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FluidInference
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speaker-diarization-coreml

Voice Activity Detection
Core ML
speech
audio
voice
speaker-diarization
speaker-change-detection
speaker-segmentation
Model card Files
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  1. Embedding.mlmodelc/analytics/coremldata.bin +3 -0
  2. Embedding.mlmodelc/coremldata.bin +3 -0
  3. Embedding.mlmodelc/metadata.json +83 -0
  4. Embedding.mlmodelc/model.mil +437 -0
  5. Embedding.mlmodelc/weights/weight.bin +3 -0
  6. Segmentation.mlmodelc/analytics/coremldata.bin +3 -0
  7. Segmentation.mlmodelc/coremldata.bin +3 -0
  8. Segmentation.mlmodelc/metadata.json +74 -0
  9. Segmentation.mlmodelc/model.mil +135 -0
  10. Segmentation.mlmodelc/weights/weight.bin +3 -0
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+ [
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+ {
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+ "shortDescription" : "pyannote community-1 speaker embedding (5 s WeSpeaker ResNet34)",
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+ "metadataOutputVersion" : "3.0",
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+ "outputSchema" : [
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+ {
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+ "hasShapeFlexibility" : "0",
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+ "formattedType" : "MultiArray (Float32 1 × 256)",
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+ "shortDescription" : "",
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+ "shape" : "[1, 256]",
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+ "name" : "embedding",
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+ "type" : "MultiArray"
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+ }
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+ ],
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+ "version" : "pyannote-speaker-diarization-community-1",
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+ "modelParameters" : [
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+
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+ ],
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+ "author" : "Fluid Inference",
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+ "specificationVersion" : 8,
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+ "storagePrecision" : "Float32",
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+ "license" : "CC-BY-4.0",
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+ "mlProgramOperationTypeHistogram" : {
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+ "Ios17.mul" : 5,
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+ "Ios17.sqrt" : 1,
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+ "Ios17.linear" : 4,
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+ "Ios17.transpose" : 2,
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+ "Ios17.sub" : 4,
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+ "Ios17.conv" : 37,
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+ "Ios17.log" : 1,
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+ "Ios17.concat" : 1,
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+ "Ios17.add" : 17,
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+ "Ios17.sliceByIndex" : 1,
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+ "Ios16.reduceMean" : 3,
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+ "Ios17.clip" : 2,
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+ "Ios16.relu" : 33,
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+ "Ios17.pow" : 2,
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+ "Ios17.expandDims" : 5,
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+ "Ios16.reduceSum" : 1,
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+ "Ios17.squeeze" : 3,
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+ "Ios17.reshape" : 1,
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+ "Pad" : 2
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+ },
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+ "computePrecision" : "Mixed (Float32, Int32)",
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+ "stateSchema" : [
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+ ],
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+ "isUpdatable" : "0",
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+ "availability" : {
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+ "macOS" : "14.0",
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+ "tvOS" : "17.0",
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+ "visionOS" : "1.0",
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+ "watchOS" : "10.0",
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+ "iOS" : "17.0",
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+ "macCatalyst" : "17.0"
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+ },
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+ "modelType" : {
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+ "name" : "MLModelType_mlProgram"
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+ },
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+ "inputSchema" : [
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+ {
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+ "hasShapeFlexibility" : "0",
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+ "formattedType" : "MultiArray (Float32 1 × 1 × 80000)",
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+ "shortDescription" : "",
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+ "shape" : "[1, 1, 80000]",
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+ "name" : "audio",
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+ "type" : "MultiArray"
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+ "userDefinedMetadata" : {
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+ "com.github.apple.coremltools.conversion_date" : "2025-09-30",
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+ "com.github.apple.coremltools.version" : "9.0b1",
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+ "com.github.apple.coremltools.source_dialect" : "TorchScript"
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+ "generatedClassName" : "embedding_community_1",
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+ "method" : "predict"
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+ }
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Embedding.mlmodelc/model.mil ADDED
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+ program(1.0)
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+ [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
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+ {
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+ func main<ios17>(tensor<fp32, [1, 1, 80000]> audio) {
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+ tensor<fp32, [1, 400]> _fbank_window = const()[name = tensor<string, []>("_fbank_window"), val = tensor<fp32, [1, 400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
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+ tensor<fp32, []> _fbank_eps = const()[name = tensor<string, []>("_fbank_eps"), val = tensor<fp32, []>(0x1.b7cdfep-34)];
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+ tensor<fp32, [400, 1, 400]> _fbank_frame_kernel = const()[name = tensor<string, []>("_fbank_frame_kernel"), val = tensor<fp32, [400, 1, 400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1728)))];
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+ tensor<fp32, [256]> resnet_seg_1_bias = const()[name = tensor<string, []>("resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(641792)))];
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+ tensor<fp32, [256, 5120]> resnet_seg_1_weight = const()[name = tensor<string, []>("resnet_seg_1_weight"), val = tensor<fp32, [256, 5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642880)))];
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+ tensor<fp32, []> var_4_promoted = const()[name = tensor<string, []>("op_4_promoted"), val = tensor<fp32, []>(0x1p+15)];
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+ tensor<fp32, [1, 1, 80000]> waveforms_3 = mul(x = audio, y = var_4_promoted)[name = tensor<string, []>("waveforms_3")];
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+ tensor<string, []> frames_1_pad_type_0 = const()[name = tensor<string, []>("frames_1_pad_type_0"), val = tensor<string, []>("valid")];
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+ tensor<int32, [1]> frames_1_strides_0 = const()[name = tensor<string, []>("frames_1_strides_0"), val = tensor<int32, [1]>([160])];
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+ tensor<int32, [2]> frames_1_pad_0 = const()[name = tensor<string, []>("frames_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
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+ tensor<int32, [1]> frames_1_dilations_0 = const()[name = tensor<string, []>("frames_1_dilations_0"), val = tensor<int32, [1]>([1])];
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+ tensor<int32, []> frames_1_groups_0 = const()[name = tensor<string, []>("frames_1_groups_0"), val = tensor<int32, []>(1)];
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+ tensor<fp32, [1, 400, 498]> frames_1 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = _fbank_frame_kernel, x = waveforms_3)[name = tensor<string, []>("frames_1")];
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+ tensor<int32, [1]> var_44_axes_0 = const()[name = tensor<string, []>("op_44_axes_0"), val = tensor<int32, [1]>([0])];
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+ tensor<fp32, [400, 498]> var_44 = squeeze(axes = var_44_axes_0, x = frames_1)[name = tensor<string, []>("op_44")];
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+ tensor<int32, [2]> frames_3_perm_0 = const()[name = tensor<string, []>("frames_3_perm_0"), val = tensor<int32, [2]>([1, 0])];
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+ tensor<int32, [1]> var_47_axes_0 = const()[name = tensor<string, []>("op_47_axes_0"), val = tensor<int32, [1]>([1])];
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+ tensor<bool, []> var_47_keep_dims_0 = const()[name = tensor<string, []>("op_47_keep_dims_0"), val = tensor<bool, []>(true)];
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+ tensor<fp32, [498, 400]> frames_3 = transpose(perm = frames_3_perm_0, x = var_44)[name = tensor<string, []>("transpose_4")];
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+ tensor<fp32, [498, 1]> var_47 = reduce_mean(axes = var_47_axes_0, keep_dims = var_47_keep_dims_0, x = frames_3)[name = tensor<string, []>("op_47")];
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+ tensor<fp32, [498, 400]> frames_5 = sub(x = frames_3, y = var_47)[name = tensor<string, []>("frames_5")];
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+ tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([1])];
