp16, [128]> layer3_3_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer3_3_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2608000)))];
227
+ tensor<fp16, [1, 128, ?, 20]> input_85_cast_fp16 = conv(bias = layer3_3_conv1_bias_to_fp16, dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = layer3_3_conv1_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("input_85_cast_fp16")];
228
+ tensor<fp16, [1, 128, ?, 20]> input_87_cast_fp16 = relu(x = input_85_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
229
+ tensor<string, []> out_21_pad_type_0 = const()[name = tensor<string, []>("out_21_pad_type_0"), val = tensor<string, []>("custom")];
230
+ tensor<int32, [4]> out_21_pad_0 = const()[name = tensor<string, []>("out_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
231
+ tensor<int32, [2]> out_21_strides_0 = const()[name = tensor<string, []>("out_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
232
+ tensor<int32, [2]> out_21_dilations_0 = const()[name = tensor<string, []>("out_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
233
+ tensor<int32, []> out_21_groups_0 = const()[name = tensor<string, []>("out_21_groups_0"), val = tensor<int32, []>(1)];
234
+ tensor<fp16, [128, 128, 3, 3]> layer3_3_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer3_3_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2608320)))];
235
+ tensor<fp16, [128]> layer3_3_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer3_3_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2903296)))];
236
+ tensor<fp16, [1, 128, ?, 20]> out_21_cast_fp16 = conv(bias = layer3_3_conv2_bias_to_fp16, dilations = out_21_dilations_0, groups = out_21_groups_0, pad = out_21_pad_0, pad_type = out_21_pad_type_0, strides = out_21_strides_0, weight = layer3_3_conv2_weight_to_fp16, x = input_87_cast_fp16)[name = tensor<string, []>("out_21_cast_fp16")];
237
+ tensor<fp16, [1, 128, ?, 20]> input_89_cast_fp16 = add(x = out_21_cast_fp16, y = input_83_cast_fp16)[name = tensor<string, []>("input_89_cast_fp16")];
238
+ tensor<fp16, [1, 128, ?, 20]> input_91_cast_fp16 = relu(x = input_89_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")];
239
+ tensor<string, []> input_93_pad_type_0 = const()[name = tensor<string, []>("input_93_pad_type_0"), val = tensor<string, []>("custom")];
240
+ tensor<int32, [4]> input_93_pad_0 = const()[name = tensor<string, []>("input_93_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
241
+ tensor<int32, [2]> input_93_strides_0 = const()[name = tensor<string, []>("input_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
242
+ tensor<int32, [2]> input_93_dilations_0 = const()[name = tensor<string, []>("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
243
+ tensor<int32, []> input_93_groups_0 = const()[name = tensor<string, []>("input_93_groups_0"), val = tensor<int32, []>(1)];
244
+ tensor<fp16, [128, 128, 3, 3]> layer3_4_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer3_4_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2903616)))];
245
+ tensor<fp16, [128]> layer3_4_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer3_4_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3198592)))];
246
+ tensor<fp16, [1, 128, ?, 20]> input_93_cast_fp16 = conv(bias = layer3_4_conv1_bias_to_fp16, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = layer3_4_conv1_weight_to_fp16, x = input_91_cast_fp16)[name = tensor<string, []>("input_93_cast_fp16")];
247
+ tensor<fp16, [1, 128, ?, 20]> input_95_cast_fp16 = relu(x = input_93_cast_fp16)[name = tensor<string, []>("input_95_cast_fp16")];
248
+ tensor<string, []> out_23_pad_type_0 = const()[name = tensor<string, []>("out_23_pad_type_0"), val = tensor<string, []>("custom")];
