ref: refactored inner eval loop (#3141)
* refactored dataloader process hook * refactored dataloader process hook * refactored dataloader process hook
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William Falcon
GitHub
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f064d74be8
commit
ccc923cbb0
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@ -1,6 +1,7 @@
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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 EvalResult
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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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class EvaluationLoop(object):
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@ -43,11 +44,38 @@ class EvaluationLoop(object):
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else:
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self.trainer.call_hook('on_validation_epoch_start', *args, **kwargs)
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def evaluation_step(self, *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, **kwargs)
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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, **kwargs)
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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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@ -69,8 +97,37 @@ class EvaluationLoop(object):
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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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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_metrics(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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step_log_metrics = output.batch_log_metrics
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step_pbar_metrics = output.batch_pbar_metrics
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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.log_metrics(metrics_by_epoch, {}, step=batch_idx)
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if len(step_pbar_metrics) > 0:
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self.trainer.add_progress_bar_metrics(step_pbar_metrics)
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@ -132,8 +132,7 @@ from torch.utils.data import DataLoader
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.utilities import rank_zero_warn, flatten_dict, AMPType
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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.core.step_result import EvalResult, Result
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from pytorch_lightning.trainer.evaluate_loop import EvaluationLoop
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try:
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@ -273,55 +272,19 @@ class TrainerEvaluationLoopMixin(ABC):
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if batch_idx >= dl_max_batches:
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break
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# -----------------
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# eval_batch_start
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# -----------------
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# val loop hooks
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self.evaluation_loop.on_evaluation_batch_start(batch, batch_idx, dataloader_idx)
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# -----------------
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# RUN EVALUATION STEP
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# -----------------
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args = self.build_args(test_mode, batch, batch_idx, dataloader_idx)
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output = self.evaluation_loop.evaluation_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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# ------------------
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# EVAL STEP END
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# ------------------
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output = self.evaluation_loop.evaluation_step(test_mode, batch, batch_idx, dataloader_idx)
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output = self.evaluation_loop.evaluation_step_end(output)
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# ------------------
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# Hook: on_eval_batch_end
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# ------------------
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self.evaluation_loop.on_evaluation_batch_end(batch, batch_idx, dataloader_idx)
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# ----------------------
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# Post processing
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# ----------------------
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# track outputs for collation
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# clean up
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self.evaluation_loop.evaluation_batch_end_cleanup(output, batch_idx, dataloader_idx)
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self.evaluation_loop.log_metrics(output, batch_idx)
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if output is not None:
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# Add step predictions to prediction collection to write later
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do_write_predictions = is_result_obj and test_mode
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if do_write_predictions:
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self.evaluation_loop.predictions.add(output.pop('predictions', None))
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dl_outputs.append(output)
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self.__eval_add_step_metrics(output, batch_idx)
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# track debug metrics
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self.dev_debugger.track_eval_loss_history(test_mode, batch_idx, dataloader_idx, output)
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self.evaluation_loop.outputs.append(dl_outputs)
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# ---------------------
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@ -454,23 +417,6 @@ class TrainerEvaluationLoopMixin(ABC):
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eval_results = eval_results[0]
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return eval_results
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def __eval_add_step_metrics(self, output, batch_idx):
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# track step level metrics
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if isinstance(output, EvalResult) and not self.running_sanity_check:
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step_log_metrics = output.batch_log_metrics
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step_pbar_metrics = output.batch_pbar_metrics
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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.current_epoch}'] = v
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self.log_metrics(metrics_by_epoch, {}, step=batch_idx)
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if len(step_pbar_metrics) > 0:
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self.add_progress_bar_metrics(step_pbar_metrics)
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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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@ -588,12 +534,3 @@ class TrainerEvaluationLoopMixin(ABC):
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print('-' * 80)
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return eval_loop_results
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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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if (test_mode and len(self.test_dataloaders) > 1) or (not test_mode and len(self.val_dataloaders) > 1):
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args.append(dataloader_idx)
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return args
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