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+ tensor<fp32, [498, 1, 400]> input_1 = expand_dims(axes = input_1_axes_0, x = frames_5)[name = tensor<string, []>("input_1")];
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+ tensor<fp32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<fp32, []>(0x0p+0)];
29
+ tensor<int32, [6]> var_51_pad_0 = const()[name = tensor<string, []>("op_51_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
30
+ tensor<string, []> var_51_mode_0 = const()[name = tensor<string, []>("op_51_mode_0"), val = tensor<string, []>("replicate")];
31
+ tensor<fp32, [498, 1, 401]> var_51 = pad(constant_val = const_0, mode = var_51_mode_0, pad = var_51_pad_0, x = input_1)[name = tensor<string, []>("op_51")];
32
+ tensor<int32, [1]> padded_axes_0 = const()[name = tensor<string, []>("padded_axes_0"), val = tensor<int32, [1]>([1])];
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+ tensor<fp32, [498, 401]> padded = squeeze(axes = padded_axes_0, x = var_51)[name = tensor<string, []>("padded")];
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+ tensor<int32, [2]> var_54_begin_0 = const()[name = tensor<string, []>("op_54_begin_0"), val = tensor<int32, [2]>([0, 0])];
35
+ tensor<int32, [2]> var_54_end_0 = const()[name = tensor<string, []>("op_54_end_0"), val = tensor<int32, [2]>([498, 400])];
36
+ tensor<bool, [2]> var_54_end_mask_0 = const()[name = tensor<string, []>("op_54_end_mask_0"), val = tensor<bool, [2]>([true, false])];
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+ tensor<fp32, [498, 400]> var_54 = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = padded)[name = tensor<string, []>("op_54")];
38
+ tensor<fp32, []> var_55 = const()[name = tensor<string, []>("op_55"), val = tensor<fp32, []>(0x1.f0a3d8p-1)];
39
+ tensor<fp32, [498, 400]> var_56 = mul(x = var_54, y = var_55)[name = tensor<string, []>("op_56")];
40
+ tensor<fp32, [498, 400]> frames_7 = sub(x = frames_5, y = var_56)[name = tensor<string, []>("frames_7")];
41
+ tensor<fp32, [498, 400]> frames_9 = mul(x = frames_7, y = _fbank_window)[name = tensor<string, []>("frames_9")];
42
+ tensor<int32, [1]> input_3_axes_0 = const()[name = tensor<string, []>("input_3_axes_0"), val = tensor<int32, [1]>([1])];
43
+ tensor<fp32, [498, 1, 400]> input_3 = expand_dims(axes = input_3_axes_0, x = frames_9)[name = tensor<string, []>("input_3")];
44
+ tensor<fp32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<fp32, []>(0x0p+0)];
45
+ tensor<int32, [6]> var_61_pad_0 = const()[name = tensor<string, []>("op_61_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])];
46
+ tensor<string, []> var_61_mode_0 = const()[name = tensor<string, []>("op_61_mode_0"), val = tensor<string, []>("constant")];
47
+ tensor<fp32, [498, 1, 512]> var_61 = pad(constant_val = const_1, mode = var_61_mode_0, pad = var_61_pad_0, x = input_3)[name = tensor<string, []>("op_61")];
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+ tensor<int32, [1]> frames_11_axes_0 = const()[name = tensor<string, []>("frames_11_axes_0"), val = tensor<int32, [1]>([1])];
49
+ tensor<fp32, [498, 512]> frames_11 = squeeze(axes = frames_11_axes_0, x = var_61)[name = tensor<string, []>("frames_11")];
50
+ tensor<fp32, [257, 512]> transpose_0 = const()[name = tensor<string, []>("transpose_0"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5885824)))];
51
+ tensor<fp32, [257]> real_bias_0 = const()[name = tensor<string, []>("real_bias_0"), val = tensor<fp32, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6412224)))];
52
+ tensor<fp32, [498, 257]> real = linear(bias = real_bias_0, weight = transpose_0, x = frames_11)[name = tensor<string, []>("real")];
53
+ tensor<fp32, [257, 512]> transpose_1 = const()[name = tensor<string, []>("transpose_1"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6413376)))];
54
+ tensor<fp32, [498, 257]> imag = linear(bias = real_bias_0, weight = transpose_1, x = frames_11)[name = tensor<string, []>("imag")];
55
+ tensor<fp32, []> var_17_promoted = const()[name = tensor<string, []>("op_17_promoted"), val = tensor<fp32, []>(0x1p+1)];
56
+ tensor<fp32, [498, 257]> var_65 = pow(x = real, y = var_17_promoted)[name = tensor<string, []>("op_65")];
57
+ tensor<fp32, []> var_17_promoted_1 = const()[name = tensor<string, []>("op_17_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
58
+ tensor<fp32, [498, 257]> var_66 = pow(x = imag, y = var_17_promoted_1)[name = tensor<string, []>("op_66")];
59
+ tensor<fp32, [498, 257]> power = add(x = var_65, y = var_66)[name = tensor<string, []>("power")];
60
+ tensor<fp32, [80, 257]> transpose_2 = const()[name = tensor<string, []>("transpose_2"), val = tensor<fp32, [80, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6939776)))];
61
+ tensor<fp32, [80]> mel_bias_0 = const()[name = tensor<string, []>("mel_bias_0"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7022080)))];
62
+ tensor<fp32, [498, 80]> mel = linear(bias = mel_bias_0, weight = transpose_2, x = power)[name = tensor<string, []>("mel")];
63
+ tensor<fp32, []> const_2 = const()[name = tensor<string, []>("const_2"), val = tensor<fp32, []>(0x1.fffffep+127)];
64
+ tensor<fp32, [498, 80]> clip_0 = clip(alpha = _fbank_eps, beta = const_2, x = mel)[name = tensor<string, []>("clip_0")];
65
+ tensor<fp32, []> var_70_epsilon_0 = const()[name = tensor<string, []>("op_70_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
66
+ tensor<fp32, [498, 80]> var_70 = log(epsilon = var_70_epsilon_0, x = clip_0)[name = tensor<string, []>("op_70")];
67
+ tensor<int32, [1]> var_73_axes_0 = const()[name = tensor<string, []>("op_73_axes_0"), val = tensor<int32, [1]>([0])];
68
+ tensor<fp32, [1, 498, 80]> var_73 = expand_dims(axes = var_73_axes_0, x = var_70)[name = tensor<string, []>("op_73")];
69
+ tensor<int32, [1]> centered_1_axes_0 = const()[name = tensor<string, []>("centered_1_axes_0"), val = tensor<int32, [1]>([1])];
70
+ tensor<bool, []> centered_1_keep_dims_0 = const()[name = tensor<string, []>("centered_1_keep_dims_0"), val = tensor<bool, []>(true)];
71
+ tensor<fp32, [1, 1, 80]> centered_1 = reduce_mean(axes = centered_1_axes_0, keep_dims = centered_1_keep_dims_0, x = var_73)[name = tensor<string, []>("centered_1")];
72
+ tensor<fp32, [1, 498, 80]> fbank_1 = sub(x = var_73, y = centered_1)[name = tensor<string, []>("fbank_1")];
73
+ tensor<int32, []> var_90 = const()[name = tensor<string, []>("op_90"), val = tensor<int32, []>(-1)];
74
+ tensor<fp32, []> var_93 = const()[name = tensor<string, []>("op_93"), val = tensor<fp32, []>(0x1.197998p-40)];
75
+ tensor<int32, [3]> var_109 = const()[name = tensor<string, []>("op_109"), val = tensor<int32, [3]>([0, 2, 1])];
76
+ tensor<int32, [1]> input_5_axes_0 = const()[name = tensor<string, []>("input_5_axes_0"), val = tensor<int32, [1]>([1])];
77
+ tensor<fp32, [1, 80, 498]> fbank = transpose(perm = var_109, x = fbank_1)[name = tensor<string, []>("transpose_3")];
78
+ tensor<fp32, [1, 1, 80, 498]> input_5 = expand_dims(axes = input_5_axes_0, x = fbank)[name = tensor<string, []>("input_5")];
79
+ tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("custom")];
80
+ tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
81
+ tensor<int32, [2]> input_7_strides_0 = const()[name = tensor<string, []>("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
82
+ tensor<int32, [2]> input_7_dilations_0 = const()[name = tensor<string, []>("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
83
+ tensor<int32, []> input_7_groups_0 = const()[name = tensor<string, []>("input_7_groups_0"), val = tensor<int32, []>(1)];
84
+ tensor<fp32, [32, 1, 3, 3]> const_9 = const()[name = tensor<string, []>("const_9"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7022464)))];
85
+ tensor<fp32, [32]> const_10 = const()[name = tensor<string, []>("const_10"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7023680)))];
86
+ tensor<fp32, [1, 32, 80, 498]> input_9 = conv(bias = const_10, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = const_9, x = input_5)[name = tensor<string, []>("input_9")];
87
+ tensor<fp32, [1, 32, 80, 498]> input_11 = relu(x = input_9)[name = tensor<string, []>("input_11")];
88
+ tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")];
89
+ tensor<int32, [4]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
90
+ tensor<int32, [2]> input_13_strides_0 = const()[name = tensor<string, []>("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
91
+ tensor<int32, [2]> input_13_dilations_0 = const()[name = tensor<string, []>("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
92
+ tensor<int32, []> input_13_groups_0 = const()[name = tensor<string, []>("input_13_groups_0"), val = tensor<int32, []>(1)];
93
+ tensor<fp32, [32, 32, 3, 3]> const_11 = const()[name = tensor<string, []>("const_11"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7023872)))];
94
+ tensor<fp32, [32]> const_12 = const()[name = tensor<string, []>("const_12"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7060800)))];
95
+ tensor<fp32, [1, 32, 80, 498]> input_15 = conv(bias = const_12, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = const_11, x = input_11)[name = tensor<string, []>("input_15")];
96
+ tensor<fp32, [1, 32, 80, 498]> input_17 = relu(x = input_15)[name = tensor<string, []>("input_17")];
97
+ tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")];
98
+ tensor<int32, [4]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
99
+ tensor<int32, [2]> input_19_strides_0 = const()[name = tensor<string, []>("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
100
+ tensor<int32, [2]> input_19_dilations_0 = const()[name = tensor<string, []>("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
101
+ tensor<int32, []> input_19_groups_0 = const()[name = tensor<string, []>("input_19_groups_0"), val = tensor<int32, []>(1)];
102
+ tensor<fp32, [32, 32, 3, 3]> const_13 = const()[name = tensor<string, []>("const_13"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7060992)))];
103
+ tensor<fp32, [32]> const_14 = const()[name = tensor<string, []>("const_14"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7097920)))];
104
+ tensor<fp32, [1, 32, 80, 498]> out_1 = conv(bias = const_14, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = const_13, x = input_17)[name = tensor<string, []>("out_1")];
105
+ tensor<fp32, [1, 32, 80, 498]> input_21 = add(x = out_1, y = input_11)[name = tensor<string, []>("input_21")];
106
+ tensor<fp32, [1, 32, 80, 498]> input_23 = relu(x = input_21)[name = tensor<string, []>("input_23")];
107
+ tensor<string, []> input_25_pad_type_0 = const()[name = tensor<string, []>("input_25_pad_type_0"), val = tensor<string, []>("custom")];
108
+ tensor<int32, [4]> input_25_pad_0 = const()[name = tensor<string, []>("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
109
+ tensor<int32, [2]> input_25_strides_0 = const()[name = tensor<string, []>("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
110
+ tensor<int32, [2]> input_25_dilations_0 = const()[name = tensor<string, []>("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
