120 lines
4.1 KiB
Python
120 lines
4.1 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 torch
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from pytorch_lightning import Trainer
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from tests.base.develop_utils import load_model_from_checkpoint, get_default_logger, \
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reset_seed
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def run_model_test_without_loggers(trainer_options, model, min_acc: float = 0.50):
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reset_seed()
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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, 'amp + ddp model failed to complete'
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pretrained_model = load_model_from_checkpoint(
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trainer.logger,
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trainer.checkpoint_callback.best_model_path,
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)
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# test new model accuracy
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test_loaders = model.test_dataloader()
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if not isinstance(test_loaders, list):
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test_loaders = [test_loaders]
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for dataloader in test_loaders:
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run_prediction(dataloader, pretrained_model, min_acc=min_acc)
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if trainer.use_ddp:
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# on hpc this would work fine... but need to hack it for the purpose of the test
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trainer.model = pretrained_model
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trainer.optimizers, trainer.lr_schedulers = pretrained_model.configure_optimizers()
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def run_model_test(trainer_options, model, on_gpu: bool = True, version=None, with_hpc: bool = True):
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reset_seed()
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save_dir = trainer_options['default_root_dir']
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# logger file to get meta
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logger = get_default_logger(save_dir, version=version)
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trainer_options.update(logger=logger)
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if 'checkpoint_callback' not in trainer_options:
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trainer_options.update(checkpoint_callback=True)
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trainer = Trainer(**trainer_options)
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initial_values = torch.tensor([torch.sum(torch.abs(x)) for x in model.parameters()])
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result = trainer.fit(model)
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post_train_values = torch.tensor([torch.sum(torch.abs(x)) for x in model.parameters()])
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assert result == 1, 'trainer failed'
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# Check that the model is actually changed post-training
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assert torch.norm(initial_values - post_train_values) > 0.1
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# test model loading
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pretrained_model = load_model_from_checkpoint(logger, trainer.checkpoint_callback.best_model_path)
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# test new model accuracy
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test_loaders = model.test_dataloader()
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if not isinstance(test_loaders, list):
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test_loaders = [test_loaders]
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for dataloader in test_loaders:
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run_prediction(dataloader, pretrained_model)
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if with_hpc:
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if trainer.use_ddp or trainer.use_ddp2:
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# on hpc this would work fine... but need to hack it for the purpose of the test
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trainer.model = pretrained_model
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trainer.optimizers, trainer.lr_schedulers, trainer.optimizer_frequencies = \
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trainer.init_optimizers(pretrained_model)
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# test HPC loading / saving
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trainer.checkpoint_connector.hpc_save(save_dir, logger)
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trainer.checkpoint_connector.hpc_load(save_dir, on_gpu=on_gpu)
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def run_prediction(dataloader, trained_model, dp=False, min_acc=0.50):
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# run prediction on 1 batch
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batch = next(iter(dataloader))
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x, y = batch
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x = x.view(x.size(0), -1)
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if dp:
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with torch.no_grad():
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output = trained_model(batch, 0)
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acc = output['val_acc']
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acc = torch.mean(acc).item()
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else:
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with torch.no_grad():
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y_hat = trained_model(x)
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y_hat = y_hat.cpu()
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# acc
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labels_hat = torch.argmax(y_hat, dim=1)
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y = y.cpu()
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acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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acc = torch.tensor(acc)
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acc = acc.item()
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assert acc >= min_acc, f"This model is expected to get > {min_acc} in test set (it got {acc})"
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