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