400 lines
12 KiB
Python
400 lines
12 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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import platform
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from distutils.version import LooseVersion
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import pytest
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import torch
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import tests.helpers.pipelines as tpipes
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import tests.helpers.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import Callback, EarlyStopping, ModelCheckpoint
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from pytorch_lightning.trainer.states import TrainerState
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from tests.helpers import BoringModel
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from tests.helpers.datamodules import ClassifDataModule
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from tests.helpers.simple_models import ClassificationModel
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def test_cpu_slurm_save_load(tmpdir):
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"""Verify model save/load/checkpoint on CPU."""
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model = BoringModel()
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# logger file to get meta
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logger = tutils.get_default_logger(tmpdir)
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version = logger.version
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# fit model
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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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logger=logger,
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limit_train_batches=0.2,
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limit_val_batches=0.2,
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callbacks=[ModelCheckpoint(dirpath=tmpdir)],
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)
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trainer.fit(model)
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real_global_step = trainer.global_step
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# traning complete
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assert trainer.state == TrainerState.FINISHED, 'cpu model failed to complete'
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# predict with trained model before saving
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# make a prediction
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dataloaders = model.test_dataloader()
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if not isinstance(dataloaders, list):
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dataloaders = [dataloaders]
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for dataloader in dataloaders:
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for batch in dataloader:
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break
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model.eval()
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pred_before_saving = model(batch)
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# test HPC saving
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# simulate snapshot on slurm
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saved_filepath = trainer.checkpoint_connector.hpc_save(trainer.weights_save_path, logger)
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assert os.path.exists(saved_filepath)
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# new logger file to get meta
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logger = tutils.get_default_logger(tmpdir, version=version)
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model = BoringModel()
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class _StartCallback(Callback):
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# set the epoch start hook so we can predict before the model does the full training
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def on_train_epoch_start(self, trainer, model):
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assert trainer.global_step == real_global_step and trainer.global_step > 0
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# predict with loaded model to make sure answers are the same
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mode = model.training
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model.eval()
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new_pred = model(batch)
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assert torch.eq(pred_before_saving, new_pred).all()
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model.train(mode)
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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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logger=logger,
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callbacks=[_StartCallback(), ModelCheckpoint(dirpath=tmpdir)],
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)
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# by calling fit again, we trigger training, loading weights from the cluster
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# and our hook to predict using current model before any more weight updates
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trainer.fit(model)
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def test_early_stopping_cpu_model(tmpdir):
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class ModelTrainVal(BoringModel):
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def validation_step(self, *args, **kwargs):
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output = super().validation_step(*args, **kwargs)
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self.log('val_loss', output['x'])
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return output
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tutils.reset_seed()
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stopping = EarlyStopping(monitor="val_loss", min_delta=0.1)
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trainer_options = dict(
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callbacks=[stopping],
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default_root_dir=tmpdir,
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gradient_clip_val=1.0,
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overfit_batches=0.20,
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track_grad_norm=2,
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progress_bar_refresh_rate=0,
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accumulate_grad_batches=2,
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limit_train_batches=0.1,
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limit_val_batches=0.1,
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)
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model = ModelTrainVal()
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tpipes.run_model_test(trainer_options, model, on_gpu=False)
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# test freeze on cpu
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model.freeze()
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model.unfreeze()
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@pytest.mark.skipif(platform.system() == "Windows", reason="Distributed training is not supported on Windows")
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@pytest.mark.skipif((platform.system() == "Darwin" and LooseVersion(torch.__version__) < LooseVersion("1.3.0")),
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reason="Distributed training is not supported on MacOS before Torch 1.3.0")
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def test_multi_cpu_model_ddp(tmpdir):
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"""Make sure DDP works."""
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tutils.set_random_master_port()
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trainer_options = dict(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=1,
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limit_train_batches=0.4,
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limit_val_batches=0.2,
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gpus=None,
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num_processes=2,
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accelerator='ddp_cpu',
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)
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dm = ClassifDataModule()
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model = ClassificationModel()
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tpipes.run_model_test(trainer_options, model, data=dm, on_gpu=False)
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def test_lbfgs_cpu_model(tmpdir):
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"""Test each of the trainer options. Testing LBFGS optimizer"""
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class ModelSpecifiedOptimizer(BoringModel):
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def __init__(self, optimizer_name, learning_rate):
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super().__init__()
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self.optimizer_name = optimizer_name
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self.learning_rate = learning_rate
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self.save_hyperparameters()
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trainer_options = dict(
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default_root_dir=tmpdir,
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max_epochs=1,
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progress_bar_refresh_rate=0,
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weights_summary="top",
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limit_train_batches=0.2,
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limit_val_batches=0.2,
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)
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model = ModelSpecifiedOptimizer(optimizer_name="LBFGS", learning_rate=0.004)
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tpipes.run_model_test_without_loggers(trainer_options, model, min_acc=0.01)
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def test_default_logger_callbacks_cpu_model(tmpdir):
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"""Test each of the trainer options."""
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trainer_options = dict(
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default_root_dir=tmpdir,
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max_epochs=1,
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gradient_clip_val=1.0,
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overfit_batches=0.20,
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progress_bar_refresh_rate=0,
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limit_train_batches=0.01,
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limit_val_batches=0.01,
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)
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model = BoringModel()
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tpipes.run_model_test_without_loggers(trainer_options, model, min_acc=0.01)
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# test freeze on cpu
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model.freeze()
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model.unfreeze()
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def test_running_test_after_fitting(tmpdir):
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"""Verify test() on fitted model."""
