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Resolve logger warnings (#1981)

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Summary:
# PR Summary
This small PR resolves the annoying deprecation warnings of the `logger` library:
```python
DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead
```

Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1981

Reviewed By: MichaelRamamonjisoa

Differential Revision: D75287169

Pulled By: bottler

fbshipit-source-id: 9ff9f5dd648aca8d8bb5d33577909da711d18647
This commit is contained in:
Emmanuel Ferdman 2025-06-10 02:27:54 -07:00 committed by Facebook GitHub Bot
parent f315ac131b
commit 182e845c19
2 changed files with 5 additions and 5 deletions
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4
pytorch3d/implicitron/dataset/sql_dataset.py
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@ -755,7 +755,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
if pick_sequences: if pick_sequences:
old_len = len(eval_batches) old_len = len(eval_batches)
eval_batches = [b for b in eval_batches if b[0][0] in pick_sequences] eval_batches = [b for b in eval_batches if b[0][0] in pick_sequences]
logger.warn( logger.warning(
f"Picked eval batches by sequence/cat: {old_len} -> {len(eval_batches)}" f"Picked eval batches by sequence/cat: {old_len} -> {len(eval_batches)}"
) )
@ -763,7 +763,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
old_len = len(eval_batches) old_len = len(eval_batches)
exclude_sequences = set(self.exclude_sequences) exclude_sequences = set(self.exclude_sequences)
eval_batches = [b for b in eval_batches if b[0][0] not in exclude_sequences] eval_batches = [b for b in eval_batches if b[0][0] not in exclude_sequences]
logger.warn( logger.warning(
f"Excluded eval batches by sequence: {old_len} -> {len(eval_batches)}" f"Excluded eval batches by sequence: {old_len} -> {len(eval_batches)}"
) )

6
pytorch3d/implicitron/models/visualization/render_flyaround.py
View File

@ -304,11 +304,11 @@ def _show_predictions(
assert isinstance(preds, list) assert isinstance(preds, list)
pred_all = [] pred_all = []
# Randomly choose a subset of the rendered images, sort by ordr in the sequence # Randomly choose a subset of the rendered images, sort by order in the sequence
n_samples = min(n_samples, len(preds)) n_samples = min(n_samples, len(preds))
pred_idx = sorted(random.sample(list(range(len(preds))), n_samples)) pred_idx = sorted(random.sample(list(range(len(preds))), n_samples))
for predi in pred_idx: for predi in pred_idx:
# Make the concatentation for the same camera vertically # Make the concatenation for the same camera vertically
pred_all.append( pred_all.append(
torch.cat( torch.cat(
[ [
@ -359,7 +359,7 @@ def _generate_prediction_videos(
vws = {} vws = {}
for k in predicted_keys: for k in predicted_keys:
if k not in preds[0]: if k not in preds[0]:
logger.warn(f"Cannot generate video for prediction key '{k}'") logger.warning(f"Cannot generate video for prediction key '{k}'")
continue continue
cache_dir = ( cache_dir = (
None None