| | |
| | 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 |
