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Zero
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# Copyright (C) 2022-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
import torch
from models.croco import CroCoNet
from PIL import Image
import torchvision.transforms
from torchvision.transforms import ToTensor, Normalize, Compose
def main():
device = torch.device('cuda:0' if torch.cuda.is_available() and torch.cuda.device_count()>0 else 'cpu')
# load 224x224 images and transform them to tensor
imagenet_mean = [0.485, 0.456, 0.406]
imagenet_mean_tensor = torch.tensor(imagenet_mean).view(1,3,1,1).to(device, non_blocking=True)
imagenet_std = [0.229, 0.224, 0.225]
imagenet_std_tensor = torch.tensor(imagenet_std).view(1,3,1,1).to(device, non_blocking=True)
trfs = Compose([ToTensor(), Normalize(mean=imagenet_mean, std=imagenet_std)])
image1 = trfs(Image.open('assets/Chateau1.png').convert('RGB')).to(device, non_blocking=True).unsqueeze(0)
image2 = trfs(Image.open('assets/Chateau2.png').convert('RGB')).to(device, non_blocking=True).unsqueeze(0)
# load model
ckpt = torch.load('pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth', 'cpu')
model = CroCoNet( **ckpt.get('croco_kwargs',{})).to(device)
model.eval()
msg = model.load_state_dict(ckpt['model'], strict=True)
# forward
with torch.inference_mode():
out, mask, target = model(image1, image2)
# the output is normalized, thus use the mean/std of the actual image to go back to RGB space
patchified = model.patchify(image1)
mean = patchified.mean(dim=-1, keepdim=True)
var = patchified.var(dim=-1, keepdim=True)
decoded_image = model.unpatchify(out * (var + 1.e-6)**.5 + mean)
# undo imagenet normalization, prepare masked image
decoded_image = decoded_image * imagenet_std_tensor + imagenet_mean_tensor
input_image = image1 * imagenet_std_tensor + imagenet_mean_tensor
ref_image = image2 * imagenet_std_tensor + imagenet_mean_tensor
image_masks = model.unpatchify(model.patchify(torch.ones_like(ref_image)) * mask[:,:,None])
masked_input_image = ((1 - image_masks) * input_image)
# make visualization
visualization = torch.cat((ref_image, masked_input_image, decoded_image, input_image), dim=3) # 4*(B, 3, H, W) -> B, 3, H, W*4
B, C, H, W = visualization.shape
visualization = visualization.permute(1, 0, 2, 3).reshape(C, B*H, W)
visualization = torchvision.transforms.functional.to_pil_image(torch.clamp(visualization, 0, 1))
fname = "demo_output.png"
visualization.save(fname)
print('Visualization save in '+fname)
if __name__=="__main__":
main()
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