336 lines
13 KiB
Python
336 lines
13 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.trainer.supporters import PredictionCollection
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from pytorch_lightning.core.step_result import Result, EvalResult
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.model_utils import is_overridden
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from pytorch_lightning.utilities.distributed import rank_zero_warn
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from pytorch_lightning.utilities.warning_utils import WarningCache
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class EvaluationLoop(object):
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def __init__(self, trainer):
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self.trainer = trainer
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self.testing = False
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self.outputs = []
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self.step_metrics = []
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self.predictions = None
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self.max_batches = None
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self.warning_cache = WarningCache()
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def on_trainer_init(self):
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self.trainer.num_val_batches = []
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self.trainer.num_sanity_val_batches = []
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self.trainer.num_test_batches = []
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self.trainer.test_dataloaders = None
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self.trainer.val_dataloaders = None
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self.trainer.running_sanity_check = False
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self.trainer.testing = False
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# when .test() is called, it sets this
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self.trainer.tested_ckpt_path = None
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# when true, prints test results
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self.trainer.verbose_test = True
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def get_evaluation_dataloaders(self, max_batches):
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# select dataloaders
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model = self.trainer.get_model()
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# select dataloaders
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if self.testing:
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self.trainer.reset_test_dataloader(model)
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dataloaders = self.trainer.test_dataloaders
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new_max_batches = self.trainer.num_test_batches
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else:
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# val
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in_sanity_check = self.trainer.running_sanity_check
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should_reload_every_epoch = self.trainer.reload_dataloaders_every_epoch
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if (self.trainer.val_dataloaders is None or should_reload_every_epoch) and not in_sanity_check:
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self.trainer.reset_val_dataloader(model)
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dataloaders = self.trainer.val_dataloaders
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new_max_batches = self.trainer.num_val_batches
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if max_batches is None:
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max_batches = new_max_batches
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return dataloaders, max_batches
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def should_skip_evaluation(self, dataloaders, max_batches):
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# skip when dataloaders aren't defined
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if dataloaders is None:
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return True
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# enable disabling validation step with limit_val_batches = 0
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should_skip = sum(max_batches) == 0
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if should_skip:
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return True
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return False
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def on_evaluation_start(self, *args, **kwargs):
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if self.testing:
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self.trainer.call_hook('on_test_start', *args, **kwargs)
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else:
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self.trainer.call_hook('on_validation_start', *args, **kwargs)
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def on_evaluation_end(self, *args, **kwargs):
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if self.testing:
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self.trainer.call_hook('on_test_end', *args, **kwargs)
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else:
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self.trainer.call_hook('on_validation_end', *args, **kwargs)
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def reload_evaluation_dataloaders(self):
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model = self.trainer.get_model()
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if self.testing:
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self.trainer.reset_test_dataloader(model)
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else:
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self.trainer.reset_val_dataloader(model)
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def is_using_eval_results(self):
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outputs = self.outputs
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using_eval_result = len(outputs) > 0 and len(outputs[0]) > 0 and isinstance(outputs[0][0], EvalResult)
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return using_eval_result
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def setup(self, model, max_batches, dataloaders):
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# copy properties for forward overrides
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self.trainer.model_connector.copy_trainer_model_properties(model)
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# bookkeeping
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self.outputs = []
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self.predictions = PredictionCollection(self.trainer.global_rank, self.trainer.world_size)
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# convert max_batches to list
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if isinstance(max_batches, int):
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max_batches = [max_batches] * len(dataloaders)
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self.max_batches = max_batches
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def on_evaluation_epoch_start(self, *args, **kwargs):
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if self.testing:
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self.trainer.call_hook('on_test_epoch_start', *args, **kwargs)
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else:
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self.trainer.call_hook('on_validation_epoch_start', *args, **kwargs)
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def build_args(self, test_mode, batch, batch_idx, dataloader_idx):
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# make dataloader_idx arg in validation_step optional
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args = [batch, batch_idx]
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multiple_val_loaders = (not test_mode and len(self.trainer.val_dataloaders) > 1)
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multiple_test_loaders = (test_mode and len(self.trainer.test_dataloaders) > 1)
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if multiple_test_loaders or multiple_val_loaders:
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args.append(dataloader_idx)
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return args
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def evaluation_step(self, test_mode, batch, batch_idx, dataloader_idx):
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# configure args
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args = self.build_args(test_mode, batch, batch_idx, dataloader_idx)
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# run actual test step
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if self.testing:
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output = self.trainer.accelerator_backend.test_step(args)
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else:
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output = self.trainer.accelerator_backend.validation_step(args)
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# track batch size for weighted average
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is_result_obj = isinstance(output, Result)
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if is_result_obj:
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output.track_batch_size(len(batch))
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# allow only EvalResult when using structured results (from val_step)
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if is_result_obj and not isinstance(output, EvalResult):
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m = 'only EvalResults or dicts are allowed from validation_step'
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raise MisconfigurationException(m)
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return output
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def evaluation_step_end(self, *args, **kwargs):
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if self.testing:
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output = self.trainer.call_hook('test_step_end', *args, **kwargs)
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else:
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output = self.trainer.call_hook('validation_step_end', *args, **kwargs)
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return output
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def evaluation_epoch_end(self, num_dataloaders):
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using_eval_result = self.is_using_eval_results()
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# call the model epoch end
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deprecated_results = self.__run_eval_epoch_end(num_dataloaders, using_eval_result)
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# 1.0
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epoch_logs = self.trainer.get_model()._results