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class ModelTrainValTest(BoringModel):
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def validation_step(self, *args, **kwargs):
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output = super().validation_step(*args, **kwargs)
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self.log('val_loss', output['x'])
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return output
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def test_step(self, *args, **kwargs):
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output = super().test_step(*args, **kwargs)
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self.log('test_loss', output['y'])
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return output
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model = ModelTrainValTest()
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# logger file to get meta
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logger = tutils.get_default_logger(tmpdir)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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# fit model
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trainer = Trainer(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=2,
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limit_train_batches=0.4,
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limit_val_batches=0.2,
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limit_test_batches=0.2,
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callbacks=[checkpoint],
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logger=logger,
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)
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trainer.fit(model)
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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trainer.test()
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# test we have good test accuracy
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tutils.assert_ok_model_acc(trainer, key='test_loss', thr=0.5)
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def test_running_test_no_val(tmpdir):
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"""Verify `test()` works on a model with no `val_dataloader`. It performs
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train and test only"""
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class ModelTrainTest(BoringModel):
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def val_dataloader(self):
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pass
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def test_step(self, *args, **kwargs):
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output = super().test_step(*args, **kwargs)
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self.log('test_loss', output['y'])
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return output
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model = ModelTrainTest()
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# logger file to get meta
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logger = tutils.get_default_logger(tmpdir)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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# fit model
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trainer = Trainer(
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default_root_dir=tmpdir,
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progress_bar_refresh_rate=0,
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max_epochs=1,
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limit_train_batches=0.4,
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limit_val_batches=0.2,
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limit_test_batches=0.2,
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callbacks=[checkpoint],
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logger=logger,
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)
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trainer.fit(model)
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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trainer.test()
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# test we have good test accuracy
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tutils.assert_ok_model_acc(trainer, key='test_loss')
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def test_simple_cpu(tmpdir):
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"""Verify continue training session on CPU."""
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model = BoringModel()
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# fit model
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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_val_batches=0.1,
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limit_train_batches=20,
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)
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trainer.fit(model)
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# traning complete
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assert trainer.state == TrainerState.FINISHED, 'amp + ddp model failed to complete'
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def test_cpu_model(tmpdir):
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"""Make sure model trains on CPU."""
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trainer_options = dict(
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default_root_dir=tmpdir, progress_bar_refresh_rate=0, max_epochs=1, limit_train_batches=4, limit_val_batches=4
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)
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, on_gpu=False)
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def test_all_features_cpu_model(tmpdir):
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"""Test each of the trainer options."""
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trainer_options = dict(
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default_root_dir=tmpdir,
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gradient_clip_val=1.0,
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overfit_batches=0.20,
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track_grad_norm=2,
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progress_bar_refresh_rate=0,
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accumulate_grad_batches=2,
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max_epochs=1,
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limit_train_batches=0.4,
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limit_val_batches=0.4,
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)
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model, on_gpu=False, min_acc=0.01)
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def test_tbptt_cpu_model(tmpdir):
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"""Test truncated back propagation through time works."""
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truncated_bptt_steps = 2
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sequence_size = 30
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batch_size = 30
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x_seq = torch.rand(batch_size, sequence_size, 1)
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y_seq_list = torch.rand(batch_size, sequence_size, 1).tolist()
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class MockSeq2SeqDataset(torch.utils.data.Dataset):
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def __getitem__(self, i):
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return x_seq, y_seq_list
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def __len__(self):
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return 1
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class BpttTestModel(BoringModel):
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def __init__(self, batch_size, in_features, out_features, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.test_hidden = None
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self.batch_size = batch_size
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self.layer = torch.nn.Linear(in_features, out_features)
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def training_step(self, batch, batch_idx, hiddens):
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assert hiddens == self.test_hidden, "Hidden state not persistent between tbptt steps"
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self.test_hidden = torch.rand(1)
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x_tensor, y_list = batch
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assert x_tensor.shape[1] == truncated_bptt_steps, "tbptt split Tensor failed"
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y_tensor = torch.tensor(y_list, dtype=x_tensor.dtype)
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assert y_tensor.shape[1] == truncated_bptt_steps, "tbptt split list failed"
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pred = self(x_tensor.view(batch_size, truncated_bptt_steps))
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loss_val = torch.nn.functional.mse_loss(pred, y_tensor.view(batch_size, truncated_bptt_steps))
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return {
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"loss": loss_val,
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"hiddens": self.test_hidden,
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}
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def training_epoch_end(self, training_step_outputs):
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training_step_outputs = training_step_outputs[0]
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assert len(training_step_outputs) == (sequence_size / truncated_bptt_steps)
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loss = torch.stack([x["loss"] for x in training_step_outputs]).mean()
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self.log("train_loss", loss)
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def train_dataloader(self):
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return torch.utils.data.DataLoader(
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dataset=MockSeq2SeqDataset(),
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batch_size=batch_size,
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shuffle=False,
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sampler=None,
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)
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model = BpttTestModel(batch_size=batch_size, in_features=truncated_bptt_steps, out_features=truncated_bptt_steps)
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model.example_input_array = torch.randn(5, truncated_bptt_steps)
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# fit model
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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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truncated_bptt_steps=truncated_bptt_steps,
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limit_val_batches=0,
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weights_summary=None,
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)
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trainer.fit(model)
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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