118 lines
4.1 KiB
Python
118 lines
4.1 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from distutils.version import LooseVersion
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import pytest
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import torch
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from tests.base import EvalModelTemplate
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from tests.base.datamodules import TrialMNISTDataModule
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from tests.base.models import ParityModuleRNN, BasicGAN
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@pytest.mark.parametrize("modelclass", [
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EvalModelTemplate,
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ParityModuleRNN,
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BasicGAN,
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])
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def test_torchscript_input_output(modelclass):
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""" Test that scripted LightningModule forward works. """
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model = modelclass()
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script = model.to_torchscript()
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assert isinstance(script, torch.jit.ScriptModule)
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model.eval()
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model_output = model(model.example_input_array)
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script_output = script(model.example_input_array)
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assert torch.allclose(script_output, model_output)
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@pytest.mark.parametrize("modelclass", [
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EvalModelTemplate,
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ParityModuleRNN,
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BasicGAN,
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])
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def test_torchscript_input_output_trace(modelclass):
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""" Test that traced LightningModule forward works. """
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model = modelclass()
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script = model.to_torchscript(method='trace')
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assert isinstance(script, torch.jit.ScriptModule)
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model.eval()
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model_output = model(model.example_input_array)
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script_output = script(model.example_input_array)
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assert torch.allclose(script_output, model_output)
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@pytest.mark.parametrize("device", [
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torch.device("cpu"),
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torch.device("cuda", 0)
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])
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU machine")
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def test_torchscript_device(device):
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""" Test that scripted module is on the correct device. """
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model = EvalModelTemplate().to(device)
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script = model.to_torchscript()
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assert next(script.parameters()).device == device
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script_output = script(model.example_input_array.to(device))
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assert script_output.device == device
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def test_torchscript_retain_training_state():
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""" Test that torchscript export does not alter the training mode of original model. """
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model = EvalModelTemplate()
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model.train(True)
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script = model.to_torchscript()
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assert model.training
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assert not script.training
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model.train(False)
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_ = model.to_torchscript()
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assert not model.training
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assert not script.training
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@pytest.mark.parametrize("modelclass", [
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EvalModelTemplate,
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ParityModuleRNN,
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BasicGAN,
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])
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def test_torchscript_properties(modelclass):
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""" Test that scripted LightningModule has unnecessary methods removed. """
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model = modelclass()
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model.datamodule = TrialMNISTDataModule()
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script = model.to_torchscript()
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assert not hasattr(script, "datamodule")
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assert not hasattr(model, "batch_size") or hasattr(script, "batch_size")
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assert not hasattr(model, "learning_rate") or hasattr(script, "learning_rate")
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if LooseVersion(torch.__version__) >= LooseVersion("1.4.0"):
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# only on torch >= 1.4 do these unused methods get removed
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assert not callable(getattr(script, "training_step", None))
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@pytest.mark.parametrize("modelclass", [
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EvalModelTemplate,
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ParityModuleRNN,
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BasicGAN,
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])
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@pytest.mark.skipif(
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LooseVersion(torch.__version__) < LooseVersion("1.5.0"),
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reason="torch.save/load has bug loading script modules on torch <= 1.4",
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)
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def test_torchscript_save_load(tmpdir, modelclass):
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""" Test that scripted LightningModules is correctly saved and can be loaded. """
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model = modelclass()
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output_file = str(tmpdir / "model.pt")
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script = model.to_torchscript(file_path=output_file)
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loaded_script = torch.jit.load(output_file)
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assert torch.allclose(next(script.parameters()), next(loaded_script.parameters()))
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