104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
# Copyright The Lightning AI 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 logging
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import pytest
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from lightning.pytorch import Trainer
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from lightning.pytorch.demos.boring_classes import BoringModel
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from lightning.pytorch.trainer.states import TrainerFn
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def test_num_dataloader_batches(tmpdir):
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"""Tests that the correct number of batches are allocated."""
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# when we have fewer batches in the dataloader we should use those instead of the limit
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model = BoringModel()
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trainer = Trainer(limit_val_batches=100, limit_train_batches=100, max_epochs=1, default_root_dir=tmpdir)
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trainer.fit(model)
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assert len(model.train_dataloader()) == 64
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assert len(model.val_dataloader()) == 64
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assert isinstance(trainer.num_val_batches, list)
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assert trainer.num_val_batches[0] == 64
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assert trainer.num_training_batches == 64
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# when we have more batches in the dataloader we should limit them
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model = BoringModel()
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trainer = Trainer(limit_val_batches=7, limit_train_batches=7, max_epochs=1, default_root_dir=tmpdir)
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trainer.fit(model)
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assert len(model.train_dataloader()) == 64
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assert len(model.val_dataloader()) == 64
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assert isinstance(trainer.num_val_batches, list)
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assert trainer.num_val_batches[0] == 7
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assert trainer.num_training_batches == 7
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@pytest.mark.parametrize(
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"mode",
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[
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"val",
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"test",
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"predict",
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],
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)
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@pytest.mark.parametrize("limit_batches", [0.1, 10])
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def test_eval_limit_batches(mode, limit_batches):
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limit_eval_batches = f"limit_{mode}_batches"
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dl_hook = f"{mode}_dataloader"
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model = BoringModel()
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eval_loader = getattr(model, dl_hook)()
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trainer = Trainer(**{limit_eval_batches: limit_batches})
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model.trainer = trainer
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trainer.strategy.connect(model)
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trainer._data_connector.attach_dataloaders(model)
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if mode == "val":
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trainer.validate_loop.setup_data()
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trainer.state.fn = TrainerFn.VALIDATING
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loader_num_batches = trainer.num_val_batches
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dataloaders = trainer.val_dataloaders
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elif mode == "test":
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trainer.test_loop.setup_data()
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loader_num_batches = trainer.num_test_batches
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dataloaders = trainer.test_dataloaders
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elif mode == "predict":
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trainer.predict_loop.setup_data()
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loader_num_batches = trainer.num_predict_batches
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dataloaders = trainer.predict_dataloaders
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expected_batches = int(limit_batches * len(eval_loader)) if isinstance(limit_batches, float) else limit_batches
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assert loader_num_batches[0] == expected_batches
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assert len(dataloaders) == len(eval_loader)
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@pytest.mark.parametrize(
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"argument",
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["limit_train_batches", "limit_val_batches", "limit_test_batches", "limit_predict_batches", "overfit_batches"],
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)
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@pytest.mark.parametrize("value", [1, 1.0])
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def test_limit_batches_info_message(caplog, argument, value):
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with caplog.at_level(logging.INFO):
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Trainer(**{argument: value})
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assert f"`Trainer({argument}={value})` was configured" in caplog.text
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message = f"configured so {'1' if isinstance(value, int) else '100%'}"
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assert message in caplog.text
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caplog.clear()
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# the message should not appear by default
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with caplog.at_level(logging.INFO):
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Trainer()
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assert message not in caplog.text
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