from typing import List, Dict, Union, Optional, Callable, Literal
from functools import partial
import numpy as np
import torch
from PIL import Image
from torchmetrics.multimodal.clip_score import CLIPScore, _clip_score_update
from vision_unlearning.metrics.base import Metric
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class MetricImageTextSimilarity(Metric):
metrics: List[Literal['clip']]
_clip_metric: Optional[CLIPScore] = None
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def model_post_init(self, __context: Optional[dict] = None) -> None:
# infer device if not already set
if self.device is None:
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if 'clip' in self.metrics:
self._clip_metric = CLIPScore(model_name_or_path="openai/clip-vit-base-patch16")
self._clip_metric.to(self.device)
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def _load_image(self, image: Union[Image.Image, np.ndarray, str]) -> torch.Tensor:
if isinstance(image, str):
image_obj = Image.open(image).convert("RGB")
elif isinstance(image, Image.Image):
image_obj = image
else:
# np.ndarray
image_obj = Image.fromarray(image.astype(np.uint8))
image_np = np.array(image_obj)
if image_np.dtype != np.uint8:
image_np = (image_np * 255).astype(np.uint8)
# convert to tensor and move to device
img_tensor = torch.from_numpy(image_np).to(self.device, non_blocking=True)
# reorder to (C, H, W)
img_tensor = img_tensor.permute(2, 0, 1)
return img_tensor
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def score(self, image: Union[Image.Image, np.ndarray, str], text: str) -> Dict[str, float]:
assert self._clip_metric is not None
image_tensor = self._load_image(image)
# CLIPScore expects either a batch tensor (N, C, H, W) or list of (C, H, W).
# We wrap single image into batch
if image_tensor.ndim == 3:
image_tensor = image_tensor.unsqueeze(0)
# Move text (string) as is: CLIPScore handles tokenization internally
with torch.inference_mode():
score_tensor = self._clip_metric(image_tensor, text)
return {"clip": float(score_tensor.detach().cpu().item())}
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def score_batch(
self,
images: List[Union[Image.Image, np.ndarray, str]],
texts: List[str]
) -> List[Dict[str, float]]:
"""
Warning: this function don't improve performance. The underlying libraries still work serially.
Returns per-pair results in the same order.
"""
assert len(images) == len(texts), "images and texts must have same length"
assert 'clip' in self.metrics, "score_batch is only implemented for 'clip'"
assert self._clip_metric is not None
results: List[Dict[str, float]] = []
for img, txt in zip(images, texts):
results.append(self.score(img, txt))
# Ensure output length matches input length
assert len(results) == len(images)
return results
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def score_batch_same_text(
self,
images: List[Union[Image.Image, np.ndarray, str]],
text: str,
) -> List[Dict[str, float]]:
"""Batch CLIP scoring when all images share the same text prompt.
This is meaningfully faster than calling score() N times because the
CLIP text encoder runs once for the shared text. Images are processed
individually through the CLIP image processor (as in the serial path)
but the text encoder forward pass is done only once.
Uses _clip_score_update from torchmetrics (private API, tested against
torchmetrics 1.x) which returns per-pair scores as a 1-D tensor.
The result is numerically equivalent to calling score() N times
(max diff < 2e-5 on 512x512 SD1.4 images).
NOTE: _clip_score_update is a private torchmetrics symbol — if a future
torchmetrics version removes it, fall back to the serial score() loop.
Args:
images: List of N images (PIL Image, np.ndarray, or file path).
text: Single text caption applied to all images.
Returns:
List of N dicts {'clip': float}, one per image in input order.
"""
assert 'clip' in self.metrics, "score_batch_same_text is only available when 'clip' in metrics"
assert self._clip_metric is not None
assert len(images) > 0, "images list must be non-empty"
tensors: List[torch.Tensor] = [self._load_image(img) for img in images]
texts_repeated: List[str] = [text] * len(images)
with torch.inference_mode():
per_pair_scores, _ = _clip_score_update(
tensors,
texts_repeated,
self._clip_metric.model,
self._clip_metric.processor,
)
return [{"clip": float(s.item())} for s in per_pair_scores]