402 lines
13 KiB
Python
402 lines
13 KiB
Python
import glob
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import logging as log
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import os
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import pickle
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import functools
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import cloudpickle
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import pytest
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import torch
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import tests.base.develop_pipelines as tpipes
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import tests.base.develop_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 tests.base import EvalModelTemplate, GenericEvalModelTemplate
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_running_test_pretrained_model_distrib_dp(tmpdir):
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"""Verify `test()` on pretrained model."""
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tutils.set_random_master_port()
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model = EvalModelTemplate()
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# exp file to get meta
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logger = tutils.get_default_logger(tmpdir)
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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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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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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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default_root_dir=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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pretrained_model = EvalModelTemplate.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)
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# run test set
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new_trainer = Trainer(**trainer_options)
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results = new_trainer.test(pretrained_model)
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pretrained_model.cpu()
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# test we have good test accuracy
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acc = results[0]['test_acc']
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assert acc > 0.5, f"Model failed to get expected {0.5} accuracy. test_acc = {acc}"
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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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tpipes.run_prediction(dataloader, pretrained_model)
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_running_test_pretrained_model_distrib_ddp_spawn(tmpdir):
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"""Verify `test()` on pretrained model."""
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tutils.set_random_master_port()
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model = EvalModelTemplate()
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# exp file to get meta
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logger = tutils.get_default_logger(tmpdir)
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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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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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checkpoint_callback=checkpoint,
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logger=logger,
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gpus=[0, 1],
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distributed_backend='ddp_spawn',
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default_root_dir=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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log.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 = EvalModelTemplate.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)
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# run test set
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new_trainer = Trainer(**trainer_options)
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results = new_trainer.test(pretrained_model)
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pretrained_model.cpu()
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acc = results[0]['test_acc']
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assert acc > 0.5, f"Model failed to get expected {0.5} accuracy. test_acc = {acc}"
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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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tpipes.run_prediction(dataloader, pretrained_model)
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def test_running_test_pretrained_model_cpu(tmpdir):
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"""Verify test() on pretrained model."""
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model = EvalModelTemplate()
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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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trainer_options = dict(
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progress_bar_refresh_rate=0,
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max_epochs=3,
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limit_train_batches=0.4,
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limit_val_batches=0.2,
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checkpoint_callback=checkpoint,
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logger=logger,
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default_root_dir=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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pretrained_model = EvalModelTemplate.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)
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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_model_acc(new_trainer)
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@pytest.mark.parametrize('model_template', [EvalModelTemplate, GenericEvalModelTemplate])
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def test_load_model_from_checkpoint(tmpdir, model_template):
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"""Verify test() on pretrained model."""
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hparams = model_template.get_default_hparams()
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model = model_template(**hparams)
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trainer_options = dict(
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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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checkpoint_callback=ModelCheckpoint(tmpdir, save_top_k=-1),
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default_root_dir=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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trainer.test(ckpt_path=None)
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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 = sorted(glob.glob(os.path.join(trainer.checkpoint_callback.dirpath, "*.ckpt")))[-1]
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pretrained_model = model_template.load_from_checkpoint(last_checkpoint)
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# test that hparams loaded correctly
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for k, v in hparams.items():
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assert getattr(pretrained_model, k) == v
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# assert weights are the same
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for (old_name, old_p), (new_name, new_p) in zip(model.named_parameters(), pretrained_model.named_parameters()):
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assert torch.all(torch.eq(old_p, new_p)), 'loaded weights are not the same as the saved weights'
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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_model_acc(new_trainer)
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_dp_resume(tmpdir):
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"""Make sure DP continues training correctly."""
