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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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