83 lines
2.3 KiB
Python
83 lines
2.3 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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import pytest
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from pytorch_lightning import Trainer
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from pytorch_lightning.demos.boring_classes import BoringModel
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from tests_pytorch.helpers.runif import RunIf
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class TrainerGetModel(BoringModel):
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def on_fit_start(self):
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assert self == self.trainer.lightning_module
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def on_fit_end(self):
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assert self == self.trainer.lightning_module
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def test_get_model(tmpdir):
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"""Tests that `trainer.lightning_module` extracts the model correctly."""
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model = TrainerGetModel()
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limit_train_batches = 2
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trainer = Trainer(
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default_root_dir=tmpdir, limit_train_batches=limit_train_batches, limit_val_batches=2, max_epochs=1
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)
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trainer.fit(model)
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@RunIf(skip_windows=True)
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def test_get_model_ddp_cpu(tmpdir):
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"""Tests that `trainer.lightning_module` extracts the model correctly when using ddp on cpu."""
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model = TrainerGetModel()
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limit_train_batches = 2
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=limit_train_batches,
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limit_val_batches=2,
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max_epochs=1,
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accelerator="cpu",
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devices=2,
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strategy="ddp_spawn",
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)
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trainer.fit(model)
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@pytest.mark.parametrize(
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"accelerator",
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[
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pytest.param("gpu", marks=RunIf(min_cuda_gpus=1)),
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pytest.param("mps", marks=RunIf(mps=True)),
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],
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)
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def test_get_model_gpu(tmpdir, accelerator):
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"""Tests that `trainer.lightning_module` extracts the model correctly when using GPU."""
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model = TrainerGetModel()
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limit_train_batches = 2
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=limit_train_batches,
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limit_val_batches=2,
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max_epochs=1,
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accelerator=accelerator,
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devices=1,
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)
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trainer.fit(model)
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