111
+ tensor<int32, []> input_25_groups_0 = const()[name = tensor<string, []>("input_25_groups_0"), val = tensor<int32, []>(1)];
112
+ tensor<fp32, [32, 32, 3, 3]> const_15 = const()[name = tensor<string, []>("const_15"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7098112)))];
113
+ tensor<fp32, [32]> const_16 = const()[name = tensor<string, []>("const_16"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7135040)))];
114
+ tensor<fp32, [1, 32, 80, 498]> input_27 = conv(bias = const_16, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = const_15, x = input_23)[name = tensor<string, []>("input_27")];
115
+ tensor<fp32, [1, 32, 80, 498]> input_29 = relu(x = input_27)[name = tensor<string, []>("input_29")];
116
+ tensor<string, []> input_31_pad_type_0 = const()[name = tensor<string, []>("input_31_pad_type_0"), val = tensor<string, []>("custom")];
117
+ tensor<int32, [4]> input_31_pad_0 = const()[name = tensor<string, []>("input_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
118
+ tensor<int32, [2]> input_31_strides_0 = const()[name = tensor<string, []>("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
119
+ tensor<int32, [2]> input_31_dilations_0 = const()[name = tensor<string, []>("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
120
+ tensor<int32, []> input_31_groups_0 = const()[name = tensor<string, []>("input_31_groups_0"), val = tensor<int32, []>(1)];
121
+ tensor<fp32, [32, 32, 3, 3]> const_17 = const()[name = tensor<string, []>("const_17"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7135232)))];
122
+ tensor<fp32, [32]> const_18 = const()[name = tensor<string, []>("const_18"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7172160)))];
123
+ tensor<fp32, [1, 32, 80, 498]> out_3 = conv(bias = const_18, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = const_17, x = input_29)[name = tensor<string, []>("out_3")];
124
+ tensor<fp32, [1, 32, 80, 498]> input_33 = add(x = out_3, y = input_23)[name = tensor<string, []>("input_33")];
125
+ tensor<fp32, [1, 32, 80, 498]> input_35 = relu(x = input_33)[name = tensor<string, []>("input_35")];
126
+ tensor<string, []> input_37_pad_type_0 = const()[name = tensor<string, []>("input_37_pad_type_0"), val = tensor<string, []>("custom")];
127
+ tensor<int32, [4]> input_37_pad_0 = const()[name = tensor<string, []>("input_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
128
+ tensor<int32, [2]> input_37_strides_0 = const()[name = tensor<string, []>("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
129
+ tensor<int32, [2]> input_37_dilations_0 = const()[name = tensor<string, []>("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
130
+ tensor<int32, []> input_37_groups_0 = const()[name = tensor<string, []>("input_37_groups_0"), val = tensor<int32, []>(1)];
131
+ tensor<fp32, [32, 32, 3, 3]> const_19 = const()[name = tensor<string, []>("const_19"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7172352)))];
132
+ tensor<fp32, [32]> const_20 = const()[name = tensor<string, []>("const_20"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7209280)))];
133
+ tensor<fp32, [1, 32, 80, 498]> input_39 = conv(bias = const_20, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = const_19, x = input_35)[name = tensor<string, []>("input_39")];
134
+ tensor<fp32, [1, 32, 80, 498]> input_41 = relu(x = input_39)[name = tensor<string, []>("input_41")];
135
+ tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("custom")];
136
+ tensor<int32, [4]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
137
+ tensor<int32, [2]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
138
+ tensor<int32, [2]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
139
+ tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)];
140
+ tensor<fp32, [32, 32, 3, 3]> const_21 = const()[name = tensor<string, []>("const_21"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7209472)))];
141
+ tensor<fp32, [32]> const_22 = const()[name = tensor<string, []>("const_22"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7246400)))];
142
+ tensor<fp32, [1, 32, 80, 498]> out_5 = conv(bias = const_22, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = const_21, x = input_41)[name = tensor<string, []>("out_5")];
143
+ tensor<fp32, [1, 32, 80, 498]> input_45 = add(x = out_5, y = input_35)[name = tensor<string, []>("input_45")];
144
+ tensor<fp32, [1, 32, 80, 498]> input_47 = relu(x = input_45)[name = tensor<string, []>("input_47")];
145
+ tensor<string, []> input_49_pad_type_0 = const()[name = tensor<string, []>("input_49_pad_type_0"), val = tensor<string, []>("custom")];
146
+ tensor<int32, [4]> input_49_pad_0 = const()[name = tensor<string, []>("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
147
+ tensor<int32, [2]> input_49_strides_0 = const()[name = tensor<string, []>("input_49_strides_0"), val = tensor<int32, [2]>([2, 2])];
148
+ tensor<int32, [2]> input_49_dilations_0 = const()[name = tensor<string, []>("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
149
+ tensor<int32, []> input_49_groups_0 = const()[name = tensor<string, []>("input_49_groups_0"), val = tensor<int32, []>(1)];
150
+ tensor<fp32, [64, 32, 3, 3]> const_23 = const()[name = tensor<string, []>("const_23"), val = tensor<fp32, [64, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7246592)))];
151
+ tensor<fp32, [64]> const_24 = const()[name = tensor<string, []>("const_24"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7320384)))];
152
+ tensor<fp32, [1, 64, 40, 249]> input_51 = conv(bias = const_24, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_23, x = input_47)[name = tensor<string, []>("input_51")];
153
+ tensor<fp32, [1, 64, 40, 249]> input_53 = relu(x = input_51)[name = tensor<string, []>("input_53")];
154
+ tensor<string, []> input_55_pad_type_0 = const()[name = tensor<string, []>("input_55_pad_type_0"), val = tensor<string, []>("custom")];
155
+ tensor<int32, [4]> input_55_pad_0 = const()[name = tensor<string, []>("input_55_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
156
+ tensor<int32, [2]> input_55_strides_0 = const()[name = tensor<string, []>("input_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
157
+ tensor<int32, [2]> input_55_dilations_0 = const()[name = tensor<string, []>("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
158
+ tensor<int32, []> input_55_groups_0 = const()[name = tensor<string, []>("input_55_groups_0"), val = tensor<int32, []>(1)];
159
+ tensor<fp32, [64, 64, 3, 3]> const_25 = const()[name = tensor<string, []>("const_25"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7320704)))];
160
+ tensor<fp32, [64]> const_26 = const()[name = tensor<string, []>("const_26"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7468224)))];
161
+ tensor<fp32, [1, 64, 40, 249]> out_7 = conv(bias = const_26, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = const_25, x = input_53)[name = tensor<string, []>("out_7")];
162
+ tensor<string, []> input_57_pad_type_0 = const()[name = tensor<string, []>("input_57_pad_type_0"), val = tensor<string, []>("valid")];
163
+ tensor<int32, [2]> input_57_strides_0 = const()[name = tensor<string, []>("input_57_strides_0"), val = tensor<int32, [2]>([2, 2])];
164
+ tensor<int32, [4]> input_57_pad_0 = const()[name = tensor<string, []>("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
165
+ tensor<int32, [2]> input_57_dilations_0 = const()[name = tensor<string, []>("input_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
166
+ tensor<int32, []> input_57_groups_0 = const()[name = tensor<string, []>("input_57_groups_0"), val = tensor<int32, []>(1)];
167
+ tensor<fp32, [64, 32, 1, 1]> const_27 = const()[name = tensor<string, []>("const_27"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7468544)))];
168
+ tensor<fp32, [64]> const_28 = const()[name = tensor<string, []>("const_28"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7476800)))];
169
+ tensor<fp32, [1, 64, 40, 249]> var_258 = conv(bias = const_28, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_27, x = input_47)[name = tensor<string, []>("op_258")];
170
+ tensor<fp32, [1, 64, 40, 249]> input_59 = add(x = out_7, y = var_258)[name = tensor<string, []>("input_59")];
171
+ tensor<fp32, [1, 64, 40, 249]> input_61 = relu(x = input_59)[name = tensor<string, []>("input_61")];
172
+ tensor<string, []> input_63_pad_type_0 = const()[name = tensor<string, []>("input_63_pad_type_0"), val = tensor<string, []>("custom")];
173
+ tensor<int32, [4]> input_63_pad_0 = const()[name = tensor<string, []>("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
174
+ tensor<int32, [2]> input_63_strides_0 = const()[name = tensor<string, []>("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
175
+ tensor<int32, [2]> input_63_dilations_0 = const()[name = tensor<string, []>("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
176
+ tensor<int32, []> input_63_groups_0 = const()[name = tensor<string, []>("input_63_groups_0"), val = tensor<int32, []>(1)];
177
+ tensor<fp32, [64, 64, 3, 3]> const_29 = const()[name = tensor<string, []>("const_29"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7477120)))];
178
+ tensor<fp32, [64]> const_30 = const()[name = tensor<string, []>("const_30"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7624640)))];
179
+ tensor<fp32, [1, 64, 40, 249]> input_65 = conv(bias = const_30, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = const_29, x = input_61)[name = tensor<string, []>("input_65")];
180
+ tensor<fp32, [1, 64, 40, 249]> input_67 = relu(x = input_65)[name = tensor<string, []>("input_67")];
181
+ tensor<string, []> input_69_pad_type_0 = const()[name = tensor<string, []>("input_69_pad_type_0"), val = tensor<string, []>("custom")];
182
+ tensor<int32, [4]> input_69_pad_0 = const()[name = tensor<string, []>("input_69_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
183
+ tensor<int32, [2]> input_69_strides_0 = const()[name = tensor<string, []>("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
184
+ tensor<int32, [2]> input_69_dilations_0 = const()[name = tensor<string, []>("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
185
+ tensor<int32, []> input_69_groups_0 = const()[name = tensor<string, []>("input_69_groups_0"), val = tensor<int32, []>(1)];
186
+ tensor<fp32, [64, 64, 3, 3]> const_31 = const()[name = tensor<string, []>("const_31"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7624960)))];
187
+ tensor<fp32, [64]> const_32 = const()[name = tensor<string, []>("const_32"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7772480)))];
188
+ tensor<fp32, [1, 64, 40, 249]> out_9 = conv(bias = const_32, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = const_31, x = input_67)[name = tensor<string, []>("out_9")];
189
+ tensor<fp32, [1, 64, 40, 249]> input_71 = add(x = out_9, y = input_61)[name = tensor<string, []>("input_71")];
190
+ tensor<fp32, [1, 64, 40, 249]> input_73 = relu(x = input_71)[name = tensor<string, []>("input_73")];
191
+ tensor<string, []> input_75_pad_type_0 = const()[name = tensor<string, []>("input_75_pad_type_0"), val = tensor<string, []>("custom")];
