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Handle new Transformers v5 return type #3353

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Handle new Transformers v5 return type #3353

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6 changes: 6 additions & 0 deletions src/torchmetrics/functional/multimodal/clip_iqa.py
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Original file line number Diff line number Diff line change
Expand Up @@ -175,6 +175,9 @@ def _clip_iqa_get_anchor_vectors(
anchors = model.get_text_features(
text_processed["input_ids"].to(device), text_processed["attention_mask"].to(device)
)
# Handle both tensor and BaseModelOutputWithPooling returns (transformers v5)
if hasattr(anchors, "pooler_output"):
anchors = anchors.pooler_output
return anchors / anchors.norm(p=2, dim=-1, keepdim=True)


Expand All @@ -198,6 +201,9 @@ def _clip_iqa_update(
else:
processed_input = processor(images=[i.cpu() for i in images], return_tensors="pt", padding=True)
img_features = model.get_image_features(processed_input["pixel_values"].to(device))
# Handle both tensor and BaseModelOutputWithPooling returns (transformers v5)
if hasattr(img_features, "pooler_output"):
img_features = img_features.pooler_output
return img_features / img_features.norm(p=2, dim=-1, keepdim=True)


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13 changes: 9 additions & 4 deletions src/torchmetrics/functional/multimodal/clip_score.py
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Original file line number Diff line number Diff line change
Expand Up @@ -130,8 +130,8 @@ def _get_features(
if modality == "image":
image_data = [i for i in data if isinstance(i, Tensor)] # Add type checking for images
processed = processor(images=[i.cpu() for i in image_data], return_tensors="pt", padding=True)
return model.get_image_features(processed["pixel_values"].to(device))
if modality == "text":
features = model.get_image_features(processed["pixel_values"].to(device))
elif modality == "text":
processed = processor(text=data, return_tensors="pt", padding=True)
if hasattr(model.config, "text_config") and hasattr(model.config.text_config, "max_position_embeddings"):
max_position_embeddings = model.config.text_config.max_position_embeddings
Expand All @@ -144,8 +144,13 @@ def _get_features(
)
processed["attention_mask"] = processed["attention_mask"][..., :max_position_embeddings]
processed["input_ids"] = processed["input_ids"][..., :max_position_embeddings]
return model.get_text_features(processed["input_ids"].to(device), processed["attention_mask"].to(device))
raise ValueError(f"invalid modality {modality}")
features = model.get_text_features(processed["input_ids"].to(device), processed["attention_mask"].to(device))
else:
raise ValueError(f"invalid modality {modality}")
# Handle both tensor and BaseModelOutputWithPooling returns (transformers v5)
if hasattr(features, "pooler_output"):
features = features.pooler_output
return features


def _clip_score_update(
Expand Down
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