376 lines
11 KiB
Python
376 lines
11 KiB
Python
import logging
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import os
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import torch
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import tests.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint
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from pytorch_lightning.testing import LightningTestModel
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def test_running_test_pretrained_model_ddp(tmpdir):
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"""Verify `test()` on pretrained model."""
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if not tutils.can_run_gpu_test():
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return
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tutils.reset_seed()
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tutils.set_random_master_port()
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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# exp file to get meta
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logger = tutils.get_test_tube_logger(tmpdir, False)
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# exp file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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trainer_options = dict(
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show_progress_bar=False,
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max_epochs=1,
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train_percent_check=0.4,
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val_percent_check=0.2,
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checkpoint_callback=checkpoint,
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logger=logger,
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gpus=[0, 1],
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distributed_backend='ddp'
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)
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# fit model
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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logging.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir)))
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# correct result and ok accuracy
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assert result == 1, 'training failed to complete'
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pretrained_model = tutils.load_model(logger,
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trainer.checkpoint_callback.filepath,
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module_class=LightningTestModel)
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# run test set
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new_trainer = Trainer(**trainer_options)
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new_trainer.test(pretrained_model)
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for dataloader in model.test_dataloader():
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tutils.run_prediction(dataloader, pretrained_model)
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def test_running_test_pretrained_model(tmpdir):
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tutils.reset_seed()
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"""Verify test() on pretrained model"""
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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# logger file to get meta
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logger = tutils.get_test_tube_logger(tmpdir, False)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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trainer_options = dict(
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show_progress_bar=False,
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max_epochs=4,
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train_percent_check=0.4,
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val_percent_check=0.2,
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checkpoint_callback=checkpoint,
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logger=logger
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)
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# fit model
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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# correct result and ok accuracy
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assert result == 1, 'training failed to complete'
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pretrained_model = tutils.load_model(
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logger, trainer.checkpoint_callback.filepath, module_class=LightningTestModel
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)
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new_trainer = Trainer(**trainer_options)
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new_trainer.test(pretrained_model)
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# test we have good test accuracy
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tutils.assert_ok_test_acc(new_trainer)
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def test_load_model_from_checkpoint(tmpdir):
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tutils.reset_seed()
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"""Verify test() on pretrained model"""
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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trainer_options = dict(
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show_progress_bar=False,
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max_epochs=2,
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train_percent_check=0.4,
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val_percent_check=0.2,
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checkpoint_callback=ModelCheckpoint(tmpdir, save_top_k=-1),
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logger=False,
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default_save_path=tmpdir,
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)
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# fit model
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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# correct result and ok accuracy
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assert result == 1, 'training failed to complete'
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# load last checkpoint
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last_checkpoint = os.path.join(trainer.checkpoint_callback.filepath, "_ckpt_epoch_1.ckpt")
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if not os.path.isfile(last_checkpoint):
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last_checkpoint = os.path.join(trainer.checkpoint_callback.filepath, "_ckpt_epoch_0.ckpt")
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pretrained_model = LightningTestModel.load_from_checkpoint(last_checkpoint)
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# test that hparams loaded correctly
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for k, v in vars(hparams).items():
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assert getattr(pretrained_model.hparams, k) == v
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new_trainer = Trainer(**trainer_options)
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new_trainer.test(pretrained_model)
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# test we have good test accuracy
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tutils.assert_ok_test_acc(new_trainer)
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def test_running_test_pretrained_model_dp(tmpdir):
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tutils.reset_seed()
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"""Verify test() on pretrained model"""
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if not tutils.can_run_gpu_test():
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return
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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# logger file to get meta
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logger = tutils.get_test_tube_logger(tmpdir, False)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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trainer_options = dict(
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show_progress_bar=True,
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max_epochs=4,
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train_percent_check=0.4,
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val_percent_check=0.2,
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checkpoint_callback=checkpoint,
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logger=logger,
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gpus=[0, 1],
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distributed_backend='dp'
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)
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# fit model
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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# correct result and ok accuracy
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assert result == 1, 'training failed to complete'
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pretrained_model = tutils.load_model(logger,
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trainer.checkpoint_callback.filepath,
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module_class=LightningTestModel)
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new_trainer = Trainer(**trainer_options)
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new_trainer.test(pretrained_model)
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# test we have good test accuracy
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tutils.assert_ok_test_acc(new_trainer)
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def test_dp_resume(tmpdir):
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"""Make sure DP continues training correctly."""
