image_utils.py 文件源码

def preprocess_image(img, cuda=False):
    means=[0.485, 0.456, 0.406]
    stds=[0.229, 0.224, 0.225]

    preprocessed_img = img.copy()[: , :, ::-1]
    for i in range(3):
        preprocessed_img[:, :, i] = preprocessed_img[:, :, i] - means[i]
        preprocessed_img[:, :, i] = preprocessed_img[:, :, i] / stds[i]
    preprocessed_img = \
        np.ascontiguousarray(np.transpose(preprocessed_img, (2, 0, 1)))
    preprocessed_img = torch.from_numpy(preprocessed_img)
    preprocessed_img.unsqueeze_(0)
    if cuda:
        preprocessed_img = Variable(preprocessed_img.cuda(), requires_grad=True)
    else:
        preprocessed_img = Variable(preprocessed_img, requires_grad=True)

    return preprocessed_img