249
+ tensor<int32, [4]> out_23_pad_0 = const()[name = tensor<string, []>("out_23_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
250
+ tensor<int32, [2]> out_23_strides_0 = const()[name = tensor<string, []>("out_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
251
+ tensor<int32, [2]> out_23_dilations_0 = const()[name = tensor<string, []>("out_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
252
+ tensor<int32, []> out_23_groups_0 = const()[name = tensor<string, []>("out_23_groups_0"), val = tensor<int32, []>(1)];
253
+ tensor<fp16, [128, 128, 3, 3]> layer3_4_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer3_4_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3198912)))];
254
+ tensor<fp16, [128]> layer3_4_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer3_4_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3493888)))];
255
+ tensor<fp16, [1, 128, ?, 20]> out_23_cast_fp16 = conv(bias = layer3_4_conv2_bias_to_fp16, dilations = out_23_dilations_0, groups = out_23_groups_0, pad = out_23_pad_0, pad_type = out_23_pad_type_0, strides = out_23_strides_0, weight = layer3_4_conv2_weight_to_fp16, x = input_95_cast_fp16)[name = tensor<string, []>("out_23_cast_fp16")];
256
+ tensor<fp16, [1, 128, ?, 20]> input_97_cast_fp16 = add(x = out_23_cast_fp16, y = input_91_cast_fp16)[name = tensor<string, []>("input_97_cast_fp16")];
257
+ tensor<fp16, [1, 128, ?, 20]> input_99_cast_fp16 = relu(x = input_97_cast_fp16)[name = tensor<string, []>("input_99_cast_fp16")];
258
+ tensor<string, []> input_101_pad_type_0 = const()[name = tensor<string, []>("input_101_pad_type_0"), val = tensor<string, []>("custom")];
259
+ tensor<int32, [4]> input_101_pad_0 = const()[name = tensor<string, []>("input_101_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
260
+ tensor<int32, [2]> input_101_strides_0 = const()[name = tensor<string, []>("input_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
261
+ tensor<int32, [2]> input_101_dilations_0 = const()[name = tensor<string, []>("input_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
262
+ tensor<int32, []> input_101_groups_0 = const()[name = tensor<string, []>("input_101_groups_0"), val = tensor<int32, []>(1)];
263
+ tensor<fp16, [128, 128, 3, 3]> layer3_5_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer3_5_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3494208)))];
264
+ tensor<fp16, [128]> layer3_5_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer3_5_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3789184)))];
265
+ tensor<fp16, [1, 128, ?, 20]> input_101_cast_fp16 = conv(bias = layer3_5_conv1_bias_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = layer3_5_conv1_weight_to_fp16, x = input_99_cast_fp16)[name = tensor<string, []>("input_101_cast_fp16")];
266
+ tensor<fp16, [1, 128, ?, 20]> input_103_cast_fp16 = relu(x = input_101_cast_fp16)[name = tensor<string, []>("input_103_cast_fp16")];
267
+ tensor<string, []> out_25_pad_type_0 = const()[name = tensor<string, []>("out_25_pad_type_0"), val = tensor<string, []>("custom")];
268
+ tensor<int32, [4]> out_25_pad_0 = const()[name = tensor<string, []>("out_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
269
+ tensor<int32, [2]> out_25_strides_0 = const()[name = tensor<string, []>("out_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
270
+ tensor<int32, [2]> out_25_dilations_0 = const()[name = tensor<string, []>("out_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
271
+ tensor<int32, []> out_25_groups_0 = const()[name = tensor<string, []>("out_25_groups_0"), val = tensor<int32, []>(1)];
272
