Linting (#207)

* add pylintrc

Signed-off-by: lizz <lizz@sensetime.com>

* python3 style super

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* add

Signed-off-by: lizz <lizz@sensetime.com>

* lint

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* no (object)

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* tiny

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* ha

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* typos

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* typo

Signed-off-by: lizz <lizz@sensetime.com>

* typo

Signed-off-by: lizz <lizz@sensetime.com>

* lint

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* lint

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* more lint

Signed-off-by: lizz <lizz@sensetime.com>

* Fix out_channels unused bug in EDVRNet

Signed-off-by: lizz <lizz@sensetime.com>

* lint

Signed-off-by: lizz <lizz@sensetime.com>

mmedit/__init__.py

@@ -7,15 +7,15 @@
 
 
 def digit_version(version_str):
-    digit_version = []
+    digit_ver = []
     for x in version_str.split('.'):
         if x.isdigit():
-            digit_version.append(int(x))
+            digit_ver.append(int(x))
         elif x.find('rc') != -1:
             patch_version = x.split('rc')
-            digit_version.append(int(patch_version[0]) - 1)
-            digit_version.append(int(patch_version[1]))
-    return digit_version
+            digit_ver.append(int(patch_version[0]) - 1)
+            digit_ver.append(int(patch_version[1]))
+    return digit_ver
 
 
 mmcv_min_version = digit_version(MMCV_MIN)

mmedit/apis/test.py

@@ -162,21 +162,21 @@ def collect_results_cpu(result_part, size, tmpdir=None):
     # collect all parts
     if rank != 0:
         return None
-    else:
-        # load results of all parts from tmp dir
-        part_list = []
-        for i in range(world_size):
-            part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
-            part_list.append(mmcv.load(part_file))
-        # sort the results
-        ordered_results = []
-        for res in zip(*part_list):
-            ordered_results.extend(list(res))
-        # the dataloader may pad some samples
-        ordered_results = ordered_results[:size]
-        # remove tmp dir
-        shutil.rmtree(tmpdir)
-        return ordered_results
+
+    # load results of all parts from tmp dir
+    part_list = []
+    for i in range(world_size):
+        part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
+        part_list.append(mmcv.load(part_file))
+    # sort the results
+    ordered_results = []
+    for res in zip(*part_list):
+        ordered_results.extend(list(res))
+    # the dataloader may pad some samples
+    ordered_results = ordered_results[:size]
+    # remove tmp dir
+    shutil.rmtree(tmpdir)
+    return ordered_results
 
 
 def collect_results_gpu(result_part, size):
@@ -211,15 +211,16 @@ def collect_results_gpu(result_part, size):
     # gather all result part
     dist.all_gather(part_recv_list, part_send)
 
-    if rank == 0:
-        part_list = []
-        for recv, shape in zip(part_recv_list, shape_list):
-            part_list.append(
-                pickle.loads(recv[:shape[0]].cpu().numpy().tobytes()))
-        # sort the results
-        ordered_results = []
-        for res in zip(*part_list):
-            ordered_results.extend(list(res))
-        # the dataloader may pad some samples
-        ordered_results = ordered_results[:size]
-        return ordered_results
+    if rank != 0:
+        return None
+
+    part_list = []
+    for recv, shape in zip(part_recv_list, shape_list):
+        part_list.append(pickle.loads(recv[:shape[0]].cpu().numpy().tobytes()))
+    # sort the results
+    ordered_results = []
+    for res in zip(*part_list):
+        ordered_results.extend(list(res))
+    # the dataloader may pad some samples
+    ordered_results = ordered_results[:size]
+    return ordered_results

mmedit/core/distributed_wrapper.py

@@ -53,7 +53,7 @@ def __init__(self,
                  broadcast_buffers=False,
                  find_unused_parameters=False,
                  **kwargs):
-        super(DistributedDataParallelWrapper, self).__init__()
+        super().__init__()
         assert len(device_ids) == 1, (
             'Currently, DistributedDataParallelWrapper only supports one'
             'single CUDA device for each process.'

mmedit/core/evaluation/eval_hooks.py

@@ -80,8 +80,7 @@ def __init__(self,
                  interval=1,
                  gpu_collect=False,
                  **eval_kwargs):
-        super(DistEvalIterHook, self).__init__(dataloader, interval,
-                                               **eval_kwargs)
+        super().__init__(dataloader, interval, **eval_kwargs)
         self.gpu_collect = gpu_collect
 
     def after_train_iter(self, runner):

mmedit/core/evaluation/metric_utils.py

@@ -61,7 +61,9 @@ def gauss_filter(sigma, epsilon=1e-2):
 def gauss_gradient(img, sigma):
     """Gaussian gradient.
 
