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from typing import List, Union
from PIL import Image
import torch
from .open_clip import create_model_and_transforms, get_tokenizer
from .config import MODEL_PATHS
class CLIPScore(torch.nn.Module):
def __init__(self, device: torch.device, path: str = MODEL_PATHS):
super().__init__()
"""Initialize the CLIPScore with a model and tokenizer.
Args:
device (torch.device): The device to load the model on.
"""
self.device = device
# Create model and transforms
self.model, _, self.preprocess_val = create_model_and_transforms(
"ViT-H-14",
# "laion2B-s32B-b79K",
pretrained=path.get("open_clip"),
precision="amp",
device=device,
jit=False,
force_quick_gelu=False,
force_custom_text=False,
force_patch_dropout=False,
force_image_size=None,
pretrained_image=False,
image_mean=None,
image_std=None,
light_augmentation=True,
aug_cfg={},
output_dict=True,
with_score_predictor=False,
with_region_predictor=False,
)
# Initialize tokenizer
self.tokenizer = get_tokenizer("ViT-H-14", path["open_clip_bpe"])
self.model = self.model.to(device)
self.model.eval()
def _calculate_score(self, image: torch.Tensor, prompt: str) -> float:
"""Calculate the CLIP score for a single image and prompt.
Args:
image (torch.Tensor): The processed image tensor.
prompt (str): The prompt text.
Returns:
float: The CLIP score.
"""
with torch.no_grad():
# Process the prompt
text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True)
# Calculate the CLIP score
outputs = self.model(image, text)
image_features, text_features = outputs["image_features"], outputs["text_features"]
logits_per_image = image_features @ text_features.T
clip_score = torch.diagonal(logits_per_image).cpu().numpy()
return clip_score[0].item()
@torch.no_grad()
def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]:
"""Score the images based on the prompt.
Args:
images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
prompt (str): The prompt text.
Returns:
List[float]: List of CLIP scores for the images.
"""
if isinstance(images, (str, Image.Image)):
# Single image
if isinstance(images, str):
image = self.preprocess_val(Image.open(images)).unsqueeze(0).to(device=self.device, non_blocking=True)
else:
image = self.preprocess_val(images).unsqueeze(0).to(device=self.device, non_blocking=True)
return [self._calculate_score(image, prompt)]
elif isinstance(images, list):
# Multiple images
scores = []
for one_images in images:
if isinstance(one_images, str):
image = self.preprocess_val(Image.open(one_images)).unsqueeze(0).to(device=self.device, non_blocking=True)
elif isinstance(one_images, Image.Image):
image = self.preprocess_val(one_images).unsqueeze(0).to(device=self.device, non_blocking=True)
else:
raise TypeError("The type of parameter images is illegal.")
scores.append(self._calculate_score(image, prompt))
return scores
else:
raise TypeError("The type of parameter images is illegal.")
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