+ tensor<fp16, [128, 128, 3, 3]> layer3_5_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer3_5_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3789504)))];
273
+ tensor<fp16, [128]> layer3_5_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer3_5_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4084480)))];
274
+ tensor<fp16, [1, 128, ?, 20]> out_25_cast_fp16 = conv(bias = layer3_5_conv2_bias_to_fp16, dilations = out_25_dilations_0, groups = out_25_groups_0, pad = out_25_pad_0, pad_type = out_25_pad_type_0, strides = out_25_strides_0, weight = layer3_5_conv2_weight_to_fp16, x = input_103_cast_fp16)[name = tensor<string, []>("out_25_cast_fp16")];
275
+ tensor<fp16, [1, 128, ?, 20]> input_105_cast_fp16 = add(x = out_25_cast_fp16, y = input_99_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")];
276
+ tensor<fp16, [1, 128, ?, 20]> input_107_cast_fp16 = relu(x = input_105_cast_fp16)[name = tensor<string, []>("input_107_cast_fp16")];
277
+ tensor<string, []> input_109_pad_type_0 = const()[name = tensor<string, []>("input_109_pad_type_0"), val = tensor<string, []>("custom")];
278
+ tensor<int32, [4]> input_109_pad_0 = const()[name = tensor<string, []>("input_109_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
279
+ tensor<int32, [2]> input_109_strides_0 = const()[name = tensor<string, []>("input_109_strides_0"), val = tensor<int32, [2]>([2, 2])];
280
+ tensor<int32, [2]> input_109_dilations_0 = const()[name = tensor<string, []>("input_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
281
+ tensor<int32, []> input_109_groups_0 = const()[name = tensor<string, []>("input_109_groups_0"), val = tensor<int32, []>(1)];
282
+ tensor<fp16, [256, 128, 3, 3]> layer4_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer4_0_conv1_weight_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4084800)))];
283
+ tensor<fp16, [256]> layer4_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer4_0_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4674688)))];
284
+ tensor<fp16, [1, 256, ?, 10]> input_109_cast_fp16 = conv(bias = layer4_0_conv1_bias_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = layer4_0_conv1_weight_to_fp16, x = input_107_cast_fp16)[name = tensor<string, []>("input_109_cast_fp16")];
285
+ tensor<fp16, [1, 256, ?, 10]> input_111_cast_fp16 = relu(x = input_109_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")];
286
+ tensor<string, []> out_27_pad_type_0 = const()[name = tensor<string, []>("out_27_pad_type_0"), val = tensor<string, []>("custom")];
287
+ tensor<int32, [4]> out_27_pad_0 = const()[name = tensor<string, []>("out_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
288
+ tensor<int32, [2]> out_27_strides_0 = const()[name = tensor<string, []>("out_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
289
+ tensor<int32, [2]> out_27_dilations_0 = const()[name = tensor<string, []>("out_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
290
+ tensor<int32, []> out_27_groups_0 = const()[name = tensor<string, []>("out_27_groups_0"), val = tensor<int32, []>(1)];
291
+ tensor<fp16, [256, 256, 3, 3]> layer4_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer4_0_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4675264)))];
292
+ tensor<fp16, [256]> layer4_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer4_0_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5854976)))];
293
+ tensor<fp16, [1, 256, ?, 10]> out_27_cast_fp16 = conv(bias = layer4_0_conv2_bias_to_fp16, dilations = out_27_dilations_0, groups = out_27_groups_0, pad = out_27_pad_0, pad_type = out_27_pad_type_0, strides = out_27_strides_0, weight = layer4_0_conv2_weight_to_fp16, x = input_111_cast_fp16)[name = tensor<string, []>("out_27_cast_fp16")];