192
+ tensor<int32, [4]> input_75_pad_0 = const()[name = tensor<string, []>("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
193
+ tensor<int32, [2]> input_75_strides_0 = const()[name = tensor<string, []>("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])];
194
+ tensor<int32, [2]> input_75_dilations_0 = const()[name = tensor<string, []>("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
195
+ tensor<int32, []> input_75_groups_0 = const()[name = tensor<string, []>("input_75_groups_0"), val = tensor<int32, []>(1)];
196
+ tensor<fp32, [64, 64, 3, 3]> const_33 = const()[name = tensor<string, []>("const_33"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7772800)))];
197
+ tensor<fp32, [64]> const_34 = const()[name = tensor<string, []>("const_34"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7920320)))];
198
+ tensor<fp32, [1, 64, 40, 249]> input_77 = conv(bias = const_34, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = const_33, x = input_73)[name = tensor<string, []>("input_77")];
199
+ tensor<fp32, [1, 64, 40, 249]> input_79 = relu(x = input_77)[name = tensor<string, []>("input_79")];
200
+ tensor<string, []> input_81_pad_type_0 = const()[name = tensor<string, []>("input_81_pad_type_0"), val = tensor<string, []>("custom")];
201
+ tensor<int32, [4]> input_81_pad_0 = const()[name = tensor<string, []>("input_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
202
+ tensor<int32, [2]> input_81_strides_0 = const()[name = tensor<string, []>("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
203
+ tensor<int32, [2]> input_81_dilations_0 = const()[name = tensor<string, []>("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
204
+ tensor<int32, []> input_81_groups_0 = const()[name = tensor<string, []>("input_81_groups_0"), val = tensor<int32, []>(1)];
205
+ tensor<fp32, [64, 64, 3, 3]> const_35 = const()[name = tensor<string, []>("const_35"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7920640)))];
206
+ tensor<fp32, [64]> const_36 = const()[name = tensor<string, []>("const_36"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8068160)))];
207
+ tensor<fp32, [1, 64, 40, 249]> out_11 = conv(bias = const_36, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = const_35, x = input_79)[name = tensor<string, []>("out_11")];
208
+ tensor<fp32, [1, 64, 40, 249]> input_83 = add(x = out_11, y = input_73)[name = tensor<string, []>("input_83")];
209
+ tensor<fp32, [1, 64, 40, 249]> input_85 = relu(x = input_83)[name = tensor<string, []>("input_85")];
210
+ tensor<string, []> input_87_pad_type_0 = const()[name = tensor<string, []>("input_87_pad_type_0"), val = tensor<string, []>("custom")];
211
+ tensor<int32, [4]> input_87_pad_0 = const()[name = tensor<string, []>("input_87_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
212
+ tensor<int32, [2]> input_87_strides_0 = const()[name = tensor<string, []>("input_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
213
+ tensor<int32, [2]> input_87_dilations_0 = const()[name = tensor<string, []>("input_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
214
+ tensor<int32, []> input_87_groups_0 = const()[name = tensor<string, []>("input_87_groups_0"), val = tensor<int32, []>(1)];
215
+ tensor<fp32, [64, 64, 3, 3]> const_37 = const()[name = tensor<string, []>("const_37"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8068480)))];
216
+ tensor<fp32, [64]> const_38 = const()[name = tensor<string, []>("const_38"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8216000)))];
217
+ tensor<fp32, [1, 64, 40, 249]> input_89 = conv(bias = const_38, dilations = input_87_dilations_0, groups = input_87_groups_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = const_37, x = input_85)[name = tensor<string, []>("input_89")];
218
+ tensor<fp32, [1, 64, 40, 249]> input_91 = relu(x = input_89)[name = tensor<string, []>("input_91")];
219
+ tensor<string, []> input_93_pad_type_0 = const()[name = tensor<string, []>("input_93_pad_type_0"), val = tensor<string, []>("custom")];
220
+ tensor<int32, [4]> input_93_pad_0 = const()[name = tensor<string, []>("input_93_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
221
+ tensor<int32, [2]> input_93_strides_0 = const()[name = tensor<string, []>("input_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
222
+ tensor<int32, [2]> input_93_dilations_0 = const()[name = tensor<string, []>("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
223
+ tensor<int32, []> input_93_groups_0 = const()[name = tensor<string, []>("input_93_groups_0"), val = tensor<int32, []>(1)];
224
+ tensor<fp32, [64, 64, 3, 3]> const_39 = const()[name = tensor<string, []>("const_39"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8216320)))];
225
+ tensor<fp32, [64]> const_40 = const()[name = tensor<string, []>("const_40"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8363840)))];
226
+ tensor<fp32, [1, 64, 40, 249]> out_13 = conv(bias = const_40, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = const_39, x = input_91)[name = tensor<string, []>("out_13")];
227
+ tensor<fp32, [1, 64, 40, 249]> input_95 = add(x = out_13, y = input_85)[name = tensor<string, []>("input_95")];
228
+ tensor<fp32, [1, 64, 40, 249]> input_97 = relu(x = input_95)[name = tensor<string, []>("input_97")];
229
+ tensor<string, []> input_99_pad_type_0 = const()[name = tensor<string, []>("input_99_pad_type_0"), val = tensor<string, []>("custom")];
230
+ tensor<int32, [4]> input_99_pad_0 = const()[name = tensor<string, []>("input_99_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
231
+ tensor<int32, [2]> input_99_strides_0 = const()[name = tensor<string, []>("input_99_strides_0"), val = tensor<int32, [2]>([2, 2])];
232
+ tensor<int32, [2]> input_99_dilations_0 = const()[name = tensor<string, []>("input_99_dilations_0"), val = tensor<int32, [2]>([1, 1])];
233
+ tensor<int32, []> input_99_groups_0 = const()[name = tensor<string, []>("input_99_groups_0"), val = tensor<int32, []>(1)];
234
+ tensor<fp32, [128, 64, 3, 3]> const_41 = const()[name = tensor<string, []>("const_41"), val = tensor<fp32, [128, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8364160)))];
235
+ tensor<fp32, [128]> const_42 = const()[name = tensor<string, []>("const_42"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8659136)))];
236
+ tensor<fp32, [1, 128, 20, 125]> input_101 = conv(bias = const_42, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = const_41, x = input_97)[name = tensor<string, []>("input_101")];
237
+ tensor<fp32, [1, 128, 20, 125]> input_103 = relu(x = input_101)[name = tensor<string, []>("input_103")];
238
+ tensor<string, []> input_105_pad_type_0 = const()[name = tensor<string, []>("input_105_pad_type_0"), val = tensor<string, []>("custom")];
239
+ tensor<int32, [4]> input_105_pad_0 = const()[name = tensor<string, []>("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
240
+ tensor<int32, [2]> input_105_strides_0 = const()[name = tensor<string, []>("input_105_strides_0"), val = tensor<int32, [2]>([1, 1])];
241
+ tensor<int32, [2]> input_105_dilations_0 = const()[name = tensor<string, []>("input_105_dilations_0"), val = tensor<int32, [2]>([1, 1])];
242
+ tensor<int32, []> input_105_groups_0 = const()[name = tensor<string, []>("input_105_groups_0"), val = tensor<int32, []>(1)];
243
+ tensor<fp32, [128, 128, 3, 3]> const_43 = const()[name = tensor<string, []>("const_43"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8659712)))];
244
+ tensor<fp32, [128]> const_44 = const()[name = tensor<string, []>("const_44"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9249600)))];
245
+ tensor<fp32, [1, 128, 20, 125]> out_15 = conv(bias = const_44, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = const_43, x = input_103)[name = tensor<string, []>("out_15")];
246
+ tensor<string, []> input_107_pad_type_0 = const()[name = tensor<string, []>("input_107_pad_type_0"), val = tensor<string, []>("valid")];
247
+ tensor<int32, [2]> input_107_strides_0 = const()[name = tensor<string, []>("input_107_strides_0"), val = tensor<int32, [2]>([2, 2])];
248
+ tensor<int32, [4]> input_107_pad_0 = const()[name = tensor<string, []>("input_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
249
+ tensor<int32, [2]> input_107_dilations_0 = const()[name = tensor<string, []>("input_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
250
+ tensor<int32, []> input_107_groups_0 = const()[name = tensor<string, []>("input_107_groups_0"), val = tensor<int32, []>(1)];
251
+ tensor<fp32, [128, 64, 1, 1]> const_45 = const()[name = tensor<string, []>("const_45"), val = tensor<fp32, [128, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9250176)))];
252
+ tensor<fp32, [128]> const_46 = const()[name = tensor<string, []>("const_46"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9283008)))];
253
+ tensor<fp32, [1, 128, 20, 125]> var_394 = conv(bias = const_46, dilations = input_107_dilations_0, groups = input_107_groups_0, pad = input_107_pad_0, pad_type = input_107_pad_type_0, strides = input_107_strides_0, weight = const_45, x = input_97)[name = tensor<string, []>("op_394")];
254
+ tensor<fp32, [1, 128, 20, 125]> input_109 = add(x = out_15, y = var_394)[name = tensor<string, []>("input_109")];
255
+ tensor<fp32, [1, 128, 20, 125]> input_111 = relu(x = input_109)[name = tensor<string, []>("input_111")];
256
+ tensor<string, []> input_113_pad_type_0 = const()[name = tensor<string, []>("input_113_pad_type_0"), val = tensor<string, []>("custom")];
257
+ tensor<int32, [4]> input_113_pad_0 = const()[name = tensor<string, []>("input_113_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
258
+ tensor<int32, [2]> input_113_strides_0 = const()[name = tensor<string, []>("input_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
259
+ tensor<int32, [2]> input_113_dilations_0 = const()[name = tensor<string, []>("input_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
260
+ tensor<int32, []> input_113_groups_0 = const()[name = tensor<string, []>("input_113_groups_0"), val = tensor<int32, []>(1)];
261
+ tensor<fp32, [128, 128, 3, 3]> const_47 = const()[name = tensor<string, []>("const_47"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9283584)))];
262
+ tensor<fp32, [128]> const_48 = const()[name = tensor<string, []>("const_48"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9873472)))];
263
+ tensor<fp32, [1, 128, 20, 125]> input_115 = conv(bias = const_48, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = const_47, x = input_111)[name = tensor<string, []>("input_115")];
264
+ tensor<fp32, [1, 128, 20, 125]> input_117 = relu(x = input_115)[name = tensor<string, []>("input_117")];
265
+ tensor<string, []> input_119_pad_type_0 = const()[name = tensor<string, []>("input_119_pad_type_0"), val = tensor<string, []>("custom")];
266
+ tensor<int32, [4]> input_119_pad_0 = const()[name = tensor<string, []>("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
267
+ tensor<int32, [2]> input_119_strides_0 = const()[name = tensor<string, []>("input_119_strides_0"), val = tensor<int32, [2]>([1, 1])];
