def _update_and_create_report(
self,
batch: Dict,
batch_idx: int,
step_output: Dict,
pl_module: LightningModule,
combined_report: Report = None,
update_meter: Meter = None,
):
report = Report(batch, step_output)
if update_meter:
update_meter.update_from_report(report)
should_accumulate = not (
batch_idx % self.trainer_config.accumulate_grad_batches == 0)
final_report = report
if should_accumulate and combined_report is not None:
combined_report.accumulate_tensor_fields_and_loss(
report, pl_module.metrics.required_params)
combined_report.batch_size += report.batch_size
final_report = combined_report
return final_report
def evaluation_loop(
self,
dataset_type: str,
use_tqdm: bool = False,
single_batch: bool = False) -> Tuple[Dict[str, Any], Type[Meter]]:
meter = Meter()
reporter = self.dataset_loader.get_test_reporter(dataset_type)
with torch.no_grad():
self.model.eval()
disable_tqdm = not use_tqdm or not is_master()
while reporter.next_dataset(flush_report=False):
dataloader = reporter.get_dataloader()
combined_report = None
for batch in tqdm.tqdm(dataloader, disable=disable_tqdm):
prepared_batch = reporter.prepare_batch(batch)
prepared_batch = to_device(prepared_batch, self.device)
model_output = self.model(prepared_batch)
report = Report(prepared_batch, model_output)
self.update_meter(report, meter)
# accumulate necessary params for metric calculation
if combined_report is None:
# make a copy of report since `reporter.add_to_report` will
# change some of the report keys later
combined_report = Report(report)
else:
combined_report.accumulate_tensor_fields_and_loss(
report, self.metrics.required_params)
combined_report.batch_size += report.batch_size
# Each node generates a separate copy of predict JSON from the report,
# which will be used to evaluate dataset-level metrics
# (such as mAP in object detection or CIDEr in image captioning)
# Since `reporter.add_to_report` changes report keys (e.g. scores),
# do this after `combined_report.accumulate_tensor_fields_and_loss`
if "__prediction_report__" in self.metrics.required_params:
reporter.add_to_report(report,
self.model,
execute_on_master_only=False)
if single_batch is True:
break
reporter.postprocess_dataset_report()
# add prediction_report is used for set-level metrics
combined_report.prediction_report = reporter.report
combined_report.metrics = self.metrics(combined_report,
combined_report)
self.update_meter(combined_report, meter, eval_mode=True)
# enable train mode again
self.model.train()
return combined_report, meter