294
+ tensor<string, []> residual_pad_type_0 = const()[name = tensor<string, []>("residual_pad_type_0"), val = tensor<string, []>("valid")];
295
+ tensor<int32, [2]> residual_strides_0 = const()[name = tensor<string, []>("residual_strides_0"), val = tensor<int32, [2]>([2, 2])];
296
+ tensor<int32, [4]> residual_pad_0 = const()[name = tensor<string, []>("residual_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
297
+ tensor<int32, [2]> residual_dilations_0 = const()[name = tensor<string, []>("residual_dilations_0"), val = tensor<int32, [2]>([1, 1])];
298
+ tensor<int32, []> residual_groups_0 = const()[name = tensor<string, []>("residual_groups_0"), val = tensor<int32, []>(1)];
299
+ tensor<fp16, [256, 128, 1, 1]> layer4_0_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("layer4_0_shortcut_weight_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5855552)))];
300
+ tensor<fp16, [256]> layer4_0_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("layer4_0_shortcut_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5921152)))];
301
+ tensor<fp16, [1, 256, ?, 10]> residual_cast_fp16 = conv(bias = layer4_0_shortcut_bias_to_fp16, dilations = residual_dilations_0, groups = residual_groups_0, pad = residual_pad_0, pad_type = residual_pad_type_0, strides = residual_strides_0, weight = layer4_0_shortcut_weight_to_fp16, x = input_107_cast_fp16)[name = tensor<string, []>("residual_cast_fp16")];
302
+ tensor<fp16, [1, 256, ?, 10]> input_113_cast_fp16 = add(x = out_27_cast_fp16, y = residual_cast_fp16)[name = tensor<string, []>("input_113_cast_fp16")];
303
+ tensor<fp16, [1, 256, ?, 10]> input_115_cast_fp16 = relu(x = input_113_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")];
304
+ tensor<string, []> input_117_pad_type_0 = const()[name = tensor<string, []>("input_117_pad_type_0"), val = tensor<string, []>("custom")];
305
+ tensor<int32, [4]> input_117_pad_0 = const()[name = tensor<string, []>("input_117_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
306
+ tensor<int32, [2]> input_117_strides_0 = const()[name = tensor<string, []>("input_117_strides_0"), val = tensor<int32, [2]>([1, 1])];
307
+ tensor<int32, [2]> input_117_dilations_0 = const()[name = tensor<string, []>("input_117_dilations_0"), val = tensor<int32, [2]>([1, 1])];
308
+ tensor<int32, []> input_117_groups_0 = const()[name = tensor<string, []>("input_117_groups_0"), val = tensor<int32, []>(1)];
309
+ tensor<fp16, [256, 256, 3, 3]> layer4_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer4_1_conv1_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5921728)))];
310
+ tensor<fp16, [256]> layer4_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer4_1_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7101440)))];
311
+ tensor<fp16, [1, 256, ?, 10]> input_117_cast_fp16 = conv(bias = layer4_1_conv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = layer4_1_conv1_weight_to_fp16, x = input_115_cast_fp16)[name = tensor<string, []>("input_117_cast_fp16")];
312
+ tensor<fp16, [1, 256, ?, 10]> input_119_cast_fp16 = relu(x = input_117_cast_fp16)[name = tensor<string, []>("input_119_cast_fp16")];
313
+ tensor<string, []> out_29_pad_type_0 = const()[name = tensor<string, []>("out_29_pad_type_0"), val = tensor<string, []>("custom")];
314
+ tensor<int32, [4]> out_29_pad_0 = const()[name = tensor<string, []>("out_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
315
+ tensor<int32, [2]> out_29_strides_0 = const()[name = tensor<string, []>("out_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
316