268
+ tensor<int32, [2]> input_119_dilations_0 = const()[name = tensor<string, []>("input_119_dilations_0"), val = tensor<int32, [2]>([1, 1])];
269
+ tensor<int32, []> input_119_groups_0 = const()[name = tensor<string, []>("input_119_groups_0"), val = tensor<int32, []>(1)];
270
+ tensor<fp32, [128, 128, 3, 3]> const_49 = const()[name = tensor<string, []>("const_49"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9874048)))];
271
+ tensor<fp32, [128]> const_50 = const()[name = tensor<string, []>("const_50"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10463936)))];
272
+ tensor<fp32, [1, 128, 20, 125]> out_17 = conv(bias = const_50, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = const_49, x = input_117)[name = tensor<string, []>("out_17")];
273
+ tensor<fp32, [1, 128, 20, 125]> input_121 = add(x = out_17, y = input_111)[name = tensor<string, []>("input_121")];
274
+ tensor<fp32, [1, 128, 20, 125]> input_123 = relu(x = input_121)[name = tensor<string, []>("input_123")];
275
+ tensor<string, []> input_125_pad_type_0 = const()[name = tensor<string, []>("input_125_pad_type_0"), val = tensor<string, []>("custom")];
276
+ tensor<int32, [4]> input_125_pad_0 = const()[name = tensor<string, []>("input_125_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
277
+ tensor<int32, [2]> input_125_strides_0 = const()[name = tensor<string, []>("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])];
278
+ tensor<int32, [2]> input_125_dilations_0 = const()[name = tensor<string, []>("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])];
279
+ tensor<int32, []> input_125_groups_0 = const()[name = tensor<string, []>("input_125_groups_0"), val = tensor<int32, []>(1)];
280
+ tensor<fp32, [128, 128, 3, 3]> const_51 = const()[name = tensor<string, []>("const_51"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10464512)))];
281
+ tensor<fp32, [128]> const_52 = const()[name = tensor<string, []>("const_52"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11054400)))];
282
+ tensor<fp32, [1, 128, 20, 125]> input_127 = conv(bias = const_52, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = const_51, x = input_123)[name = tensor<string, []>("input_127")];
283
+ tensor<fp32, [1, 128, 20, 125]> input_129 = relu(x = input_127)[name = tensor<string, []>("input_129")];
284
+ tensor<string, []> input_131_pad_type_0 = const()[name = tensor<string, []>("input_131_pad_type_0"), val = tensor<string, []>("custom")];
285
+ tensor<int32, [4]> input_131_pad_0 = const()[name = tensor<string, []>("input_131_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
286
+ tensor<int32, [2]> input_131_strides_0 = const()[name = tensor<string, []>("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
287
+ tensor<int32, [2]> input_131_dilations_0 = const()[name = tensor<string, []>("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
288
+ tensor<int32, []> input_131_groups_0 = const()[name = tensor<string, []>("input_131_groups_0"), val = tensor<int32, []>(1)];
289
+ tensor<fp32, [128, 128, 3, 3]> const_53 = const()[name = tensor<string, []>("const_53"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11054976)))];
290
+ tensor<fp32, [128]> const_54 = const()[name = tensor<string, []>("const_54"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11644864)))];
291
+ tensor<fp32, [1, 128, 20, 125]> out_19 = conv(bias = const_54, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = const_53, x = input_129)[name = tensor<string, []>("out_19")];
292
+ tensor<fp32, [1, 128, 20, 125]> input_133 = add(x = out_19, y = input_123)[name = tensor<string, []>("input_133")];
293
+ tensor<fp32, [1, 128, 20, 125]> input_135 = relu(x = input_133)[name = tensor<string, []>("input_135")];
294
+ tensor<string, []> input_137_pad_type_0 = const()[name = tensor<string, []>("input_137_pad_type_0"), val = tensor<string, []>("custom")];
295
+ tensor<int32, [4]> input_137_pad_0 = const()[name = tensor<string, []>("input_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
296
+ tensor<int32, [2]> input_137_strides_0 = const()[name = tensor<string, []>("input_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
297
+ tensor<int32, [2]> input_137_dilations_0 = const()[name = tensor<string, []>("input_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
298
+ tensor<int32, []> input_137_groups_0 = const()[name = tensor<string, []>("input_137_groups_0"), val = tensor<int32, []>(1)];
299
+ tensor<fp32, [128, 128, 3, 3]> const_55 = const()[name = tensor<string, []>("const_55"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11645440)))];
300
+ tensor<fp32, [128]> const_56 = const()[name = tensor<string, []>("const_56"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12235328)))];
301
+ tensor<fp32, [1, 128, 20, 125]> input_139 = conv(bias = const_56, dilations = input_137_dilations_0, groups = input_137_groups_0, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = input_137_strides_0, weight = const_55, x = input_135)[name = tensor<string, []>("input_139")];
302
+ tensor<fp32, [1, 128, 20, 125]> input_141 = relu(x = input_139)[name = tensor<string, []>("input_141")];
303
+ tensor<string, []> input_143_pad_type_0 = const()[name = tensor<string, []>("input_143_pad_type_0"), val = tensor<string, []>("custom")];
304
+ tensor<int32, [4]> input_143_pad_0 = const()[name = tensor<string, []>("input_143_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
305
+ tensor<int32, [2]> input_143_strides_0 = const()[name = tensor<string, []>("input_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
306
+ tensor<int32, [2]> input_143_dilations_0 = const()[name = tensor<string, []>("input_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
307
+ tensor<int32, []> input_143_groups_0 = const()[name = tensor<string, []>("input_143_groups_0"), val = tensor<int32, []>(1)];
308
+ tensor<fp32, [128, 128, 3, 3]> const_57 = const()[name = tensor<string, []>("const_57"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12235904)))];
309
+ tensor<fp32, [128]> const_58 = const()[name = tensor<string, []>("const_58"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12825792)))];
310
+ tensor<fp32, [1, 128, 20, 125]> out_21 = conv(bias = const_58, dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = const_57, x = input_141)[name = tensor<string, []>("out_21")];
311
+ tensor<fp32, [1, 128, 20, 125]> input_145 = add(x = out_21, y = input_135)[name = tensor<string, []>("input_145")];
312
+ tensor<fp32, [1, 128, 20, 125]> input_147 = relu(x = input_145)[name = tensor<string, []>("input_147")];
313
+ tensor<string, []> input_149_pad_type_0 = const()[name = tensor<string, []>("input_149_pad_type_0"), val = tensor<string, []>("custom")];
314
+ tensor<int32, [4]> input_149_pad_0 = const()[name = tensor<string, []>("input_149_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
315
+ tensor<int32, [2]> input_149_strides_0 = const()[name = tensor<string, []>("input_149_strides_0"), val = tensor<int32, [2]>([1, 1])];
316
+ tensor<int32, [2]> input_149_dilations_0 = const()[name = tensor<string, []>("input_149_dilations_0"), val = tensor<int32, [2]>([1, 1])];
317
+ tensor<int32, []> input_149_groups_0 = const()[name = tensor<string, []>("input_149_groups_0"), val = tensor<int32, []>(1)];
318
+ tensor<fp32, [128, 128, 3, 3]> const_59 = const()[name = tensor<string, []>("const_59"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12826368)))];
319
+ tensor<fp32, [128]> const_60 = const()[name = tensor<string, []>("const_60"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13416256)))];
320
+ tensor<fp32, [1, 128, 20, 125]> input_151 = conv(bias = const_60, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = const_59, x = input_147)[name = tensor<string, []>("input_151")];
321
+ tensor<fp32, [1, 128, 20, 125]> input_153 = relu(x = input_151)[name = tensor<string, []>("input_153")];
322
+ tensor<string, []> input_155_pad_type_0 = const()[name = tensor<string, []>("input_155_pad_type_0"), val = tensor<string, []>("custom")];
323
+ tensor<int32, [4]> input_155_pad_0 = const()[name = tensor<string, []>("input_155_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
324
+ tensor<int32, [2]> input_155_strides_0 = const()[name = tensor<string, []>("input_155_strides_0"), val = tensor<int32, [2]>([1, 1])];
325
+ tensor<int32, [2]> input_155_dilations_0 = const()[name = tensor<string, []>("input_155_dilations_0"), val = tensor<int32, [2]>([1, 1])];
326
+ tensor<int32, []> input_155_groups_0 = const()[name = tensor<string, []>("input_155_groups_0"), val = tensor<int32, []>(1)];
327
+ tensor<fp32, [128, 128, 3, 3]> const_61 = const()[name = tensor<string, []>("const_61"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13416832)))];
328
+ tensor<fp32, [128]> const_62 = const()[name = tensor<string, []>("const_62"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14006720)))];
329
+ tensor<fp32, [1, 128, 20, 125]> out_23 = conv(bias = const_62, dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = const_61, x = input_153)[name = tensor<string, []>("out_23")];
330
+ tensor<fp32, [1, 128, 20, 125]> input_157 = add(x = out_23, y = input_147)[name = tensor<string, []>("input_157")];
331
+ tensor<fp32, [1, 128, 20, 125]> input_159 = relu(x = input_157)[name = tensor<string, []>("input_159")];
332
+ tensor<string, []> input_161_pad_type_0 = const()[name = tensor<string, []>("input_161_pad_type_0"), val = tensor<string, []>("custom")];
333
+ tensor<int32, [4]> input_161_pad_0 = const()[name = tensor<string, []>("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
334
+ tensor<int32, [2]> input_161_strides_0 = const()[name = tensor<string, []>("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
335
+ tensor<int32, [2]> input_161_dilations_0 = const()[name = tensor<string, []>("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
336
+ tensor<int32, []> input_161_groups_0 = const()[name = tensor<string, []>("input_161_groups_0"), val = tensor<int32, []>(1)];
337
+ tensor<fp32, [128, 128, 3, 3]> const_63 = const()[name = tensor<string, []>("const_63"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14007296)))];
338
+ tensor<fp32, [128]> const_64 = const()[name = tensor<string, []>("const_64"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14597184)))];
339
+ tensor<fp32, [1, 128, 20, 125]> input_163 = conv(bias = const_64, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_63, x = input_159)[name = tensor<string, []>("input_163")];
340
+ tensor<fp32, [1, 128, 20, 125]> input_165 = relu(x = input_163)[name = tensor<string, []>("input_165")];
341
+ tensor<string, []> input_167_pad_type_0 = const()[name = tensor<string, []>("input_167_pad_type_0"), val = tensor<string, []>("custom")];
342
+ tensor<int32, [4]> input_167_pad_0 = const()[name = tensor<string, []>("input_167_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
343
+ tensor<int32, [2]> input_167_strides_0 = const()[name = tensor<string, []>("input_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
344
