121 lines
4.6 KiB
Python
121 lines
4.6 KiB
Python
# Copyright The Lightning AI 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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from functools import partial
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import torch
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from lightning.pytorch import LightningDataModule, LightningModule, Trainer
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from lightning.pytorch.demos.boring_classes import BoringModel
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from lightning.pytorch.utilities.imports import _TORCHMETRICS_GREATER_EQUAL_0_11 as _TM_GE_0_11
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from torchmetrics.functional import accuracy
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from tests_pytorch.helpers.utils import get_default_logger, load_model_from_checkpoint
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def run_model_test_without_loggers(
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trainer_options: dict, model: LightningModule, data: LightningDataModule = None, min_acc: float = 0.50
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):
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# fit model
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trainer = Trainer(**trainer_options)
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trainer.fit(model, datamodule=data)
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# correct result and ok accuracy
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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model2 = load_model_from_checkpoint(trainer.checkpoint_callback.best_model_path, type(model))
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# test new model accuracy
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test_loaders = model2.test_dataloader() if not data else data.test_dataloader()
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if not isinstance(test_loaders, list):
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test_loaders = [test_loaders]
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if not isinstance(model2, BoringModel):
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for dataloader in test_loaders:
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run_model_prediction(model2, dataloader, min_acc=min_acc)
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def run_model_test(
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trainer_options,
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model: LightningModule,
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data: LightningDataModule = None,
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version=None,
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with_hpc: bool = True,
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min_acc: float = 0.25,
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min_change_ratio: float = 0.03,
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):
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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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trainer = Trainer(**trainer_options)
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with torch.no_grad():
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initial_values = torch.cat([x.view(-1) for x in model.parameters()])
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trainer.fit(model, datamodule=data)
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with torch.no_grad():
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post_train_values = torch.cat([x.view(-1) for x in model.parameters()])
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# Check that the model has changed post-training
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change_ratio = torch.norm(initial_values - post_train_values) / torch.norm(initial_values)
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assert change_ratio >= min_change_ratio, (
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f"The change in the model's parameter norm is {change_ratio:.1f}"
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f" relative to the initial norm, but expected a change by >={min_change_ratio}"
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)
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if trainer.world_size != trainer.num_devices:
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# we're in multinode. unless the filesystem is shared, only the main node will have access to the checkpoint
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# since we cannot know this, the code below needs to be skipped
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return
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# test model loading
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_ = load_model_from_checkpoint(trainer.checkpoint_callback.best_model_path, type(model))
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# test new model accuracy
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test_loaders = model.test_dataloader() if not data else data.test_dataloader()
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if not isinstance(test_loaders, list):
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test_loaders = [test_loaders]
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if not isinstance(model, BoringModel):
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for dataloader in test_loaders:
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run_model_prediction(model, dataloader, min_acc=min_acc)
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if with_hpc:
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# test HPC saving
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# save logger to make sure we get all the metrics
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if logger:
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logger.finalize("finished")
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hpc_save_path = trainer._checkpoint_connector.hpc_save_path(save_dir)
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trainer.save_checkpoint(hpc_save_path)
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# test HPC loading
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checkpoint_path = trainer._checkpoint_connector._CheckpointConnector__get_max_ckpt_path_from_folder(save_dir)
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trainer._checkpoint_connector.restore(checkpoint_path)
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@torch.no_grad()
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def run_model_prediction(trained_model, dataloader, min_acc=0.50):
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orig_device = trained_model.device
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# run prediction on 1 batch
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trained_model.cpu()
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trained_model.eval()
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batch = next(iter(dataloader))
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x, y = batch
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x = x.flatten(1)
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y_hat = trained_model(x)
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metric = partial(accuracy, task="multiclass") if _TM_GE_0_11 else accuracy
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acc = metric(y_hat.cpu(), y.cpu(), top_k=2, num_classes=y_hat.size(-1)).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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trained_model.to(orig_device)
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