Spaces:
Running
Running
File size: 2,658 Bytes
e666301 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import torch
import io
class ImageProcessor:
def __init__(self):
# Initialize CLIP
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(self.device)
self.clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
def get_embedding(self, image: Image.Image):
inputs = self.clip_processor(images=image, return_tensors="pt").to(self.device)
with torch.no_grad():
outputs = self.clip_model.get_image_features(**inputs)
# Robustly handle different CLIP output formats
if hasattr(outputs, "image_embeds"):
image_features = outputs.image_embeds
elif hasattr(outputs, "pooler_output"):
image_features = outputs.pooler_output
elif isinstance(outputs, (list, tuple)):
image_features = outputs[0]
else:
image_features = outputs
# Final check: must be a tensor
if not isinstance(image_features, torch.Tensor):
try:
image_features = outputs[0]
except:
raise Exception(f"Failed to extract tensor from {type(outputs)}")
# Normalize
image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)
return image_features.cpu().numpy()[0].tolist()
def get_text_embedding(self, text: str):
"""
Generate embedding for text query.
"""
inputs = self.clip_processor(text=text, return_tensors="pt", padding=True, truncation=True).to(self.device)
with torch.no_grad():
outputs = self.clip_model.get_text_features(**inputs)
# Robustly handle different CLIP output formats
if hasattr(outputs, "text_embeds"):
text_features = outputs.text_embeds
elif hasattr(outputs, "pooler_output"):
text_features = outputs.pooler_output
elif isinstance(outputs, (list, tuple)):
text_features = outputs[0]
else:
text_features = outputs
# Final check: must be a tensor
if not isinstance(text_features, torch.Tensor):
try:
text_features = outputs[0]
except:
raise Exception(f"Failed to extract tensor from {type(outputs)}")
# Normalize
text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)
return text_features.cpu().numpy()[0].tolist()
|