| from transformers import CLIPProcessor
|
|
|
|
|
| class ClipTransform(object):
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| def __init__(self, split):
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| self.transform = CLIPProcessor.from_pretrained("geolocal/StreetCLIP")
|
|
|
| def __call__(self, x):
|
|
|
| return self.transform(images=[x], return_tensors="pt")
|
|
|
|
|
| if __name__ == "__main__":
|
|
|
| import glob
|
| import torchvision.transforms as transforms
|
| from torchvision.utils import save_image
|
| from omegaconf import DictConfig, OmegaConf
|
| from hydra.utils import instantiate
|
| import torch
|
| from PIL import Image
|
|
|
| fast_clip_config = OmegaConf.load(
|
| "./configs/dataset/train_transform/fast_clip.yaml"
|
| )
|
| fast_clip_transform = instantiate(fast_clip_config)
|
| clip_transform = ClipTransform(None)
|
|
|
| img_paths = glob.glob("./datasets/osv5m/test/images/*.jpg")
|
| original_imgs, re_implemted_imgs, diff = [], [], []
|
|
|
| for i in range(16):
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| img = Image.open(img_paths[i])
|
| clip_img = clip_transform(img)
|
| fast_clip_img = fast_clip_transform(img)
|
| original_imgs.append(clip_img)
|
| re_implemted_imgs.append(fast_clip_img)
|
| max_diff = (clip_img - fast_clip_img).abs()
|
| diff.append(max_diff)
|
| if max_diff.max() > 1e-5:
|
| print(max_diff.max())
|
| original_imgs = torch.stack(original_imgs)
|
| re_implemted_imgs = torch.stack(re_implemted_imgs)
|
| diff = torch.stack(diff)
|
|
|