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# enable returning anything
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for i, r in enumerate(deprecated_results):
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if not isinstance(r, (dict, Result, torch.Tensor)):
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deprecated_results[i] = []
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return deprecated_results, epoch_logs
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def log_epoch_metrics(self, deprecated_eval_results, epoch_logs, test_mode):
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using_eval_result = self.is_using_eval_results()
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eval_loop_results = self.trainer.logger_connector.on_evaluation_epoch_end(
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deprecated_eval_results,
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epoch_logs,
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using_eval_result,
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test_mode
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)
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return eval_loop_results
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def __run_eval_epoch_end(self, num_dataloaders, using_eval_result):
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model = self.trainer.get_model()
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# with a single dataloader don't pass an array
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outputs = self.outputs
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eval_results = outputs
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if num_dataloaders == 1:
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eval_results = outputs[0]
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user_reduced = False
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if self.testing:
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if is_overridden('test_epoch_end', model=model):
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model._current_fx_name = 'test_epoch_end'
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if using_eval_result:
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eval_results = self.__gather_epoch_end_eval_results(outputs)
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eval_results = model.test_epoch_end(eval_results)
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user_reduced = True
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else:
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if is_overridden('validation_epoch_end', model=model):
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model._current_fx_name = 'validation_epoch_end'
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if using_eval_result:
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eval_results = self.__gather_epoch_end_eval_results(outputs)
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eval_results = model.validation_epoch_end(eval_results)
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user_reduced = True
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# depre warning
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if eval_results is not None and user_reduced:
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step = 'testing_epoch_end' if self.testing else 'validation_epoch_end'
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m = f'The {step} should not return anything as of 9.1.' \
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f'to log, use self.log(...) or self.write(...) directly in the LightningModule'
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self.warning_cache.warn(m)
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if using_eval_result and not user_reduced:
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eval_results = self.__auto_reduce_result_objs(outputs)
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if not isinstance(eval_results, list):
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eval_results = [eval_results]
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return eval_results
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def __gather_epoch_end_eval_results(self, outputs):
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eval_results = []
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for epoch_output in outputs:
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result = epoch_output[0].__class__.gather(epoch_output)
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if 'checkpoint_on' in result:
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result.checkpoint_on = result.checkpoint_on.mean()
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if 'early_stop_on' in result:
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result.early_stop_on = result.early_stop_on.mean()
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eval_results.append(result)
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# with 1 dataloader don't pass in a list
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if len(eval_results) == 1:
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eval_results = eval_results[0]
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return eval_results
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def __auto_reduce_result_objs(self, outputs):
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# outputs has a list of results per dataloader
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eval_results = []
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for dl_output in outputs:
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result = dl_output[0]
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result = result.__class__.reduce_on_epoch_end(dl_output)
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if 'checkpoint_on' in result:
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result.checkpoint_on = result.checkpoint_on.mean()
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if 'early_stop_on' in result:
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result.early_stop_on = result.early_stop_on.mean()
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eval_results.append(result)
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return eval_results
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def on_evaluation_batch_start(self, *args, **kwargs):
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# reset the result of the PL module
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model = self.trainer.get_model()
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model._results = Result()
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model._current_fx_name = 'evaluation_step'
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if self.testing:
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self.trainer.call_hook('on_test_batch_start', *args, **kwargs)
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else:
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self.trainer.call_hook('on_validation_batch_start', *args, **kwargs)
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def on_evaluation_batch_end(self, *args, **kwargs):
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if self.testing:
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self.trainer.call_hook('on_test_batch_end', *args, **kwargs)
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else:
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self.trainer.call_hook('on_validation_batch_end', *args, **kwargs)
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def evaluation_batch_end_cleanup(self, output, batch_idx, dataloader_idx):
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# Add step predictions to prediction collection to write later
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if output is not None:
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do_write_predictions = isinstance(output, Result) and self.testing
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if do_write_predictions:
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self.predictions.add(output.pop('predictions', None))
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# track debug metrics
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self.trainer.dev_debugger.track_eval_loss_history(self.testing, batch_idx, dataloader_idx, output)
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def on_evaluation_epoch_end(self, *args, **kwargs):
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# call the callback hook
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if self.testing:
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self.trainer.call_hook('on_test_epoch_end', *args, **kwargs)
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else:
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self.trainer.call_hook('on_validation_epoch_end', *args, **kwargs)
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def log_evaluation_step_metrics(self, batch, batch_idx):
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results = self.trainer.get_model()._results
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if len(results) == 1:
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return None
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results.track_batch_size(len(batch))
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self.__log_result_step_metrics(results, batch_idx)
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return results
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# TODO: deprecate at 1.0
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def log_evaluation_step_metrics_legacy(self, output, batch_idx):
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if self.trainer.running_sanity_check:
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return
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if isinstance(output, EvalResult):
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self.__log_result_step_metrics(output, batch_idx)
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def __log_result_step_metrics(self, output, batch_idx):
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step_log_metrics = output.get_batch_log_metrics(include_forked_originals=False)
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step_pbar_metrics = output.get_batch_pbar_metrics(include_forked_originals=False)
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if len(step_log_metrics) > 0:
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# make the metrics appear as a different line in the same graph
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metrics_by_epoch = {}
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for k, v in step_log_metrics.items():
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metrics_by_epoch[f'{k}/epoch_{self.trainer.current_epoch}'] = v
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self.trainer.logger_connector.log_metrics(metrics_by_epoch, {}, step=batch_idx)
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if len(step_pbar_metrics) > 0:
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self.trainer.logger_connector.add_progress_bar_metrics(step_pbar_metrics)
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