140 lines
5.2 KiB
Python
140 lines
5.2 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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import torch
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from torch.utils.data import DataLoader, DistributedSampler, RandomSampler, Sampler, SequentialSampler
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from legacy.simple_classif_training import ClassifDataModule, ClassificationModel
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from pytorch_lightning import Trainer
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from pytorch_lightning.trainer.states import RunningStage
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from tests.helpers.boring_model import BoringModel, RandomDataset
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from tests.helpers.runif import RunIf
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@pytest.mark.parametrize("overfit_batches", [1, 2, 0.1, 0.25, 1.0])
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def test_overfit_basic(tmpdir, overfit_batches):
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"""Tests that only training_step can be used when overfitting."""
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model = BoringModel()
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model.validation_step = None
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total_train_samples = len(BoringModel().train_dataloader())
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trainer = Trainer(
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default_root_dir=tmpdir, max_epochs=1, overfit_batches=overfit_batches, enable_model_summary=False
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)
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trainer.fit(model)
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assert trainer.num_val_batches == []
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assert trainer.num_training_batches == int(
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overfit_batches * (1 if isinstance(overfit_batches, int) else total_train_samples)
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)
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def test_overfit_batches_raises_warning_in_case_of_sequential_sampler(tmpdir):
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class NonSequentialSampler(Sampler):
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def __init__(self, data_source):
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self.data_source = data_source
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def __iter__(self):
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return iter(range(len(self.data_source)))
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def __len__(self):
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return len(self.data_source)
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class TestModel(BoringModel):
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def train_dataloader(self):
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dataset = RandomDataset(32, 64)
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sampler = NonSequentialSampler(dataset)
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return torch.utils.data.DataLoader(dataset, sampler=sampler)
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, overfit_batches=2)
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with pytest.warns(UserWarning, match="requested to overfit but enabled training dataloader shuffling"):
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trainer.fit(model)
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assert isinstance(trainer.train_dataloader.loaders.sampler, SequentialSampler)
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@pytest.mark.parametrize(
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"stage,mode",
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[(RunningStage.VALIDATING, "val"), (RunningStage.TESTING, "test"), (RunningStage.PREDICTING, "predict")],
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)
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@pytest.mark.parametrize("overfit_batches", [0.11, 4])
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def test_overfit_batch_limits_eval(stage, mode, overfit_batches):
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model = ClassificationModel()
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dm = ClassifDataModule()
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eval_loader = getattr(dm, f"{mode}_dataloader")()
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trainer = Trainer(overfit_batches=overfit_batches)
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model.trainer = trainer
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trainer._data_connector.attach_datamodule(model, datamodule=dm)
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loader_num_batches, dataloaders = trainer._reset_eval_dataloader(stage, model=model)
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if stage == RunningStage.VALIDATING:
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assert loader_num_batches[0] == 0
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else:
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assert loader_num_batches[0] == len(eval_loader)
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assert isinstance(dataloaders[0].sampler, SequentialSampler)
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@pytest.mark.parametrize("overfit_batches", [0.11, 4])
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def test_overfit_batch_limits_train(overfit_batches):
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model = ClassificationModel()
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dm = ClassifDataModule()
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# original train loader which should be replaced in all methods
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train_loader = dm.train_dataloader()
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assert isinstance(train_loader.sampler, RandomSampler)
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# Create a reference train dataloader without shuffling.
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train_loader = DataLoader(dm.train_dataloader().dataset, shuffle=False)
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(xa, ya) = next(iter(train_loader))
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train_loader = DataLoader(dm.train_dataloader().dataset, shuffle=True)
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full_train_samples = len(train_loader)
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# set the model loaders
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model.train_dataloader = lambda: train_loader
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# test train loader applies correct limits
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trainer = Trainer(overfit_batches=overfit_batches)
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model.trainer = trainer
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trainer._data_connector.attach_dataloaders(model=model)
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trainer.reset_train_dataloader(model)
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expected_batches = (
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int(overfit_batches * full_train_samples) if isinstance(overfit_batches, float) else overfit_batches
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)
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assert trainer.num_training_batches == expected_batches
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# make sure the loaders are the same
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(xb, yb) = next(iter(trainer.train_dataloader))
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assert torch.eq(xa, xb).all()
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assert torch.eq(ya, yb).all()
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@RunIf(skip_windows=True)
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def test_distributed_sampler_with_overfit_batches():
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model = BoringModel()
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trainer = Trainer(
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overfit_batches=1,
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strategy="ddp_spawn",
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num_processes=2,
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)
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model.trainer = trainer
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trainer.model = model
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trainer._data_connector.attach_dataloaders(model)
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trainer.reset_train_dataloader()
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train_sampler = trainer.train_dataloader.loaders.sampler
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assert isinstance(train_sampler, DistributedSampler)
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assert train_sampler.shuffle is False
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