+ tensor<int32, [2]> input_167_dilations_0 = const()[name = tensor<string, []>("input_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
345
+ tensor<int32, []> input_167_groups_0 = const()[name = tensor<string, []>("input_167_groups_0"), val = tensor<int32, []>(1)];
346
+ tensor<fp32, [128, 128, 3, 3]> const_65 = const()[name = tensor<string, []>("const_65"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14597760)))];
347
+ tensor<fp32, [128]> const_66 = const()[name = tensor<string, []>("const_66"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15187648)))];
348
+ tensor<fp32, [1, 128, 20, 125]> out_25 = conv(bias = const_66, dilations = input_167_dilations_0, groups = input_167_groups_0, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = input_167_strides_0, weight = const_65, x = input_165)[name = tensor<string, []>("out_25")];
349
+ tensor<fp32, [1, 128, 20, 125]> input_169 = add(x = out_25, y = input_159)[name = tensor<string, []>("input_169")];
350
+ tensor<fp32, [1, 128, 20, 125]> input_171 = relu(x = input_169)[name = tensor<string, []>("input_171")];
351
+ tensor<string, []> input_173_pad_type_0 = const()[name = tensor<string, []>("input_173_pad_type_0"), val = tensor<string, []>("custom")];
352
+ tensor<int32, [4]> input_173_pad_0 = const()[name = tensor<string, []>("input_173_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
353
+ tensor<int32, [2]> input_173_strides_0 = const()[name = tensor<string, []>("input_173_strides_0"), val = tensor<int32, [2]>([2, 2])];
354
+ tensor<int32, [2]> input_173_dilations_0 = const()[name = tensor<string, []>("input_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
355
+ tensor<int32, []> input_173_groups_0 = const()[name = tensor<string, []>("input_173_groups_0"), val = tensor<int32, []>(1)];
356
+ tensor<fp32, [256, 128, 3, 3]> const_67 = const()[name = tensor<string, []>("const_67"), val = tensor<fp32, [256, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15188224)))];
357
+ tensor<fp32, [256]> const_68 = const()[name = tensor<string, []>("const_68"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16367936)))];
358
+ tensor<fp32, [1, 256, 10, 63]> input_175 = conv(bias = const_68, dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = const_67, x = input_171)[name = tensor<string, []>("input_175")];
359
+ tensor<fp32, [1, 256, 10, 63]> input_177 = relu(x = input_175)[name = tensor<string, []>("input_177")];
360
+ tensor<string, []> input_179_pad_type_0 = const()[name = tensor<string, []>("input_179_pad_type_0"), val = tensor<string, []>("custom")];
361
+ tensor<int32, [4]> input_179_pad_0 = const()[name = tensor<string, []>("input_179_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
362
+ tensor<int32, [2]> input_179_strides_0 = const()[name = tensor<string, []>("input_179_strides_0"), val = tensor<int32, [2]>([1, 1])];
363
+ tensor<int32, [2]> input_179_dilations_0 = const()[name = tensor<string, []>("input_179_dilations_0"), val = tensor<int32, [2]>([1, 1])];
364
+ tensor<int32, []> input_179_groups_0 = const()[name = tensor<string, []>("input_179_groups_0"), val = tensor<int32, []>(1)];
365
+ tensor<fp32, [256, 256, 3, 3]> const_69 = const()[name = tensor<string, []>("const_69"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16369024)))];
366
+ tensor<fp32, [256]> const_70 = const()[name = tensor<string, []>("const_70"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18728384)))];
367
+ tensor<fp32, [1, 256, 10, 63]> out_27 = conv(bias = const_70, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = const_69, x = input_177)[name = tensor<string, []>("out_27")];
368
+ tensor<string, []> input_181_pad_type_0 = const()[name = tensor<string, []>("input_181_pad_type_0"), val = tensor<string, []>("valid")];
369
+ tensor<int32, [2]> input_181_strides_0 = const()[name = tensor<string, []>("input_181_strides_0"), val = tensor<int32, [2]>([2, 2])];
370
+ tensor<int32, [4]> input_181_pad_0 = const()[name = tensor<string, []>("input_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
371
+ tensor<int32, [2]> input_181_dilations_0 = const()[name = tensor<string, []>("input_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
372
+ tensor<int32, []> input_181_groups_0 = const()[name = tensor<string, []>("input_181_groups_0"), val = tensor<int32, []>(1)];
373
+ tensor<fp32, [256, 128, 1, 1]> const_71 = const()[name = tensor<string, []>("const_71"), val = tensor<fp32, [256, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18729472)))];
374
+ tensor<fp32, [256]> const_72 = const()[name = tensor<string, []>("const_72"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18860608)))];
375
+ tensor<fp32, [1, 256, 10, 63]> var_585 = conv(bias = const_72, dilations = input_181_dilations_0, groups = input_181_groups_0, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = input_181_strides_0, weight = const_71, x = input_171)[name = tensor<string, []>("op_585")];
376
+ tensor<fp32, [1, 256, 10, 63]> input_183 = add(x = out_27, y = var_585)[name = tensor<string, []>("input_183")];
377
+ tensor<fp32, [1, 256, 10, 63]> input_185 = relu(x = input_183)[name = tensor<string, []>("input_185")];
378
+ tensor<string, []> input_187_pad_type_0 = const()[name = tensor<string, []>("input_187_pad_type_0"), val = tensor<string, []>("custom")];
379
+ tensor<int32, [4]> input_187_pad_0 = const()[name = tensor<string, []>("input_187_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
380
+ tensor<int32, [2]> input_187_strides_0 = const()[name = tensor<string, []>("input_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
381
+ tensor<int32, [2]> input_187_dilations_0 = const()[name = tensor<string, []>("input_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
382
+ tensor<int32, []> input_187_groups_0 = const()[name = tensor<string, []>("input_187_groups_0"), val = tensor<int32, []>(1)];
383
+ tensor<fp32, [256, 256, 3, 3]> const_73 = const()[name = tensor<string, []>("const_73"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18861696)))];
384
+ tensor<fp32, [256]> const_74 = const()[name = tensor<string, []>("const_74"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21221056)))];
385
+ tensor<fp32, [1, 256, 10, 63]> input_189 = conv(bias = const_74, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = const_73, x = input_185)[name = tensor<string, []>("input_189")];
386
+ tensor<fp32, [1, 256, 10, 63]> input_191 = relu(x = input_189)[name = tensor<string, []>("input_191")];
387
+ tensor<string, []> input_193_pad_type_0 = const()[name = tensor<string, []>("input_193_pad_type_0"), val = tensor<string, []>("custom")];
388
+ tensor<int32, [4]> input_193_pad_0 = const()[name = tensor<string, []>("input_193_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
389
+ tensor<int32, [2]> input_193_strides_0 = const()[name = tensor<string, []>("input_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
390
+ tensor<int32, [2]> input_193_dilations_0 = const()[name = tensor<string, []>("input_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
391
+ tensor<int32, []> input_193_groups_0 = const()[name = tensor<string, []>("input_193_groups_0"), val = tensor<int32, []>(1)];
392
+ tensor<fp32, [256, 256, 3, 3]> const_75 = const()[name = tensor<string, []>("const_75"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21222144)))];
393
+ tensor<fp32, [256]> const_76 = const()[name = tensor<string, []>("const_76"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23581504)))];
394
+ tensor<fp32, [1, 256, 10, 63]> out_29 = conv(bias = const_76, dilations = input_193_dilations_0, groups = input_193_groups_0, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = input_193_strides_0, weight = const_75, x = input_191)[name = tensor<string, []>("out_29")];
395
+ tensor<fp32, [1, 256, 10, 63]> input_195 = add(x = out_29, y = input_185)[name = tensor<string, []>("input_195")];
396
+ tensor<fp32, [1, 256, 10, 63]> input_197 = relu(x = input_195)[name = tensor<string, []>("input_197")];
397
+ tensor<string, []> input_199_pad_type_0 = const()[name = tensor<string, []>("input_199_pad_type_0"), val = tensor<string, []>("custom")];
398
+ tensor<int32, [4]> input_199_pad_0 = const()[name = tensor<string, []>("input_199_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
399
+ tensor<int32, [2]> input_199_strides_0 = const()[name = tensor<string, []>("input_199_strides_0"), val = tensor<int32, [2]>([1, 1])];
400
+ tensor<int32, [2]> input_199_dilations_0 = const()[name = tensor<string, []>("input_199_dilations_0"), val = tensor<int32, [2]>([1, 1])];
401
+ tensor<int32, []> input_199_groups_0 = const()[name = tensor<string, []>("input_199_groups_0"), val = tensor<int32, []>(1)];
402
+ tensor<fp32, [256, 256, 3, 3]> const_77 = const()[name = tensor<string, []>("const_77"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23582592)))];
403
+ tensor<fp32, [256]> const_78 = const()[name = tensor<string, []>("const_78"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25941952)))];
404
+ tensor<fp32, [1, 256, 10, 63]> input_201 = conv(bias = const_78, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = const_77, x = input_197)[name = tensor<string, []>("input_201")];
405
+ tensor<fp32, [1, 256, 10, 63]> input_203 = relu(x = input_201)[name = tensor<string, []>("input_203")];
406
+ tensor<string, []> input_205_pad_type_0 = const()[name = tensor<string, []>("input_205_pad_type_0"), val = tensor<string, []>("custom")];
407
+ tensor<int32, [4]> input_205_pad_0 = const()[name = tensor<string, []>("input_205_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
408
+ tensor<int32, [2]> input_205_strides_0 = const()[name = tensor<string, []>("input_205_strides_0"), val = tensor<int32, [2]>([1, 1])];
409
+ tensor<int32, [2]> input_205_dilations_0 = const()[name = tensor<string, []>("input_205_dilations_0"), val = tensor<int32, [2]>([1, 1])];
410
+ tensor<int32, []> input_205_groups_0 = const()[name = tensor<string, []>("input_205_groups_0"), val = tensor<int32, []>(1)];
411
+ tensor<fp32, [256, 256, 3, 3]> const_79 = const()[name = tensor<string, []>("const_79"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25943040)))];
412
+ tensor<fp32, [256]> const_80 = const()[name = tensor<string, []>("const_80"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28302400)))];
413
+ tensor<fp32, [1, 256, 10, 63]> out = conv(bias = const_80, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_79, x = input_203)[name = tensor<string, []>("out")];
414
+ tensor<fp32, [1, 256, 10, 63]> input_207 = add(x = out, y = input_197)[name = tensor<string, []>("input_207")];
415
+ tensor<fp32, [1, 256, 10, 63]> features = relu(x = input_207)[name = tensor<string, []>("features")];
416
+ tensor<int32, [3]> var_654 = const()[name = tensor<string, []>("op_654"), val = tensor<int32, [3]>([1, 2560, 63])];
417
+ tensor<fp32, [1, 2560, 63]> sequences = reshape(shape = var_654, x = features)[name = tensor<string, []>("sequences")];
418
+ tensor<int32, [1]> mean_axes_0 = const()[name = tensor<string, []>("mean_axes_0"), val = tensor<int32, [1]>([-1])];
419
