Add TorchVision classification models: SqueezeNet, DenseNet, ShuffleN…#1550
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alinpahontu2912 wants to merge 4 commits intodotnet:mainfrom
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Add TorchVision classification models: SqueezeNet, DenseNet, ShuffleN…#1550alinpahontu2912 wants to merge 4 commits intodotnet:mainfrom
alinpahontu2912 wants to merge 4 commits intodotnet:mainfrom
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…etV2, EfficientNet, MNASNet Add 5 new model families (21 variants) ported from PyTorch torchvision: - SqueezeNet 1.0/1.1 - DenseNet-121/161/169/201 - ShuffleNet V2 x0.5/x1.0/x1.5/x2.0 - EfficientNet B0-B7, EfficientNet V2 S/M/L - MNASNet 0.5/0.75/1.0/1.3 All models support pre-trained weight loading via weights_file/skipfc parameters with state_dict keys matching PyTorch exactly. Tests added for all new model families. TODO: The following torchvision classification models are not yet implemented: - RegNet (Y/X variants) - ConvNeXt (Tiny, Small, Base, Large) - Vision Transformer / ViT (B-16, B-32, L-16, L-32, H-14) - Swin Transformer (T, S, B) - Swin Transformer V2 (T, S, B) - MaxViT (T) Closes dotnet#586 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
- Fix EfficientNetB0 and EfficientNetV2S named_children order to match field declaration order (features, avgpool, classifier) - Fix DenseNet121 state_dict count from 242 to 727 to reflect proper registration of all dense layers via register_module Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
EfficientNetV2S (782 state dict entries) is the largest non-skipped model and causes the test host process to crash from memory pressure when run alongside all other model tests. Skip it following the same pattern used for other large EfficientNet variants (B1-B7, V2M, V2L). Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Pull request overview
Adds additional TorchVision classification model families to TorchSharp, aligning module structure/state_dict keys with torchvision to enable loading pre-trained weights from exported PyTorch state_dicts.
Changes:
- Introduces 5 new model families (SqueezeNet, DenseNet, ShuffleNetV2, EfficientNet V1/V2, MNASNet) with torchvision-style factory methods and optional weight loading (
weights_file/skipfc). - Adds/extends unit tests to validate basic model structure (named children), state_dict sizes for select variants, and forward output shapes.
Reviewed changes
Copilot reviewed 6 out of 6 changed files in this pull request and generated 4 comments.
Show a summary per file
| File | Description |
|---|---|
| test/TorchSharpTest/TestTorchVision.cs | Adds tests covering the new model families (some heavy variants explicitly skipped for CI stability). |
| src/TorchVision/models/SqueezeNet.cs | Implements SqueezeNet 1.0/1.1 modules + factory methods with optional weight loading. |
| src/TorchVision/models/DenseNet.cs | Implements DenseNet-121/161/169/201 modules + factory methods with optional weight loading. |
| src/TorchVision/models/ShuffleNetV2.cs | Implements ShuffleNet V2 x0.5/x1.0/x1.5/x2.0 modules + factory methods with optional weight loading. |
| src/TorchVision/models/EfficientNet.cs | Implements EfficientNet B0–B7 and EfficientNetV2 S/M/L modules + factory methods with optional weight loading. |
| src/TorchVision/models/MNASNet.cs | Implements MNASNet 0.5/0.75/1.0/1.3 modules + factory methods with optional weight loading. |
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Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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Fixes #588
Add 5 new model families (21 variants) ported from PyTorch torchvision:
All models support pre-trained weight loading via weights_file/skipfc parameters with state_dict keys matching PyTorch exactly.
Tests added for all new model families.
TODO: The following torchvision classification models are not yet implemented: