122 lines
3.5 KiB
Python
122 lines
3.5 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 os
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from tests.base.boring_model import BoringModel
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from pytorch_lightning import Trainer
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def test_logdir(tmpdir):
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"""
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Tests that the path is correct when checkpoint and loggers are used
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"""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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expected = os.path.join(self.trainer.default_root_dir, 'lightning_logs', 'version_0')
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assert self.trainer.log_dir == expected
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return {"loss": loss}
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model = TestModel()
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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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)
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trainer.fit(model)
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def test_logdir_no_checkpoint_cb(tmpdir):
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"""
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Tests that the path is correct with no checkpoint
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"""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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expected = os.path.join(self.trainer.default_root_dir, 'lightning_logs', 'version_0')
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assert self.trainer.log_dir == expected
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return {"loss": loss}
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model = TestModel()
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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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checkpoint_callback=False
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)
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trainer.fit(model)
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def test_logdir_no_logger(tmpdir):
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"""
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Tests that the path is correct even when there is no logger
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"""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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expected = os.path.join(self.trainer.default_root_dir)
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assert self.trainer.log_dir == expected
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return {"loss": loss}
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model = TestModel()
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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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logger=False,
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)
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trainer.fit(model)
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def test_logdir_no_logger_no_checkpoint(tmpdir):
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"""
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Tests that the path is correct even when there is no logger
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"""
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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expected = os.path.join(self.trainer.default_root_dir)
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assert self.trainer.log_dir == expected
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return {"loss": loss}
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model = TestModel()
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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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logger=False,
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checkpoint_callback=False
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)
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trainer.fit(model)
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