fix pylint error

Author: xinntao

basicsr/archs/arch_util.py

@@ -110,7 +110,7 @@ def __init__(self, scale, num_feat):
             m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
             m.append(nn.PixelShuffle(3))
         else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+            raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
         super(Upsample, self).__init__(*m)
 
 

basicsr/archs/swinir_arch.py

@@ -662,7 +662,7 @@ def __init__(self, scale, num_feat):
             m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
             m.append(nn.PixelShuffle(3))
         else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+            raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
         super(Upsample, self).__init__(*m)
 
 

basicsr/data/__init__.py

@@ -33,7 +33,7 @@ def build_dataset(dataset_opt):
     dataset_opt = deepcopy(dataset_opt)
     dataset = DATASET_REGISTRY.get(dataset_opt['type'])(dataset_opt)
     logger = get_root_logger()
-    logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} ' 'is built.')
+    logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} is built.')
     return dataset
 
 
@@ -77,7 +77,7 @@ def build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None,
     elif phase in ['val', 'test']:  # validation
         dataloader_args = dict(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
     else:
-        raise ValueError(f'Wrong dataset phase: {phase}. ' "Supported ones are 'train', 'val' and 'test'.")
+        raise ValueError(f"Wrong dataset phase: {phase}. Supported ones are 'train', 'val' and 'test'.")
 
     dataloader_args['pin_memory'] = dataset_opt.get('pin_memory', False)
     dataloader_args['persistent_workers'] = dataset_opt.get('persistent_workers', False)

basicsr/data/ffhq_dataset.py

@@ -37,7 +37,7 @@ def __init__(self, opt):
         if self.io_backend_opt['type'] == 'lmdb':
             self.io_backend_opt['db_paths'] = self.gt_folder
             if not self.gt_folder.endswith('.lmdb'):
-                raise ValueError("'dataroot_gt' should end with '.lmdb', " f'but received {self.gt_folder}')
+                raise ValueError("'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
             with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
                 self.paths = [line.split('.')[0] for line in fin]
         else:

basicsr/metrics/fid.py

@@ -74,7 +74,7 @@ def calculate_fid(mu1, sigma1, mu2, sigma2, eps=1e-6):
 
     # Product might be almost singular
     if not np.isfinite(cov_sqrt).all():
-        print('Product of cov matrices is singular. Adding {eps} to diagonal ' 'of cov estimates')
+        print('Product of cov matrices is singular. Adding {eps} to diagonal of cov estimates')
         offset = np.eye(sigma1.shape[0]) * eps
         cov_sqrt = linalg.sqrtm((sigma1 + offset) @ (sigma2 + offset))
 

basicsr/metrics/metric_util.py

@@ -21,7 +21,7 @@ def reorder_image(img, input_order='HWC'):
     """
 
     if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' "'HWC' and 'CHW'")
+        raise ValueError(f"Wrong input_order {input_order}. Supported input_orders are 'HWC' and 'CHW'")
     if len(img.shape) == 2:
         img = img[..., None]
     if input_order == 'CHW':

basicsr/metrics/psnr_ssim.py

@@ -26,7 +26,7 @@ def calculate_psnr(img, img2, crop_border, input_order='HWC', test_y_channel=Fal
 
     assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.')
     if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' '"HWC" and "CHW"')
+        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"')
     img = reorder_image(img, input_order=input_order)
     img2 = reorder_image(img2, input_order=input_order)
     img = img.astype(np.float64)
@@ -108,7 +108,7 @@ def calculate_ssim(img, img2, crop_border, input_order='HWC', test_y_channel=Fal
 
     assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.')
     if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' '"HWC" and "CHW"')
+        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"')
     img = reorder_image(img, input_order=input_order)
     img2 = reorder_image(img2, input_order=input_order)
     img = img.astype(np.float64)

basicsr/ops/dcn/deform_conv.py

@@ -44,7 +44,7 @@ def forward(ctx,
                 deformable_groups=1,
                 im2col_step=64):
         if input is not None and input.dim() != 4:
-            raise ValueError(f'Expected 4D tensor as input, got {input.dim()}' 'D tensor instead.')
+            raise ValueError(f'Expected 4D tensor as input, got {input.dim()}D tensor instead.')
         ctx.stride = _pair(stride)
         ctx.padding = _pair(padding)
         ctx.dilation = _pair(dilation)

basicsr/train.py

@@ -124,7 +124,7 @@ def train_pipeline(root_path):
     model = build_model(opt)
     if resume_state:  # resume training
         model.resume_training(resume_state)  # handle optimizers and schedulers
-        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.")
+        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, iter: {resume_state['iter']}.")
         start_epoch = resume_state['epoch']
         current_iter = resume_state['iter']
     else:
@@ -144,7 +144,7 @@ def train_pipeline(root_path):
         if opt['datasets']['train'].get('pin_memory') is not True:
             raise ValueError('Please set pin_memory=True for CUDAPrefetcher.')
     else:
-        raise ValueError(f'Wrong prefetch_mode {prefetch_mode}.' "Supported ones are: None, 'cuda', 'cpu'.")
+        raise ValueError(f"Wrong prefetch_mode {prefetch_mode}. Supported ones are: None, 'cuda', 'cpu'.")
 
     # training
     logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}')

basicsr/utils/face_util.py

@@ -9,7 +9,7 @@
 try:
     import dlib
 except ImportError:
-    print('Please install dlib before testing face restoration.' 'Reference:　https://github.com/davisking/dlib')
+    print('Please install dlib before testing face restoration. Reference:　https://github.com/davisking/dlib')
 
 
 class FaceRestorationHelper(object):

basicsr/utils/file_client.py

@@ -119,7 +119,7 @@ def get(self, filepath, client_key):
             client_key (str): Used for distinguishing different lmdb envs.
         """
         filepath = str(filepath)
-        assert client_key in self._client, (f'client_key {client_key} is not ' 'in lmdb clients.')
+        assert client_key in self._client, (f'client_key {client_key} is not in lmdb clients.')
         client = self._client[client_key]
         with client.begin(write=False) as txn:
             value_buf = txn.get(filepath.encode('ascii'))

basicsr/utils/flow_util.py

@@ -33,7 +33,7 @@ def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs):
                 raise IOError(f'Invalid flow file: {flow_path}')
             else:
                 if header != 'PIEH':
-                    raise IOError(f'Invalid flow file: {flow_path}, ' 'header does not contain PIEH')
+                    raise IOError(f'Invalid flow file: {flow_path}, header does not contain PIEH')
 
             w = np.fromfile(f, np.int32, 1).squeeze()
             h = np.fromfile(f, np.int32, 1).squeeze()

scripts/data_preparation/create_lmdb.py

@@ -157,8 +157,7 @@ def prepare_keys_vimeo90k(folder_path, train_list_path, mode):
     parser.add_argument(
         '--dataset',
         type=str,
-        help=("Options: 'DIV2K', 'REDS', 'Vimeo90K' "
-              'You may need to modify the corresponding configurations in codes.'))
+        help=("Options: 'DIV2K', 'REDS', 'Vimeo90K' You may need to modify the corresponding configurations in codes."))
     args = parser.parse_args()
     dataset = args.dataset.lower()
     if dataset == 'div2k':