mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-11 12:31:23 +08:00
Fixing tests (#936)
* abs import * rename test model * update trainer * revert test_step check * move tags * fix test_step * clean tests * fix template * update dataset path * fix parent order
This commit is contained in:
Jirka Borovec
@@ -216,13 +216,13 @@ class TrainerEvaluationLoopMixin(ABC):
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# this is just empty shell for code from other class
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pass
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def evaluate(self, model, dataloaders, max_batches, test=False):
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def evaluate(self, model, dataloaders, max_batches, test_mode: bool = False):
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"""Run evaluation code.
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:param model: PT model
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:param dataloaders: list of PT dataloaders
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:param max_batches: Scalar
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:param test: boolean
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:param test_mode
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:return:
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"""
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# enable eval mode
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@@ -260,18 +260,14 @@ class TrainerEvaluationLoopMixin(ABC):
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# -----------------
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# RUN EVALUATION STEP
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# -----------------
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output = self.evaluation_forward(model,
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batch,
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batch_idx,
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dataloader_idx,
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test)
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output = self.evaluation_forward(model, batch, batch_idx, dataloader_idx, test_mode)
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# track outputs for collation
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dl_outputs.append(output)
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# batch done
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if batch_idx % self.progress_bar_refresh_rate == 0:
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if test:
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if test_mode:
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self.test_progress_bar.update(self.progress_bar_refresh_rate)
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else:
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self.val_progress_bar.update(self.progress_bar_refresh_rate)
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@@ -286,7 +282,7 @@ class TrainerEvaluationLoopMixin(ABC):
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# give model a chance to do something with the outputs (and method defined)
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model = self.get_model()
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if test and self.is_overriden('test_end'):
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if test_mode and self.is_overriden('test_end'):
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eval_results = model.test_end(outputs)
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elif self.is_overriden('validation_end'):
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eval_results = model.validation_end(outputs)
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@@ -299,11 +295,11 @@ class TrainerEvaluationLoopMixin(ABC):
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return eval_results
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def run_evaluation(self, test=False):
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def run_evaluation(self, test_mode: bool = False):
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# when testing make sure user defined a test step
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if test and not self.is_overriden('test_step'):
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m = '''You called `.test()` without defining model's `.test_step()`.
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Please define and try again'''
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if test_mode and not self.is_overriden('test_step'):
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m = "You called `.test()` without defining model's `.test_step()`." \
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" Please define and try again"
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raise MisconfigurationException(m)
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# hook
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@@ -311,7 +307,7 @@ class TrainerEvaluationLoopMixin(ABC):
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model.on_pre_performance_check()
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# select dataloaders
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if test:
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if test_mode:
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if self.reload_dataloaders_every_epoch or self.test_dataloaders is None:
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self.reset_test_dataloader(model)
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@@ -331,18 +327,15 @@ class TrainerEvaluationLoopMixin(ABC):
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# init validation or test progress bar
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# main progress bar will already be closed when testing so initial position is free
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position = 2 * self.process_position + (not test)
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desc = 'Testing' if test else 'Validating'
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pbar = tqdm(desc=desc, total=max_batches, leave=test, position=position,
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position = 2 * self.process_position + (not test_mode)
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desc = 'Testing' if test_mode else 'Validating'
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pbar = tqdm(desc=desc, total=max_batches, leave=test_mode, position=position,
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disable=not self.show_progress_bar, dynamic_ncols=True,
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file=sys.stdout)
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setattr(self, f'{"test" if test else "val"}_progress_bar', pbar)
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setattr(self, f'{"test" if test_mode else "val"}_progress_bar', pbar)
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# run evaluation
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eval_results = self.evaluate(self.model,
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dataloaders,
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max_batches,
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test)
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eval_results = self.evaluate(self.model, dataloaders, max_batches, test_mode)
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_, prog_bar_metrics, log_metrics, callback_metrics, _ = self.process_output(
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eval_results)
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@@ -359,27 +352,27 @@ class TrainerEvaluationLoopMixin(ABC):
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model.on_post_performance_check()
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# add model specific metrics
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if not test:
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if not test_mode:
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self.main_progress_bar.set_postfix(**self.training_tqdm_dict)
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# close progress bar
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if test:
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if test_mode:
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self.test_progress_bar.close()
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else:
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self.val_progress_bar.close()
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# model checkpointing
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if self.proc_rank == 0 and self.checkpoint_callback is not None and not test:
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if self.proc_rank == 0 and self.checkpoint_callback is not None and not test_mode:
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self.checkpoint_callback.on_validation_end()
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def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False):
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def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool = False):
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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 and len(self.test_dataloaders) > 1:
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if test_mode and len(self.test_dataloaders) > 1:
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args.append(dataloader_idx)
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elif not test and len(self.val_dataloaders) > 1:
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elif not test_mode and len(self.val_dataloaders) > 1:
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args.append(dataloader_idx)
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# handle DP, DDP forward
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@@ -402,7 +395,7 @@ class TrainerEvaluationLoopMixin(ABC):
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args[0] = batch
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# CPU
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if test:
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if test_mode:
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output = model.test_step(*args)
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else:
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output = model.validation_step(*args)
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