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See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129759 Approved by: https://github.com/justinchuby, https://github.com/ezyang
42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
"""
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This is a script for end-to-end mobile custom build test purpose. It prepares
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MobileNetV2 TorchScript model, and dumps root ops used by the model for custom
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build script to create a tailored build which only contains these used ops.
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"""
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import yaml
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from torchvision import models
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import torch
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# Download and trace the model.
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model = models.mobilenet_v2(weights=models.MobileNet_V2_Weights.IMAGENET1K_V1)
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model.eval()
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example = torch.rand(1, 3, 224, 224)
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traced_script_module = torch.jit.trace(model, example)
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# Save traced TorchScript model.
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traced_script_module.save("MobileNetV2.pt")
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# Dump root ops used by the model (for custom build optimization).
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ops = torch.jit.export_opnames(traced_script_module)
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# Besides the ops used by the model, custom c++ client code might use some extra
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# ops, too. For example, the dummy predictor.cpp driver in this test suite calls
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# `aten::ones` to create all-one-tensor for testing purpose, which is not used
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# by the MobileNetV2 model itself.
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#
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# This is less a problem for Android, where we expect users to use the limited
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# set of Java APIs. To actually solve this problem, we probably need ask users
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# to run code analyzer against their client code to dump these extra root ops.
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# So it will require more work to switch to custom build with dynamic dispatch -
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# in static dispatch case these extra ops will be kept by linker automatically.
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#
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# For CI purpose this one-off hack is probably fine? :)
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EXTRA_CI_ROOT_OPS = ["aten::ones"]
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ops.extend(EXTRA_CI_ROOT_OPS)
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with open("MobileNetV2.yaml", "w") as output:
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yaml.dump(ops, output)
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