+ tensor<bool, []> mean_keep_dims_0 = const()[name = tensor<string, []>("mean_keep_dims_0"), val = tensor<bool, []>(false)];
420
+ tensor<fp32, [1, 2560]> mean = reduce_mean(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = sequences)[name = tensor<string, []>("mean")];
421
+ tensor<int32, [1]> var_658_axes_0 = const()[name = tensor<string, []>("op_658_axes_0"), val = tensor<int32, [1]>([-1])];
422
+ tensor<fp32, [1, 2560, 1]> var_658 = expand_dims(axes = var_658_axes_0, x = mean)[name = tensor<string, []>("op_658")];
423
+ tensor<fp32, [1, 2560, 63]> centered = sub(x = sequences, y = var_658)[name = tensor<string, []>("centered")];
424
+ tensor<fp32, [1, 2560, 63]> var_660 = mul(x = centered, y = centered)[name = tensor<string, []>("op_660")];
425
+ tensor<int32, [1]> sum_sq_axes_0 = const()[name = tensor<string, []>("sum_sq_axes_0"), val = tensor<int32, [1]>([-1])];
426
+ tensor<bool, []> sum_sq_keep_dims_0 = const()[name = tensor<string, []>("sum_sq_keep_dims_0"), val = tensor<bool, []>(false)];
427
+ tensor<fp32, [1, 2560]> sum_sq = reduce_sum(axes = sum_sq_axes_0, keep_dims = sum_sq_keep_dims_0, x = var_660)[name = tensor<string, []>("sum_sq")];
428
+ tensor<fp32, [1]> _inversed_var_y_0 = const()[name = tensor<string, []>("_inversed_var_y_0"), val = tensor<fp32, [1]>([0x1.08421p-6])];
429
+ tensor<fp32, [1, 2560]> _inversed_var = mul(x = sum_sq, y = _inversed_var_y_0)[name = tensor<string, []>("_inversed_var")];
430
+ tensor<fp32, []> const_8 = const()[name = tensor<string, []>("const_8"), val = tensor<fp32, []>(0x1.fffffep+127)];
431
+ tensor<fp32, [1, 2560]> clip_1 = clip(alpha = var_93, beta = const_8, x = _inversed_var)[name = tensor<string, []>("clip_1")];
432
+ tensor<fp32, [1, 2560]> std = sqrt(x = clip_1)[name = tensor<string, []>("std")];
433
+ tensor<bool, []> input_interleave_0 = const()[name = tensor<string, []>("input_interleave_0"), val = tensor<bool, []>(false)];
434
+ tensor<fp32, [1, 5120]> input = concat(axis = var_90, interleave = input_interleave_0, values = (mean, std))[name = tensor<string, []>("input")];
435
+ tensor<fp32, [1, 256]> embedding = linear(bias = resnet_seg_1_bias, weight = resnet_seg_1_weight, x = input)[name = tensor<string, []>("linear_0")];
436
+ } -> (embedding);
437
+ }
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+ },
37
+ "computePrecision" : "Mixed (Float32, Int32)",
38
+ "stateSchema" : [
39
+
40
+ ],
41
+ "isUpdatable" : "0",
42
+ "availability" : {
43
+ "macOS" : "14.0",
44
+ "tvOS" : "17.0",
45
+ "visionOS" : "1.0",
46
+ "watchOS" : "10.0",
47
+ "iOS" : "17.0",
48
+ "macCatalyst" : "17.0"
49
+ },
50
+ "modelType" : {
51
+ "name" : "MLModelType_mlProgram"
52
+ },
53
+ "inputSchema" : [
54
+ {
55
+ "hasShapeFlexibility" : "0",
56
+ "isOptional" : "0",
57
+ "dataType" : "Float32",
58
+ "formattedType" : "MultiArray (Float32 1 × 1 × 160000)",
59
+ "shortDescription" : "",
60
+ "shape" : "[1, 1, 160000]",
61
+ "name" : "audio",
62
+ "type" : "MultiArray"
63
+ }
64
+ ],
65
+ "userDefinedMetadata" : {
66
+ "com.github.apple.coremltools.conversion_date" : "2025-09-30",
67
+ "com.github.apple.coremltools.source" : "torch==2.8.0",
68
+ "com.github.apple.coremltools.version" : "9.0b1",
69
+ "com.github.apple.coremltools.source_dialect" : "TorchScript"
70
+ },
71
+ "generatedClassName" : "segmentation_community_1",
72
+ "method" : "predict"
73
+ }
74
+ ]
Segmentation.mlmodelc/model.mil ADDED
@@ -0,0 +1,135 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ program(1.0)
2
+ [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
3
+ {
4
+ func main<ios17>(tensor<fp32, [1, 1, 160000]> audio) {
5
+ tensor<fp32, [1]> sincnet_wav_norm1d_bias = const()[name = tensor<string, []>("sincnet_wav_norm1d_bias"), val = tensor<fp32, [1]>([0x1.73505ep-5])];
6
+ tensor<fp32, [1]> sincnet_wav_norm1d_weight = const()[name = tensor<string, []>("sincnet_wav_norm1d_weight"), val = tensor<fp32, [1]>([0x1.43f862p-7])];
7
+ tensor<fp32, [80]> sincnet_norm1d_0_bias = const()[name = tensor<string, []>("sincnet_norm1d_0_bias"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
8
+ tensor<fp32, [80]> sincnet_norm1d_0_weight = const()[name = tensor<string, []>("sincnet_norm1d_0_weight"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448)))];
9
+ tensor<fp32, [60]> sincnet_conv1d_1_bias = const()[name = tensor<string, []>("sincnet_conv1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(832)))];
10
+ tensor<fp32, [60, 80, 5]> sincnet_conv1d_1_weight = const()[name = tensor<string, []>("sincnet_conv1d_1_weight"), val = tensor<fp32, [60, 80, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152)))];
11
+ tensor<fp32, [60]> sincnet_norm1d_1_bias = const()[name = tensor<string, []>("sincnet_norm1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97216)))];
12
+ tensor<fp32, [60]> sincnet_norm1d_1_weight = const()[name = tensor<string, []>("sincnet_norm1d_1_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97536)))];
13
+ tensor<fp32, [60]> sincnet_conv1d_2_bias = const()[name = tensor<string, []>("sincnet_conv1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97856)))];
14
+ tensor<fp32, [60, 60, 5]> sincnet_conv1d_2_weight = const()[name = tensor<string, []>("sincnet_conv1d_2_weight"), val = tensor<fp32, [60, 60, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98176)))];
15
+ tensor<fp32, [60]> sincnet_norm1d_2_bias = const()[name = tensor<string, []>("sincnet_norm1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170240)))];
16
+ tensor<fp32, [60]> sincnet_norm1d_2_weight = const()[name = tensor<string, []>("sincnet_norm1d_2_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170560)))];
17
+ tensor<fp32, [128]> linear_0_bias = const()[name = tensor<string, []>("linear_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170880)))];
18
+ tensor<fp32, [128, 256]> linear_0_weight = const()[name = tensor<string, []>("linear_0_weight"), val = tensor<fp32, [128, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171456)))];
19
+ tensor<fp32, [128]> linear_1_bias = const()[name = tensor<string, []>("linear_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302592)))];
20
+ tensor<fp32, [128, 128]> linear_1_weight = const()[name = tensor<string, []>("linear_1_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303168)))];
21
+ tensor<fp32, [7]> classifier_bias = const()[name = tensor<string, []>("classifier_bias"), val = tensor<fp32, [7]>([-0x1.00e888p+0, 0x1.67cb52p-2, 0x1.3d87fp-1, 0x1.c8aa8p-2, -0x1.445f5ep-2, -0x1.591274p-1, -0x1.8fb70ep-2])];
22
+ tensor<fp32, [7, 128]> classifier_weight = const()[name = tensor<string, []>("classifier_weight"), val = tensor<fp32, [7, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368768)))];
23
+ tensor<fp32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<fp32, []>(0x1.47ae14p-7)];
24
+ tensor<fp32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
25
+ tensor<fp32, [1, 1, 160000]> waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = audio)[name = tensor<string, []>("waveform")];
26
+ tensor<fp32, [80, 1, 251]> filters = const()[name = tensor<string, []>("filters"), val = tensor<fp32, [80, 1, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372416)))];
27
+ tensor<string, []> outputs_pad_type_0 = const()[name = tensor<string, []>("outputs_pad_type_0"), val = tensor<string, []>("valid")];
28
+ tensor<int32, [1]> outputs_strides_0 = const()[name = tensor<string, []>("outputs_strides_0"), val = tensor<int32, [1]>([10])];
29
+ tensor<int32, [2]> outputs_pad_0 = const()[name = tensor<string, []>("outputs_pad_0"), val = tensor<int32, [2]>([0, 0])];
30
+ tensor<int32, [1]> outputs_dilations_0 = const()[name = tensor<string, []>("outputs_dilations_0"), val = tensor<int32, [1]>([1])];
31
+ tensor<int32, []> outputs_groups_0 = const()[name = tensor<string, []>("outputs_groups_0"), val = tensor<int32, []>(1)];
32
+ tensor<fp32, [1, 80, 15975]> outputs = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters, x = waveform)[name = tensor<string, []>("outputs")];
33
+ tensor<fp32, [1, 80, 15975]> input_1 = abs(x = outputs)[name = tensor<string, []>("input_1")];
34
+ tensor<int32, [1]> var_119 = const()[name = tensor<string, []>("op_119"), val = tensor<int32, [1]>([3])];
35
+ tensor<int32, [1]> var_120 = const()[name = tensor<string, []>("op_120"), val = tensor<int32, [1]>([3])];
36
+ tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
37
+ tensor<int32, [2]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
38
+ tensor<bool, []> input_3_ceil_mode_0 = const()[name = tensor<string, []>("input_3_ceil_mode_0"), val = tensor<bool, []>(false)];
39
+ tensor<fp32, [1, 80, 5325]> input_3 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1)[name = tensor<string, []>("input_3")];
40
+ tensor<fp32, [1, 80, 5325]> input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = tensor<string, []>("input_5")];
41
+ tensor<fp32, [1, 80, 5325]> input_7 = leaky_relu(alpha = var_9, x = input_5)[name = tensor<string, []>("input_7")];
42
+ tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("valid")];
43
+ tensor<int32, [1]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [1]>([1])];
44
+ tensor<int32, [2]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
45
+ tensor<int32, [1]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [1]>([1])];
46
+ tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
47
+ tensor<fp32, [1, 60, 5321]> input_9 = conv(bias = sincnet_conv1d_1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight, x = input_7)[name = tensor<string, []>("input_9")];
48
+ tensor<int32, [1]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [1]>([3])];
49
+ tensor<int32, [1]> var_136 = const()[name = tensor<string, []>("op_136"), val = tensor<int32, [1]>([3])];
50
+ tensor<string, []> input_11_pad_type_0 = const()[name = tensor<string, []>("input_11_pad_type_0"), val = tensor<string, []>("custom")];
51
+ tensor<int32, [2]> input_11_pad_0 = const()[name = tensor<string, []>("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
52
+ tensor<bool, []> input_11_ceil_mode_0 = const()[name = tensor<string, []>("input_11_ceil_mode_0"), val = tensor<bool, []>(false)];
53
+ tensor<fp32, [1, 60, 1773]> input_11 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9)[name = tensor<string, []>("input_11")];
54
+ tensor<fp32, [1, 60, 1773]> input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = tensor<string, []>("input_13")];
55
+ tensor<fp32, [1, 60, 1773]> input_15 = leaky_relu(alpha = var_9, x = input_13)[name = tensor<string, []>("input_15")];
56
+ tensor<string, []> input_17_pad_type_0 = const()[name = tensor<string, []>("input_17_pad_type_0"), val = tensor<string, []>("valid")];
57
+ tensor<int32, [1]> input_17_strides_0 = const()[name = tensor<string, []>("input_17_strides_0"), val = tensor<int32, [1]>([1])];
58
+ tensor<int32, [2]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
59
+ tensor<int32, [1]> input_17_dilations_0 = const()[name = tensor<string, []>("input_17_dilations_0"), val = tensor<int32, [1]>([1])];
60
+ tensor<int32, []> input_17_groups_0 = const()[name = tensor<string, []>("input_17_groups_0"), val = tensor<int32, []>(1)];
