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Append val/test output to instance variable in EncDecSpeakerLabelModel #7562

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merged 3 commits into from
Sep 29, 2023
Merged

Append val/test output to instance variable in EncDecSpeakerLabelModel #7562

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bb08920
Append val/test output to the instance variable in EncDecSpeakerLabel…
athitten Sep 28, 2023
3952c68
Handle test case in evaluation_step
athitten Sep 28, 2023
af78882
Replace type with isinstance
athitten Sep 29, 2023
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12 changes: 9 additions & 3 deletions nemo/collections/asr/models/enhancement_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -434,10 +434,16 @@ def evaluation_step(self, batch, batch_idx, dataloader_idx: int = 0, tag: str =
# Log global step
self.log('global_step', torch.tensor(self.trainer.global_step, dtype=torch.float32), sync_dist=True)

if type(self.trainer.val_dataloaders) == list and len(self.trainer.val_dataloaders) > 1:
self.validation_step_outputs[dataloader_idx].append(output_dict)
if tag == 'val':
if isinstance(self.trainer.val_dataloaders, (list, tuple)) and len(self.trainer.val_dataloaders) > 1:
self.validation_step_outputs[dataloader_idx].append(output_dict)
else:
self.validation_step_outputs.append(output_dict)
else:
self.validation_step_outputs.append(output_dict)
if isinstance(self.trainer.test_dataloaders, (list, tuple)) and len(self.trainer.test_dataloaders) > 1:
self.test_step_outputs[dataloader_idx].append(output_dict)
else:
self.test_step_outputs.append(output_dict)
return output_dict

@classmethod
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14 changes: 13 additions & 1 deletion nemo/collections/asr/models/label_models.py
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Expand Up @@ -373,13 +373,25 @@ def evaluation_step(self, batch, batch_idx, dataloader_idx: int = 0, tag: str =
self._macro_accuracy.update(preds=logits, target=labels)
stats = self._macro_accuracy._final_state()

return {
output = {
f'{tag}_loss': loss_value,
f'{tag}_correct_counts': correct_counts,
f'{tag}_total_counts': total_counts,
f'{tag}_acc_micro_top_k': acc_top_k,
f'{tag}_acc_macro_stats': stats,
}
if tag == 'val':
if isinstance(self.trainer.val_dataloaders, (list, tuple)) and len(self.trainer.val_dataloaders) > 1:
self.validation_step_outputs[dataloader_idx].append(output)
else:
self.validation_step_outputs.append(output)
else:
if isinstance(self.trainer.test_dataloaders, (list, tuple)) and len(self.trainer.test_dataloaders) > 1:
self.test_step_outputs[dataloader_idx].append(output)
else:
self.test_step_outputs.append(output)

return output

def multi_evaluation_epoch_end(self, outputs, dataloader_idx: int = 0, tag: str = 'val'):
loss_mean = torch.stack([x[f'{tag}_loss'] for x in outputs]).mean()
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