| |
| from tqdm import tqdm |
| from PIL import Image |
| import torch |
| import os |
| import numpy as np |
| |
| from transformers import CLIPProcessor, CLIPModel |
| |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
| model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14").to(device) |
| processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14") |
| |
| def calculate_clip_I(image1, image2): |
|
|
| inputs1 = processor(images=image1, return_tensors="pt").to(device) |
| inputs2 = processor(images=image2, return_tensors="pt").to(device) |
|
|
| with torch.no_grad(): |
| image_features1 = model.get_image_features(**inputs1) |
| image_features2 = model.get_image_features(**inputs2) |
|
|
| image_features1 /= image_features1.norm(dim=-1, keepdim=True) |
| image_features2 /= image_features2.norm(dim=-1, keepdim=True) |
|
|
| similarity = torch.matmul(image_features1, image_features2.T).cpu().numpy()[0][0] |
| |
| return similarity |