-    From https://www.mathworks.com/matlabcentral/mlc-downloads/downloads/submissions/8060/versions/2/previews/gaussgradient/gaussgradient.m/index.html  # noqa
+    From https://www.mathworks.com/matlabcentral/mlc-downloads/downloads/
+    submissions/8060/versions/2/previews/gaussgradient/gaussgradient.m/
+    index.html
 
     Args:
         img (ndarray): Input image.

mmedit/core/evaluation/metrics.py

@@ -19,8 +19,8 @@ def sad(alpha, trimap, pred_alpha):
     assert (pred_alpha[trimap == 255] == 255).all()
     alpha = alpha.astype(np.float64) / 255
     pred_alpha = pred_alpha.astype(np.float64) / 255
-    sad = np.abs(pred_alpha - alpha).sum() / 1000
-    return sad
+    sad_result = np.abs(pred_alpha - alpha).sum() / 1000
+    return sad_result
 
 
 def mse(alpha, trimap, pred_alpha):
@@ -35,10 +35,10 @@ def mse(alpha, trimap, pred_alpha):
     pred_alpha = pred_alpha.astype(np.float64) / 255
     weight_sum = (trimap == 128).sum()
     if weight_sum != 0:
-        mse = ((pred_alpha - alpha)**2).sum() / weight_sum
+        mse_result = ((pred_alpha - alpha)**2).sum() / weight_sum
     else:
-        mse = 0
-    return mse
+        mse_result = 0
+    return mse_result
 
 
 def gradient_error(alpha, trimap, pred_alpha, sigma=1.4):
@@ -100,7 +100,6 @@ def connectivity(alpha, trimap, pred_alpha, step=0.1):
     alpha = alpha.astype(np.float32) / 255
     pred_alpha = pred_alpha.astype(np.float32) / 255
 
-    height, width = alpha.shape
     thresh_steps = np.arange(0, 1 + step, step)
     round_down_map = -np.ones_like(alpha)
     for i in range(1, len(thresh_steps)):
@@ -196,10 +195,10 @@ def psnr(img1, img2, crop_border=0, input_order='HWC'):
         img1 = img1[crop_border:-crop_border, crop_border:-crop_border, None]
         img2 = img2[crop_border:-crop_border, crop_border:-crop_border, None]
 
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
+    mse_value = np.mean((img1 - img2)**2)
+    if mse_value == 0:
         return float('inf')
-    return 20. * np.log10(255. / np.sqrt(mse))
+    return 20. * np.log10(255. / np.sqrt(mse_value))
 
 
 def _ssim(img1, img2):
@@ -280,7 +279,7 @@ def ssim(img1, img2, crop_border=0, input_order='HWC'):
     return np.array(ssims).mean()
 
 
-class L1Evaluation(object):
+class L1Evaluation:
     """L1 evaluation metric.
 
     Args:
@@ -347,8 +346,8 @@ def compute_feature(block):
     # the products of pairs of adjacent coefficients computed along
     # horizontal, vertical and diagonal orientations.
     shifts = [[0, 1], [1, 0], [1, 1], [1, -1]]
-    for i in range(len(shifts)):
-        shifted_block = np.roll(block, shifts[i], axis=(0, 1))
+    for shift in shifts:
+        shifted_block = np.roll(block, shift, axis=(0, 1))
         alpha, beta_l, beta_r = estimate_aggd_param(block * shifted_block)
         mean = (beta_r - beta_l) * (gamma(2 / alpha) / gamma(1 / alpha))
         feat.extend([alpha, mean, beta_l, beta_r])
@@ -408,7 +407,7 @@ def niqe_core(img,
         feat = []
         for idx_w in range(num_block_w):
             for idx_h in range(num_block_h):
-                # process ecah block
+                # process each block
                 block = img_nomalized[idx_h * block_size_h //
                                       scale:(idx_h + 1) * block_size_h //
                                       scale, idx_w * block_size_w //

mmedit/core/mask.py

@@ -282,7 +282,7 @@ def random_irregular_mask(img_shape,
                 angle = 2 * math.pi - angle
             length = length_list[direct_n]
             brush_w = brush_width_list[direct_n]
-            # compute end point accoriding to the random angle
+            # compute end point according to the random angle
             end_x = (start_x + length * np.sin(angle)).astype(np.int32)
             end_y = (start_y + length * np.cos(angle)).astype(np.int32)
 

mmedit/core/optimizer/builder.py

@@ -46,11 +46,12 @@ def build_optimizers(model, cfgs):
     for key, cfg in cfgs.items():
         if not isinstance(cfg, dict):
             is_dict_of_dict = False
+
     if is_dict_of_dict:
         for key, cfg in cfgs.items():
             cfg_ = cfg.copy()
             module = getattr(model, key)
             optimizers[key] = build_optimizer(module, cfg_)
         return optimizers
-    else:
-        return build_optimizer(model, cfgs)
+
+    return build_optimizer(model, cfgs)