+ tensor<int32, [2]> out_29_dilations_0 = const()[name = tensor<string, []>("out_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
317
+ tensor<int32, []> out_29_groups_0 = const()[name = tensor<string, []>("out_29_groups_0"), val = tensor<int32, []>(1)];
318
+ tensor<fp16, [256, 256, 3, 3]> layer4_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer4_1_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7102016)))];
319
+ tensor<fp16, [256]> layer4_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer4_1_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8281728)))];
320
+ tensor<fp16, [1, 256, ?, 10]> out_29_cast_fp16 = conv(bias = layer4_1_conv2_bias_to_fp16, dilations = out_29_dilations_0, groups = out_29_groups_0, pad = out_29_pad_0, pad_type = out_29_pad_type_0, strides = out_29_strides_0, weight = layer4_1_conv2_weight_to_fp16, x = input_119_cast_fp16)[name = tensor<string, []>("out_29_cast_fp16")];
321
+ tensor<fp16, [1, 256, ?, 10]> input_121_cast_fp16 = add(x = out_29_cast_fp16, y = input_115_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")];
322
+ tensor<fp16, [1, 256, ?, 10]> input_123_cast_fp16 = relu(x = input_121_cast_fp16)[name = tensor<string, []>("input_123_cast_fp16")];
323
+ tensor<string, []> input_125_pad_type_0 = const()[name = tensor<string, []>("input_125_pad_type_0"), val = tensor<string, []>("custom")];
324
+ tensor<int32, [4]> input_125_pad_0 = const()[name = tensor<string, []>("input_125_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
325
+ tensor<int32, [2]> input_125_strides_0 = const()[name = tensor<string, []>("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])];
326
+ tensor<int32, [2]> input_125_dilations_0 = const()[name = tensor<string, []>("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])];
327
+ tensor<int32, []> input_125_groups_0 = const()[name = tensor<string, []>("input_125_groups_0"), val = tensor<int32, []>(1)];
328
+ tensor<fp16, [256, 256, 3, 3]> layer4_2_conv1_weight_to_fp16 = const()[name = tensor<string, []>("layer4_2_conv1_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8282304)))];
329
+ tensor<fp16, [256]> layer4_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("layer4_2_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9462016)))];
330
+ tensor<fp16, [1, 256, ?, 10]> input_125_cast_fp16 = conv(bias = layer4_2_conv1_bias_to_fp16, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = layer4_2_conv1_weight_to_fp16, x = input_123_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")];
331
+ tensor<fp16, [1, 256, ?, 10]> input_127_cast_fp16 = relu(x = input_125_cast_fp16)[name = tensor<string, []>("input_127_cast_fp16")];
332
+ tensor<string, []> out_pad_type_0 = const()[name = tensor<string, []>("out_pad_type_0"), val = tensor<string, []>("custom")];
333
+ tensor<int32, [4]> out_pad_0 = const()[name = tensor<string, []>("out_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
334
+ tensor<int32, [2]> out_strides_0 = const()[name = tensor<string, []>("out_strides_0"), val = tensor<int32, [2]>([1, 1])];
335
+ tensor<int32, [2]> out_dilations_0 = const()[name = tensor<string, []>("out_dilations_0"), val = tensor<int32, [2]>([1, 1])];
336
+ tensor<int32, []> out_groups_0 = const()[name = tensor<string, []>("out_groups_0"), val = tensor<int32, []>(1)];
337
+ tensor<fp16, [256, 256, 3, 3]> layer4_2_conv2_weight_to_fp16 = const()[name = tensor<string, []>("layer4_2_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9462592)))];
338
+ tensor<fp16, [256]> layer4_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("layer4_2_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10642304)))];
339
