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|
|
| import torch |
| import numpy as np |
|
|
| IMG_NORM_MEAN = [0.485, 0.456, 0.406] |
| IMG_NORM_STD = [0.229, 0.224, 0.225] |
|
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|
|
| def normalize_rgb(img, imagenet_normalization=True): |
| """ |
| Args: |
| - img: np.array - (W,H,3) - np.uint8 - 0/255 |
| Return: |
| - img: np.array - (3,W,H) - np.float - -3/3 |
| """ |
| img = img.astype(np.float32) / 255. |
| img = np.transpose(img, (2,0,1)) |
| if imagenet_normalization: |
| img = (img - np.asarray(IMG_NORM_MEAN).reshape(3,1,1)) / np.asarray(IMG_NORM_STD).reshape(3,1,1) |
| img = img.astype(np.float32) |
| return img |
|
|
| def unpatch(data, patch_size=14, c=3, img_size=224): |
| |
| if len(data.shape) == 2: |
| c=1 |
| data = data[:,:,None].repeat([1,1,patch_size**2]) |
|
|
| B,N,HWC = data.shape |
| HW = patch_size**2 |
| c = int(HWC / HW) |
| h = w = int(N**.5) |
| p = q = int(HW**.5) |
| data = data.reshape([B,h,w,p,q,c]) |
| data = torch.einsum('nhwpqc->nchpwq', data) |
| return data.reshape([B,c,img_size,img_size]) |
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