87 lines
2.9 KiB
Python
87 lines
2.9 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 copy import deepcopy
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import torch
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import pytorch_lightning as pl
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from pytorch_lightning import seed_everything, Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint
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from tests.base import BoringModel
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def test_finetuning_with_resume_from_checkpoint(tmpdir):
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"""
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This test validates that generated ModelCheckpoint is pointing to the right best_model_path during test
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"""
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seed_everything(3)
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checkpoint_callback = ModelCheckpoint(monitor='val_loss', dirpath=tmpdir, filename="{epoch:02d}", save_top_k=-1)
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class ExtendedBoringModel(BoringModel):
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def configure_optimizers(self):
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optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.001)
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lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
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return [optimizer], [lr_scheduler]
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def validation_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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self.log("val_loss", loss, on_epoch=True, prog_bar=True)
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model = ExtendedBoringModel()
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model.validation_epoch_end = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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limit_train_batches=12,
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limit_val_batches=6,
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limit_test_batches=12,
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callbacks=[checkpoint_callback],
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logger=False,
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)
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trainer.fit(model)
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assert os.listdir(tmpdir) == ['epoch=00.ckpt']
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best_model_paths = [checkpoint_callback.best_model_path]
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results = []
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for idx in range(3, 6):
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# load from checkpoint
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trainer = pl.Trainer(
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default_root_dir=tmpdir,
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max_epochs=idx,
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limit_train_batches=12,
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limit_val_batches=12,
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limit_test_batches=12,
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resume_from_checkpoint=best_model_paths[-1],
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progress_bar_refresh_rate=0,
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)
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trainer.fit(model)
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trainer.test()
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results.append(deepcopy(trainer.callback_metrics))
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best_model_paths.append(trainer.checkpoint_callback.best_model_path)
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for idx in range(len(results) - 1):
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assert results[idx]["val_loss"] > results[idx + 1]["val_loss"]
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for idx, best_model_path in enumerate(best_model_paths):
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if idx == 0:
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assert best_model_path.endswith(f"epoch=0{idx}.ckpt")
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else:
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assert f"epoch={idx + 1}" in best_model_path
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