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if not tutils.can_run_gpu_test():
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return
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tutils.reset_seed()
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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trainer_options = dict(
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show_progress_bar=True,
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max_epochs=2,
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gpus=2,
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distributed_backend='dp',
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)
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# get logger
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logger = tutils.get_test_tube_logger(tmpdir, debug=False)
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# exp file to get weights
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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# add these to the trainer options
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trainer_options['logger'] = logger
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trainer_options['checkpoint_callback'] = checkpoint
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# fit model
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trainer = Trainer(**trainer_options)
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trainer.is_slurm_managing_tasks = True
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result = trainer.fit(model)
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# track epoch before saving
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real_global_epoch = trainer.current_epoch
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# correct result and ok accuracy
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assert result == 1, 'amp + dp model failed to complete'
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# ---------------------------
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# HPC LOAD/SAVE
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# ---------------------------
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# save
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trainer.hpc_save(tmpdir, logger)
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# init new trainer
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new_logger = tutils.get_test_tube_logger(tmpdir, version=logger.version)
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trainer_options['logger'] = new_logger
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trainer_options['checkpoint_callback'] = ModelCheckpoint(tmpdir)
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trainer_options['train_percent_check'] = 0.2
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trainer_options['val_percent_check'] = 0.2
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trainer_options['max_epochs'] = 1
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new_trainer = Trainer(**trainer_options)
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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 assert_good_acc():
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assert new_trainer.current_epoch == real_global_epoch and new_trainer.current_epoch > 0
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# if model and state loaded correctly, predictions will be good even though we
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# haven't trained with the new loaded model
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dp_model = new_trainer.model
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dp_model.eval()
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dataloader = trainer.get_train_dataloader()
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tutils.run_prediction(dataloader, dp_model, dp=True)
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# new model
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model = LightningTestModel(hparams)
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model.on_train_start = assert_good_acc
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# fit new model which should load hpc weights
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new_trainer.fit(model)
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# test freeze on gpu
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model.freeze()
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model.unfreeze()
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def test_cpu_restore_training(tmpdir):
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"""Verify continue training session on CPU."""
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tutils.reset_seed()
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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# logger file to get meta
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test_logger_version = 10
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logger = tutils.get_test_tube_logger(tmpdir, False, version=test_logger_version)
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trainer_options = dict(
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max_epochs=8,
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val_check_interval=0.50,
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val_percent_check=0.2,
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train_percent_check=0.2,
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logger=logger,
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checkpoint_callback=ModelCheckpoint(tmpdir, save_top_k=-1)
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)
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# fit model
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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real_global_epoch = trainer.current_epoch
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# traning complete
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assert result == 1, 'amp + ddp model failed to complete'
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# wipe-out trainer and model
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# retrain with not much data... this simulates picking training back up after slurm
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# we want to see if the weights come back correctly
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new_logger = tutils.get_test_tube_logger(tmpdir, False, version=test_logger_version)
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trainer_options = dict(
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max_epochs=2,
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val_check_interval=0.50,
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val_percent_check=0.2,
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train_percent_check=0.2,
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logger=new_logger,
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checkpoint_callback=ModelCheckpoint(tmpdir),
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)
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trainer = Trainer(**trainer_options)
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model = LightningTestModel(hparams)
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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 assert_good_acc():
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assert trainer.current_epoch == real_global_epoch
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assert trainer.current_epoch >= 0
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# if model and state loaded correctly, predictions will be good even though we
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# haven't trained with the new loaded model
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trainer.model.eval()
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for dataloader in trainer.get_val_dataloaders():
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tutils.run_prediction(dataloader, trainer.model)
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model.on_train_start = assert_good_acc
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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_model_saving_loading(tmpdir):
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"""Tests use case where trainer saves the model, and user loads it from tags independently."""
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tutils.reset_seed()
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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# logger file to get meta
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logger = tutils.get_test_tube_logger(tmpdir, False)
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trainer_options = dict(
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max_epochs=1,
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logger=logger,
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checkpoint_callback=ModelCheckpoint(tmpdir)
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)
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# fit model
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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# traning complete
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assert result == 1, 'amp + ddp model failed to complete'
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# make a prediction
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for dataloader in model.test_dataloader():
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for batch in dataloader:
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break
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x, y = batch
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x = x.view(x.size(0), -1)
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# generate preds before saving model
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model.eval()
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pred_before_saving = model(x)
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# save model
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new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
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trainer.save_checkpoint(new_weights_path)
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# load new model
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tags_path = tutils.get_data_path(logger, path_dir=tmpdir)
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tags_path = os.path.join(tags_path, 'meta_tags.csv')
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model_2 = LightningTestModel.load_from_metrics(weights_path=new_weights_path,
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tags_csv=tags_path)
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model_2.eval()
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# make prediction
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# assert that both predictions are the same
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new_pred = model_2(x)
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assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
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# if __name__ == '__main__':
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# pytest.main([__file__])
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