+ tensor<fp16, [1, 256, ?, 10]> out_cast_fp16 = conv(bias = layer4_2_conv2_bias_to_fp16, dilations = out_dilations_0, groups = out_groups_0, pad = out_pad_0, pad_type = out_pad_type_0, strides = out_strides_0, weight = layer4_2_conv2_weight_to_fp16, x = input_127_cast_fp16)[name = tensor<string, []>("out_cast_fp16")];
340
+ tensor<fp16, [1, 256, ?, 10]> input_129_cast_fp16 = add(x = out_cast_fp16, y = input_123_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
341
+ tensor<fp16, [1, 256, ?, 10]> x_1_cast_fp16 = relu(x = input_129_cast_fp16)[name = tensor<string, []>("x_1_cast_fp16")];
342
+ tensor<int32, [3]> concat_0x = const()[name = tensor<string, []>("concat_0x"), val = tensor<int32, [3]>([1, 2560, -1])];
343
+ tensor<fp16, [1, 2560, ?]> x_cast_fp16 = reshape(shape = concat_0x, x = x_1_cast_fp16)[name = tensor<string, []>("x_cast_fp16")];
344
+ tensor<int32, [1]> mean_axes_0 = const()[name = tensor<string, []>("mean_axes_0"), val = tensor<int32, [1]>([2])];
345
+ tensor<bool, []> mean_keep_dims_0 = const()[name = tensor<string, []>("mean_keep_dims_0"), val = tensor<bool, []>(false)];
346
+ tensor<fp16, [1, 2560]> mean_cast_fp16 = reduce_mean(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = x_cast_fp16)[name = tensor<string, []>("mean_cast_fp16")];
347
+ tensor<int32, [1]> reduce_mean_0_axes_0 = const()[name = tensor<string, []>("reduce_mean_0_axes_0"), val = tensor<int32, [1]>([2])];
348
+ tensor<bool, []> reduce_mean_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_0_keep_dims_0"), val = tensor<bool, []>(true)];
349
+ tensor<fp16, [1, 2560, 1]> reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = x_cast_fp16)[name = tensor<string, []>("reduce_mean_0_cast_fp16")];
350
+ tensor<fp16, [1, 2560, ?]> sub_0_cast_fp16 = sub(x = x_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor<string, []>("sub_0_cast_fp16")];
351
+ tensor<fp16, [1, 2560, ?]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor<string, []>("square_0_cast_fp16")];
352
+ tensor<int32, [1]> reduce_mean_1_axes_0 = const()[name = tensor<string, []>("reduce_mean_1_axes_0"), val = tensor<int32, [1]>([2])];
353
+ tensor<bool, []> reduce_mean_1_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_1_keep_dims_0"), val = tensor<bool, []>(false)];
354
+ tensor<fp16, [1, 2560]> reduce_mean_1_cast_fp16 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0_cast_fp16)[name = tensor<string, []>("reduce_mean_1_cast_fp16")];
355
+ tensor<int32, [3]> shape_0_cast_fp16 = shape(x = x_cast_fp16)[name = tensor<string, []>("shape_0_cast_fp16")];
356
+ tensor<int32, [1]> slice_by_index_0_begin_0 = const()[name = tensor<string, []>("slice_by_index_0_begin_0"), val = tensor<int32, [1]>([2])];
357
+ tensor<int32, [1]> slice_by_index_0_end_0 = const()[name = tensor<string, []>("slice_by_index_0_end_0"), val = tensor<int32, [1]>([0])];
358
+ tensor<bool, [1]> slice_by_index_0_squeeze_mask_0 = const()[name = tensor<string, []>("slice_by_index_0_squeeze_mask_0"), val = tensor<bool, [1]>([true])];
359
+ tensor<int32, []> slice_by_index_0 = slice_by_index(begin = slice_by_index_0_begin_0, end = slice_by_index_0_end_0, squeeze_mask = slice_by_index_0_squeeze_mask_0, x = shape_0_cast_fp16)[name = tensor<string, []>("slice_by_index_0")];
360
+ tensor<int32, []> concat_1_axis_0 = const()[name = tensor<string, []>("concat_1_axis_0"), val = tensor<int32, []>(0)];
361
+ tensor<bool, []> concat_1_interleave_0 = const()[name = tensor<string, []>("concat_1_interleave_0"), val = tensor<bool, []>(false)];
362
+ tensor<int32, [1]> concat_1 = concat(axis = concat_1_axis_0, interleave = concat_1_interleave_0, values = slice_by_index_0)[name = tensor<string, []>("concat_1")];
363