61
+ tensor<fp32, [1, 60, 1769]> input_17 = conv(bias = sincnet_conv1d_2_bias, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight, x = input_15)[name = tensor<string, []>("input_17")];
62
+ tensor<int32, [1]> var_151 = const()[name = tensor<string, []>("op_151"), val = tensor<int32, [1]>([3])];
63
+ tensor<int32, [1]> var_152 = const()[name = tensor<string, []>("op_152"), val = tensor<int32, [1]>([3])];
64
+ tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")];
65
+ tensor<int32, [2]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
66
+ tensor<bool, []> input_19_ceil_mode_0 = const()[name = tensor<string, []>("input_19_ceil_mode_0"), val = tensor<bool, []>(false)];
67
+ tensor<fp32, [1, 60, 589]> input_19 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17)[name = tensor<string, []>("input_19")];
68
+ tensor<fp32, [1, 60, 589]> input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = tensor<string, []>("input_21")];
69
+ tensor<fp32, [1, 60, 589]> x = leaky_relu(alpha = var_9, x = input_21)[name = tensor<string, []>("x")];
70
+ tensor<int32, [3]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [3]>([2, 0, 1])];
71
+ tensor<fp32, [512]> add_0 = const()[name = tensor<string, []>("add_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452800)))];
72
+ tensor<fp32, [512]> add_1 = const()[name = tensor<string, []>("add_1"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454912)))];
73
+ tensor<fp32, [512, 60]> concat_4 = const()[name = tensor<string, []>("concat_4"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457024)))];
74
+ tensor<fp32, [512, 128]> concat_5 = const()[name = tensor<string, []>("concat_5"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579968)))];
75
+ tensor<fp32, [512, 60]> concat_6 = const()[name = tensor<string, []>("concat_6"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(842176)))];
76
+ tensor<fp32, [512, 128]> concat_7 = const()[name = tensor<string, []>("concat_7"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(965120)))];
77
+ tensor<fp32, [1, 256]> input_25_lstm_layer_0_lstm_h0_reshaped = const()[name = tensor<string, []>("input_25_lstm_layer_0_lstm_h0_reshaped"), val = tensor<fp32, [1, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1227328)))];
78
+ tensor<string, []> input_25_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_direction_0"), val = tensor<string, []>("bidirectional")];
79
+ tensor<bool, []> input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
80
+ tensor<string, []> input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
81
+ tensor<string, []> input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
82
+ tensor<string, []> input_25_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
83
+ tensor<fp32, [589, 1, 60]> transpose_4 = transpose(perm = transpose_4_perm_0, x = x)[name = tensor<string, []>("transpose_6")];
84
+ tensor<fp32, [589, 1, 256]> input_25_lstm_layer_0_0, tensor<fp32, [1, 256]> input_25_lstm_layer_0_1, tensor<fp32, [1, 256]> input_25_lstm_layer_0_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_5, weight_hh_back = concat_7, weight_ih = concat_4, weight_ih_back = concat_6, x = transpose_4)[name = tensor<string, []>("input_25_lstm_layer_0")];
85
+ tensor<fp32, [512]> add_2 = const()[name = tensor<string, []>("add_2"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1228416)))];
86
+ tensor<fp32, [512]> add_3 = const()[name = tensor<string, []>("add_3"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1230528)))];
87
+ tensor<fp32, [512, 256]> concat_14 = const()[name = tensor<string, []>("concat_14"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1232640)))];
88
+ tensor<fp32, [512, 128]> concat_15 = const()[name = tensor<string, []>("concat_15"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1756992)))];
89
+ tensor<fp32, [512, 256]> concat_16 = const()[name = tensor<string, []>("concat_16"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2019200)))];
90
+ tensor<fp32, [512, 128]> concat_17 = const()[name = tensor<string, []>("concat_17"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2543552)))];
91
+ tensor<string, []> input_25_lstm_layer_1_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_direction_0"), val = tensor<string, []>("bidirectional")];
92
+ tensor<bool, []> input_25_lstm_layer_1_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_output_sequence_0"), val = tensor<bool, []>(true)];
93
+ tensor<string, []> input_25_lstm_layer_1_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
94
+ tensor<string, []> input_25_lstm_layer_1_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_cell_activation_0"), val = tensor<string, []>("tanh")];
95
+ tensor<string, []> input_25_lstm_layer_1_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_activation_0"), val = tensor<string, []>("tanh")];
96
+ tensor<fp32, [589, 1, 256]> input_25_lstm_layer_1_0, tensor<fp32, [1, 256]> input_25_lstm_layer_1_1, tensor<fp32, [1, 256]> input_25_lstm_layer_1_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_15, weight_hh_back = concat_17, weight_ih = concat_14, weight_ih_back = concat_16, x = input_25_lstm_layer_0_0)[name = tensor<string, []>("input_25_lstm_layer_1")];
97
+ tensor<fp32, [512]> add_4 = const()[name = tensor<string, []>("add_4"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2805760)))];
98
+ tensor<fp32, [512]> add_5 = const()[name = tensor<string, []>("add_5"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2807872)))];
99
+ tensor<fp32, [512, 256]> concat_24 = const()[name = tensor<string, []>("concat_24"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2809984)))];
100
+ tensor<fp32, [512, 128]> concat_25 = const()[name = tensor<string, []>("concat_25"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3334336)))];
101
+ tensor<fp32, [512, 256]> concat_26 = const()[name = tensor<string, []>("concat_26"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3596544)))];
102
+ tensor<fp32, [512, 128]> concat_27 = const()[name = tensor<string, []>("concat_27"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4120896)))];
103
+ tensor<string, []> input_25_lstm_layer_2_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_direction_0"), val = tensor<string, []>("bidirectional")];
104
+ tensor<bool, []> input_25_lstm_layer_2_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_output_sequence_0"), val = tensor<bool, []>(true)];
105
+ tensor<string, []> input_25_lstm_layer_2_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
106
+ tensor<string, []> input_25_lstm_layer_2_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_cell_activation_0"), val = tensor<string, []>("tanh")];
107
+ tensor<string, []> input_25_lstm_layer_2_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_activation_0"), val = tensor<string, []>("tanh")];
108
+ tensor<fp32, [589, 1, 256]> input_25_lstm_layer_2_0, tensor<fp32, [1, 256]> input_25_lstm_layer_2_1, tensor<fp32, [1, 256]> input_25_lstm_layer_2_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_25, weight_hh_back = concat_27, weight_ih = concat_24, weight_ih_back = concat_26, x = input_25_lstm_layer_1_0)[name = tensor<string, []>("input_25_lstm_layer_2")];
109
+ tensor<fp32, [512]> add_6 = const()[name = tensor<string, []>("add_6"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4383104)))];
110
+ tensor<fp32, [512]> add_7 = const()[name = tensor<string, []>("add_7"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4385216)))];
111
+ tensor<fp32, [512, 256]> concat_34 = const()[name = tensor<string, []>("concat_34"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4387328)))];
112
+ tensor<fp32, [512, 128]> concat_35 = const()[name = tensor<string, []>("concat_35"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4911680)))];
113
+ tensor<fp32, [512, 256]> concat_36 = const()[name = tensor<string, []>("concat_36"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5173888)))];
114
+ tensor<fp32, [512, 128]> concat_37 = const()[name = tensor<string, []>("concat_37"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5698240)))];
115
+ tensor<string, []> input_25_batch_first_direction_0 = const()[name = tensor<string, []>("input_25_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
116
+ tensor<bool, []> input_25_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_25_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
117
+ tensor<string, []> input_25_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
118
+ tensor<string, []> input_25_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
119
+ tensor<string, []> input_25_batch_first_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_activation_0"), val = tensor<string, []>("tanh")];
120
+ tensor<fp32, [589, 1, 256]> input_25_batch_first_0, tensor<fp32, [1, 256]> input_25_batch_first_1, tensor<fp32, [1, 256]> input_25_batch_first_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_35, weight_hh_back = concat_37, weight_ih = concat_34, weight_ih_back = concat_36, x = input_25_lstm_layer_2_0)[name = tensor<string, []>("input_25_batch_first")];
121
+ tensor<int32, [3]> input_25_perm_0 = const()[name = tensor<string, []>("input_25_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
122
+ tensor<fp32, [1, 589, 256]> input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = tensor<string, []>("transpose_5")];
123
+ tensor<fp32, [1, 589, 128]> input_27 = linear(bias = linear_0_bias, weight = linear_0_weight, x = input_25)[name = tensor<string, []>("linear_0")];
124
+ tensor<fp32, []> var_220 = const()[name = tensor<string, []>("op_220"), val = tensor<fp32, []>(0x1.47ae14p-7)];
125
+ tensor<fp32, [1, 589, 128]> input_29 = leaky_relu(alpha = var_220, x = input_27)[name = tensor<string, []>("input_29")];
126
+ tensor<fp32, [1, 589, 128]> input_31 = linear(bias = linear_1_bias, weight = linear_1_weight, x = input_29)[name = tensor<string, []>("linear_1")];
127
+ tensor<fp32, []> var_225 = const()[name = tensor<string, []>("op_225"), val = tensor<fp32, []>(0x1.47ae14p-7)];
128
+ tensor<fp32, [1, 589, 128]> input_33 = leaky_relu(alpha = var_225, x = input_31)[name = tensor<string, []>("input_33")];
129
+ tensor<fp32, [1, 589, 7]> input = linear(bias = classifier_bias, weight = classifier_weight, x = input_33)[name = tensor<string, []>("linear_2")];
130
+ tensor<int32, []> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, []>(-1)];
131
+ tensor<fp32, [1, 589, 7]> var_232_softmax = softmax(axis = var_231, x = input)[name = tensor<string, []>("op_232_softmax")];
132
+ tensor<fp32, []> var_232_epsilon_0 = const()[name = tensor<string, []>("op_232_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
133
+ tensor<fp32, [1, 589, 7]> log_probs = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = tensor<string, []>("op_232")];
134
+ } -> (log_probs);
135
+ }
Segmentation.mlmodelc/weights/weight.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:a847db3ca569feb6ed3f3f998a4525bee7481d170a8804cd1b47d28823b5bcc0
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+ size 5960448