mmedit/core/scheduler/lr_updater.py

@@ -19,7 +19,7 @@ class LinearLrUpdaterHook(LrUpdaterHook):
     """
 
     def __init__(self, target_lr=0, start=0, interval=1, **kwargs):
-        super(LinearLrUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
         self.target_lr = target_lr
         self.start = start
         self.interval = interval
@@ -41,10 +41,11 @@ def get_lr(self, runner, base_lr):
             progress = runner.iter
             max_progress = runner.max_iters
         assert max_progress >= self.start
+
         if max_progress == self.start:
             return base_lr
-        else:
-            # Before 'start', fix lr; After 'start', linearly update lr.
-            factor = (max(0, progress - self.start) // self.interval) / (
-                (max_progress - self.start) // self.interval)
-            return base_lr + (self.target_lr - base_lr) * factor
+
+        # Before 'start', fix lr; After 'start', linearly update lr.
+        factor = (max(0, progress - self.start) // self.interval) / (
+            (max_progress - self.start) // self.interval)
+        return base_lr + (self.target_lr - base_lr) * factor

mmedit/datasets/base_dataset.py

@@ -22,7 +22,7 @@ class BaseDataset(Dataset, metaclass=ABCMeta):
     """
 
     def __init__(self, pipeline, test_mode=False):
-        super(BaseDataset, self).__init__()
+        super().__init__()
         self.test_mode = test_mode
         self.pipeline = Compose(pipeline)
 
@@ -71,7 +71,7 @@ def __getitem__(self, idx):
         Args:
             idx (int): Index for getting each item.
         """
-        if not self.test_mode:
-            return self.prepare_train_data(idx)
-        else:
+        if self.test_mode:
             return self.prepare_test_data(idx)
+
+        return self.prepare_train_data(idx)

mmedit/datasets/base_matting_dataset.py

@@ -10,7 +10,7 @@ class BaseMattingDataset(BaseDataset):
     """
 
     def __init__(self, ann_file, pipeline, data_prefix=None, test_mode=False):
-        super(BaseMattingDataset, self).__init__(pipeline, test_mode)
+        super().__init__(pipeline, test_mode)
         self.ann_file = str(ann_file)
         self.data_prefix = str(data_prefix)
         self.data_infos = self.load_annotations()

mmedit/datasets/base_sr_dataset.py

@@ -16,7 +16,7 @@ class BaseSRDataset(BaseDataset):
     """
 
     def __init__(self, pipeline, scale, test_mode=False):
-        super(BaseSRDataset, self).__init__(pipeline, test_mode)
+        super().__init__(pipeline, test_mode)
         self.scale = scale
 
     @staticmethod

mmedit/datasets/dataset_wrappers.py

@@ -2,7 +2,7 @@
 
 
 @DATASETS.register_module()
-class RepeatDataset(object):
+class RepeatDataset:
     """A wrapper of repeated dataset.
 
     The length of repeated dataset will be `times` larger than the original

mmedit/datasets/generation_paired_dataset.py

@@ -21,7 +21,7 @@ class GenerationPairedDataset(BaseGenerationDataset):
     """
 
     def __init__(self, dataroot, pipeline, test_mode=False):
-        super(GenerationPairedDataset, self).__init__(pipeline, test_mode)
+        super().__init__(pipeline, test_mode)
         phase = 'test' if test_mode else 'train'
         self.dataroot = osp.join(str(dataroot), phase)
         self.data_infos = self.load_annotations()

mmedit/datasets/generation_unpaired_dataset.py

@@ -25,7 +25,7 @@ class GenerationUnpairedDataset(BaseGenerationDataset):
     """
 
     def __init__(self, dataroot, pipeline, test_mode=False):
-        super(GenerationUnpairedDataset, self).__init__(pipeline, test_mode)
+        super().__init__(pipeline, test_mode)
         phase = 'test' if test_mode else 'train'
         self.dataroot_a = osp.join(str(dataroot), phase + 'A')
         self.dataroot_b = osp.join(str(dataroot), phase + 'B')

mmedit/datasets/img_inpainting_dataset.py

@@ -10,7 +10,7 @@ class ImgInpaintingDataset(BaseDataset):
     """
 
     def __init__(self, ann_file, pipeline, data_prefix=None, test_mode=False):
-        super(ImgInpaintingDataset, self).__init__(pipeline, test_mode)
+        super().__init__(pipeline, test_mode)
         self.ann_file = str(ann_file)
         self.data_prefix = str(data_prefix)
         self.data_infos = self.load_annotations()