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L40S
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import torch
from PIL import Image
from transformers import AutoProcessor, AutoModel
from typing import List, Union
import os
from .config import MODEL_PATHS
class PickScore(torch.nn.Module):
def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS):
super().__init__()
"""Initialize the Selector with a processor and model.
Args:
device (Union[str, torch.device]): The device to load the model on.
"""
self.device = device if isinstance(device, torch.device) else torch.device(device)
processor_name_or_path = path.get("clip")
model_pretrained_name_or_path = path.get("pickscore")
self.processor = AutoProcessor.from_pretrained(processor_name_or_path)
self.model = AutoModel.from_pretrained(model_pretrained_name_or_path).eval().to(self.device)
def _calculate_score(self, image: torch.Tensor, prompt: str, softmax: bool = False) -> float:
"""Calculate the score for a single image and prompt.
Args:
image (torch.Tensor): The processed image tensor.
prompt (str): The prompt text.
softmax (bool): Whether to apply softmax to the scores.
Returns:
float: The score for the image.
"""
with torch.no_grad():
# Prepare text inputs
text_inputs = self.processor(
text=prompt,
padding=True,
truncation=True,
max_length=77,
return_tensors="pt",
).to(self.device)
# Embed images and text
image_embs = self.model.get_image_features(pixel_values=image)
image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)
text_embs = self.model.get_text_features(**text_inputs)
text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True)
# Compute score
score = (text_embs @ image_embs.T)[0]
if softmax:
# Apply logit scale and softmax
score = torch.softmax(self.model.logit_scale.exp() * score, dim=-1)
return score.cpu().item()
@torch.no_grad()
def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str, softmax: bool = False) -> 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.
softmax (bool): Whether to apply softmax to the scores.
Returns:
List[float]: List of scores for the images.
"""
try:
if isinstance(images, (str, Image.Image)):
# Single image
if isinstance(images, str):
pil_image = Image.open(images)
else:
pil_image = images
# Prepare image inputs
image_inputs = self.processor(
images=pil_image,
padding=True,
truncation=True,
max_length=77,
return_tensors="pt",
).to(self.device)
return [self._calculate_score(image_inputs["pixel_values"], prompt, softmax)]
elif isinstance(images, list):
# Multiple images
scores = []
for one_image in images:
if isinstance(one_image, str):
pil_image = Image.open(one_image)
elif isinstance(one_image, Image.Image):
pil_image = one_image
else:
raise TypeError("The type of parameter images is illegal.")
# Prepare image inputs
image_inputs = self.processor(
images=pil_image,
padding=True,
truncation=True,
max_length=77,
return_tensors="pt",
).to(self.device)
scores.append(self._calculate_score(image_inputs["pixel_values"], prompt, softmax))
return scores
else:
raise TypeError("The type of parameter images is illegal.")
except Exception as e:
raise RuntimeError(f"Error in scoring images: {e}")
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