| """ |
| This is a script for end-to-end mobile custom build test purpose. It prepares |
| MobileNetV2 TorchScript model, and dumps root ops used by the model for custom |
| build script to create a tailored build which only contains these used ops. |
| """ |
| |
| import torch |
| import torchvision |
| import yaml |
| |
| # Download and trace the model. |
| model = torchvision.models.mobilenet_v2(pretrained=True) |
| model.eval() |
| example = torch.rand(1, 3, 224, 224) |
| traced_script_module = torch.jit.trace(model, example) |
| |
| # Save traced TorchScript model. |
| traced_script_module.save("MobileNetV2.pt") |
| |
| # Dump root ops used by the model (for custom build optimization). |
| ops = torch.jit.export_opnames(traced_script_module) |
| |
| # Besides the ops used by the model, custom c++ client code might use some extra |
| # ops, too. For example, the dummy predictor.cpp driver in this test suite calls |
| # `aten::ones` to create all-one-tensor for testing purpose, which is not used |
| # by the MobileNetV2 model itself. |
| # |
| # This is less a problem for Android, where we expect users to use the limited |
| # set of Java APIs. To actually solve this problem, we probably need ask users |
| # to run code analyzer against their client code to dump these extra root ops. |
| # So it will require more work to switch to custom build with dynamic dispatch - |
| # in static dispatch case these extra ops will be kept by linker automatically. |
| # |
| # For CI purpose this one-off hack is probably fine? :) |
| EXTRA_CI_ROOT_OPS = ['aten::ones'] |
| |
| ops.extend(EXTRA_CI_ROOT_OPS) |
| with open('MobileNetV2.yaml', 'w') as output: |
| yaml.dump(ops, output) |