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hparams = EvalModelTemplate.get_default_hparams()
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model = EvalModelTemplate(**hparams)
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trainer_options = dict(max_epochs=1, gpus=2, distributed_backend='dp', default_root_dir=tmpdir,)
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# get logger
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logger = tutils.get_default_logger(tmpdir)
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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. Increment since we finished the current epoch, don't want to rerun
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real_global_epoch = trainer.current_epoch + 1
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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.checkpoint_connector.hpc_save(tmpdir, logger)
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# init new trainer
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new_logger = tutils.get_default_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['limit_train_batches'] = 0.5
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trainer_options['limit_val_batches'] = 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.train_dataloader
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tpipes.run_prediction(dataloader, dp_model, dp=True)
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# new model
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model = EvalModelTemplate(**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_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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model = EvalModelTemplate()
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# logger file to get meta
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logger = tutils.get_default_logger(tmpdir)
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# fit model
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trainer = Trainer(
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max_epochs=1, logger=logger, checkpoint_callback=ModelCheckpoint(tmpdir), default_root_dir=tmpdir,
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)
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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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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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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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hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
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hparams_path = os.path.join(hparams_path, 'hparams.yaml')
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model_2 = EvalModelTemplate.load_from_checkpoint(checkpoint_path=new_weights_path, hparams_file=hparams_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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@pytest.mark.parametrize('url_ckpt', [True, False])
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def test_strict_model_load_more_params(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
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"""Tests use case where trainer saves the model, and user loads it from tags independently."""
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# set $TORCH_HOME, which determines torch hub's cache path, to tmpdir
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monkeypatch.setenv('TORCH_HOME', tmpdir)
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model = EvalModelTemplate()
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# Extra layer
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model.c_d3 = torch.nn.Linear(model.hidden_dim, model.hidden_dim)
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# logger file to get meta
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logger = tutils.get_default_logger(tmpdir)
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# fit model
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trainer = Trainer(
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default_root_dir=tmpdir, max_epochs=1, logger=logger, checkpoint_callback=ModelCheckpoint(tmpdir),
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)
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result = trainer.fit(model)
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# traning complete
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assert result == 1
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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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hparams_path = os.path.join(tutils.get_data_path(logger, path_dir=tmpdir), 'hparams.yaml')
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hparams_url = f'http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}'
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ckpt_path = hparams_url if url_ckpt else new_weights_path
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EvalModelTemplate.load_from_checkpoint(
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checkpoint_path=ckpt_path, hparams_file=hparams_path, strict=False,
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)
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with pytest.raises(RuntimeError, match=r'Unexpected key\(s\) in state_dict: "c_d3.weight", "c_d3.bias"'):
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EvalModelTemplate.load_from_checkpoint(
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checkpoint_path=ckpt_path, hparams_file=hparams_path, strict=True,
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)
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@pytest.mark.parametrize('url_ckpt', [True, False])
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def test_strict_model_load_less_params(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
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"""Tests use case where trainer saves the model, and user loads it from tags independently."""
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# set $TORCH_HOME, which determines torch hub's cache path, to tmpdir
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monkeypatch.setenv('TORCH_HOME', tmpdir)
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model = EvalModelTemplate()
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# logger file to get meta
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logger = tutils.get_default_logger(tmpdir)
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# fit model
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trainer = Trainer(
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default_root_dir=tmpdir, max_epochs=1, logger=logger, checkpoint_callback=ModelCheckpoint(tmpdir),
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)
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result = trainer.fit(model)
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# traning complete
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assert result == 1
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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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hparams_path = os.path.join(tutils.get_data_path(logger, path_dir=tmpdir), 'hparams.yaml')
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hparams_url = f'http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}'
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ckpt_path = hparams_url if url_ckpt else new_weights_path
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class CurrentModel(EvalModelTemplate):
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def __init__(self):
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super().__init__()
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self.c_d3 = torch.nn.Linear(7, 7)
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CurrentModel.load_from_checkpoint(
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checkpoint_path=ckpt_path, hparams_file=hparams_path, strict=False,
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)
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with pytest.raises(RuntimeError, match=r'Missing key\(s\) in state_dict: "c_d3.weight", "c_d3.bias"'):
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CurrentModel.load_from_checkpoint(
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checkpoint_path=ckpt_path, hparams_file=hparams_path, strict=True,
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)
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def test_model_pickle(tmpdir):
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model = EvalModelTemplate()
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pickle.dumps(model)
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cloudpickle.dumps(model)
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