+ tensor<bool, []> reduce_prod_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_prod_0_keep_dims_0"), val = tensor<bool, []>(false)];
364
+ tensor<int32, []> reduce_prod_0 = reduce_prod(keep_dims = reduce_prod_0_keep_dims_0, x = concat_1)[name = tensor<string, []>("reduce_prod_0")];
365
+ tensor<string, []> cast_2_to_fp16_dtype_0 = const()[name = tensor<string, []>("cast_2_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
366
+ tensor<fp16, []> sub_1_y_0_to_fp16 = const()[name = tensor<string, []>("sub_1_y_0_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
367
+ tensor<fp16, []> reduce_prod_0_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = reduce_prod_0)[name = tensor<string, []>("cast_6")];
368
+ tensor<fp16, []> sub_1_cast_fp16 = sub(x = reduce_prod_0_to_fp16, y = sub_1_y_0_to_fp16)[name = tensor<string, []>("sub_1_cast_fp16")];
369
+ tensor<fp16, []> real_div_0_cast_fp16 = real_div(x = reduce_prod_0_to_fp16, y = sub_1_cast_fp16)[name = tensor<string, []>("real_div_0_cast_fp16")];
370
+ tensor<fp16, [1, 2560]> mul_0_cast_fp16 = mul(x = reduce_mean_1_cast_fp16, y = real_div_0_cast_fp16)[name = tensor<string, []>("mul_0_cast_fp16")];
371
+ tensor<fp16, [1, 2560]> sqrt_0_cast_fp16 = sqrt(x = mul_0_cast_fp16)[name = tensor<string, []>("sqrt_0_cast_fp16")];
372
+ tensor<int32, []> var_412 = const()[name = tensor<string, []>("op_412"), val = tensor<int32, []>(-1)];
373
+ tensor<bool, []> input_131_interleave_0 = const()[name = tensor<string, []>("input_131_interleave_0"), val = tensor<bool, []>(false)];
374
+ tensor<fp16, [1, 5120]> input_131_cast_fp16 = concat(axis = var_412, interleave = input_131_interleave_0, values = (mean_cast_fp16, sqrt_0_cast_fp16))[name = tensor<string, []>("input_131_cast_fp16")];
375
+ tensor<fp16, [256, 5120]> embedding_weight_to_fp16 = const()[name = tensor<string, []>("embedding_weight_to_fp16"), val = tensor<fp16, [256, 5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10642880)))];
376
+ tensor<fp16, [256]> embedding_bias_to_fp16 = const()[name = tensor<string, []>("embedding_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13264384)))];
377
+ tensor<fp16, [1, 256]> linear_0_cast_fp16 = linear(bias = embedding_bias_to_fp16, weight = embedding_weight_to_fp16, x = input_131_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
378
+ tensor<int32, [1]> var_419 = const()[name = tensor<string, []>("op_419"), val = tensor<int32, [1]>([-1])];
379
+ tensor<bool, []> var_420 = const()[name = tensor<string, []>("op_420"), val = tensor<bool, []>(true)];
380
+ tensor<fp16, [1, 1]> var_422_cast_fp16 = reduce_l2_norm(axes = var_419, keep_dims = var_420, x = linear_0_cast_fp16)[name = tensor<string, []>("op_422_cast_fp16")];
381
+ tensor<fp16, []> var_423_to_fp16 = const()[name = tensor<string, []>("op_423_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
382
+ tensor<fp16, [1, 1]> var_424_cast_fp16 = maximum(x = var_422_cast_fp16, y = var_423_to_fp16)[name = tensor<string, []>("op_424_cast_fp16")];
383
+ tensor<int32, [2]> denom_reps_0 = const()[name = tensor<string, []>("denom_reps_0"), val = tensor<int32, [2]>([1, 256])];
384
+ tensor<fp16, [1, 256]> denom_cast_fp16 = tile(reps = denom_reps_0, x = var_424_cast_fp16)[name = tensor<string, []>("denom_cast_fp16")];
385
+ tensor<fp16, [1, 256]> embedding = real_div(x = linear_0_cast_fp16, y = denom_cast_fp16)[name = tensor<string, []>("op_426_cast_fp16")];
386
+ } -> (embedding);
387
+ }
wespeaker.mlmodelc/weights/weight.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:6dba18a57a81b1e872802ca4def29541bb7900ccff430d9b2040092cadd7d688
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+ size 13264960