Spaces:
Running
Running
File size: 1,491 Bytes
c4c7cee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
from transformers import CLIPProcessor
class ClipTransform(object):
def __init__(self, split):
self.transform = CLIPProcessor.from_pretrained("geolocal/StreetCLIP")
def __call__(self, x):
# return self.transform(images=x, return_tensors="pt")["pixel_values"].squeeze(0)
return self.transform(images=[x], return_tensors="pt")
if __name__ == "__main__":
# sanity check
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):
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)
|