There is a question that the data processing of super-resolution and the data processing of Image denoising?

[24werewolf]

There is a question that the data processing of super-resolution and the data processing of Image denoising? `################################################################################################## class DataLoaderTrain(Dataset): def init(self, rgb_dir, img_options=None, target_transform=None): super(DataLoaderTrain, self).init()

    self.target_transform = target_transform

    clean_files = sorted(os.listdir(os.path.join(rgb_dir, 'groundtruth')))
    noisy_files = sorted(os.listdir(os.path.join(rgb_dir, 'input')))

    self.clean_filenames = [os.path.join(rgb_dir, 'groundtruth', x) for x in clean_files if is_png_file(x)]
    self.noisy_filenames = [os.path.join(rgb_dir, 'input', x)       for x in noisy_files if is_png_file(x)]

    self.img_options = img_options

    self.tar_size = len(self.clean_filenames)  # get the size of target

def __len__(self):
    return self.tar_size

def __getitem__(self, index):
    tar_index   = index % self.tar_size
    clean = torch.from_numpy(np.float32(load_img(self.clean_filenames[tar_index])))
    noisy = torch.from_numpy(np.float32(load_img(self.noisy_filenames[tar_index])))

    clean = clean.permute(2,0,1)
    noisy = noisy.permute(2,0,1)

    clean_filename = os.path.split(self.clean_filenames[tar_index])[-1]
    noisy_filename = os.path.split(self.noisy_filenames[tar_index])[-1]

    #Crop Input and Target
    ps = self.img_options['patch_size']
    H = clean.shape[1]
    W = clean.shape[2]
    r = np.random.randint(0, H - ps)
    c = np.random.randint(0, W - ps)
    clean = clean[:, r:r + ps, c:c + ps]
    noisy = noisy[:, r:r + ps, c:c + ps]

    apply_trans = transforms_aug[random.getrandbits(3)]

    clean = getattr(augment, apply_trans)(clean)
    noisy = getattr(augment, apply_trans)(noisy)        

    return clean, noisy, clean_filename, noisy_filename`

[24werewolf]

For super-resolution, the shape of the Crop Input and target are not the same

[adityac8]

You can refer to #6