'Refactored by Sourcery'

Author: Sourcery AI

datasets/cityscapes.py

@@ -90,15 +90,15 @@ def __init__(self, root, split='train', mode='fine', target_type='semantic', tra
         if not os.path.isdir(self.images_dir) or not os.path.isdir(self.targets_dir):
             raise RuntimeError('Dataset not found or incomplete. Please make sure all required folders for the'
                                ' specified "split" and "mode" are inside the "root" directory')
-        
+
         for city in os.listdir(self.images_dir):
             img_dir = os.path.join(self.images_dir, city)
             target_dir = os.path.join(self.targets_dir, city)
 
             for file_name in os.listdir(img_dir):
                 self.images.append(os.path.join(img_dir, file_name))
-                target_name = '{}_{}'.format(file_name.split('_leftImg8bit')[0],
-                                             self._get_target_suffix(self.mode, self.target_type))
+                target_name = f"{file_name.split('_leftImg8bit')[0]}_{self._get_target_suffix(self.mode, self.target_type)}"
+
                 self.targets.append(os.path.join(target_dir, target_name))
 
     @classmethod
@@ -135,13 +135,13 @@ def _load_json(self, path):
         return data
 
     def _get_target_suffix(self, mode, target_type):
-        if target_type == 'instance':
-            return '{}_instanceIds.png'.format(mode)
-        elif target_type == 'semantic':
-            return '{}_labelIds.png'.format(mode)
-        elif target_type == 'color':
-            return '{}_color.png'.format(mode)
-        elif target_type == 'polygon':
-            return '{}_polygons.json'.format(mode)
+        if target_type == 'color':
+            return f'{mode}_color.png'
         elif target_type == 'depth':
-            return '{}_disparity.png'.format(mode)
+            return f'{mode}_disparity.png'
+        elif target_type == 'instance':
+            return f'{mode}_instanceIds.png'
+        elif target_type == 'polygon':
+            return f'{mode}_polygons.json'
+        elif target_type == 'semantic':
+            return f'{mode}_labelIds.png'

datasets/utils.py

@@ -38,9 +38,7 @@ def makedir_exist_ok(dirpath):
     try:
         os.makedirs(dirpath)
     except OSError as e:
-        if e.errno == errno.EEXIST:
-            pass
-        else:
+        if e.errno != errno.EEXIST:
             raise
 
 
@@ -63,10 +61,10 @@ def download_url(url, root, filename=None, md5=None):
 
     # downloads file
     if os.path.isfile(fpath) and check_integrity(fpath, md5):
-        print('Using downloaded and verified file: ' + fpath)
+        print(f'Using downloaded and verified file: {fpath}')
     else:
         try:
-            print('Downloading ' + url + ' to ' + fpath)
+            print(f'Downloading {url} to {fpath}')
             urllib.request.urlretrieve(
                 url, fpath,
                 reporthook=gen_bar_updater(tqdm(unit='B', unit_scale=True))

datasets/voc.py

@@ -93,14 +93,14 @@ def __init__(self,
         if year=='2012_aug':
             is_aug = True
             year = '2012'
-        
+
         self.root = os.path.expanduser(root)
         self.year = year
         self.url = DATASET_YEAR_DICT[year]['url']
         self.filename = DATASET_YEAR_DICT[year]['filename']
         self.md5 = DATASET_YEAR_DICT[year]['md5']
         self.transform = transform
-        
+
         self.image_set = image_set
         base_dir = DATASET_YEAR_DICT[year]['base_dir']
         voc_root = os.path.join(self.root, base_dir)
@@ -112,7 +112,7 @@ def __init__(self,
         if not os.path.isdir(voc_root):
             raise RuntimeError('Dataset not found or corrupted.' +
                                ' You can use download=True to download it')
-        
+
         if is_aug and image_set=='train':
             mask_dir = os.path.join(voc_root, 'SegmentationClassAug')
             assert os.path.exists(mask_dir), "SegmentationClassAug not found, please refer to README.md and prepare it manually"
@@ -129,9 +129,9 @@ def __init__(self,
 
         with open(os.path.join(split_f), "r") as f:
             file_names = [x.strip() for x in f.readlines()]
-        
-        self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]
-        self.masks = [os.path.join(mask_dir, x + ".png") for x in file_names]
+
+        self.images = [os.path.join(image_dir, f'{x}.jpg') for x in file_names]
+        self.masks = [os.path.join(mask_dir, f'{x}.png') for x in file_names]
         assert (len(self.images) == len(self.masks))
 
     def __getitem__(self, index):

main.py

@@ -32,10 +32,15 @@ def get_argparser():
                         help="num classes (default: None)")
 
     # Deeplab Options
-    available_models = sorted(name for name in network.modeling.__dict__ if name.islower() and \
-                              not (name.startswith("__") or name.startswith('_')) and callable(
-                              network.modeling.__dict__[name])
-                              )
+    available_models = sorted(
+        name
+        for name in network.modeling.__dict__
+        if name.islower()
+        and not name.startswith("__")
+        and not name.startswith('_')
+        and callable(network.modeling.__dict__[name])
+    )
+
     parser.add_argument("--model", type=str, default='deeplabv3plus_mobilenet',
                         choices=available_models, help='model name')
     parser.add_argument("--separable_conv", action='store_true', default=False,
@@ -281,7 +286,7 @@ def save_ckpt(path):
             "scheduler_state": scheduler.state_dict(),
             "best_score": best_score,
         }, path)
-        print("Model saved as %s" % path)
+        print(f"Model saved as {path}")
 
     utils.mkdir('checkpoints')
     # Restore

metrics/stream_metrics.py

@@ -48,11 +48,10 @@ def to_str(results):
 
     def _fast_hist(self, label_true, label_pred):
         mask = (label_true >= 0) & (label_true < self.n_classes)
-        hist = np.bincount(
+        return np.bincount(
             self.n_classes * label_true[mask].astype(int) + label_pred[mask],
             minlength=self.n_classes ** 2,
         ).reshape(self.n_classes, self.n_classes)
-        return hist
 
     def get_results(self):
         """Returns accuracy score evaluation result.
@@ -85,7 +84,7 @@ def reset(self):
 class AverageMeter(object):
     """Computes average values"""
     def __init__(self):
-        self.book = dict()
+        self.book = {}
 
     def reset_all(self):
         self.book.clear()

network/_deeplab.py

@@ -134,11 +134,13 @@ class ASPP(nn.Module):
     def __init__(self, in_channels, atrous_rates):
         super(ASPP, self).__init__()
         out_channels = 256
-        modules = []
-        modules.append(nn.Sequential(
-            nn.Conv2d(in_channels, out_channels, 1, bias=False),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True)))
+        modules = [
+            nn.Sequential(
+                nn.Conv2d(in_channels, out_channels, 1, bias=False),
+                nn.BatchNorm2d(out_channels),
+                nn.ReLU(inplace=True),
+            )
+        ]
 
         rate1, rate2, rate3 = tuple(atrous_rates)
         modules.append(ASPPConv(in_channels, out_channels, rate1))
@@ -155,9 +157,7 @@ def __init__(self, in_channels, atrous_rates):
             nn.Dropout(0.1),)
 
     def forward(self, x):
-        res = []
-        for conv in self.convs:
-            res.append(conv(x))
+        res = [conv(x) for conv in self.convs]
         res = torch.cat(res, dim=1)
         return self.project(res)
 

network/backbone/hrnetv2.py

@@ -118,16 +118,28 @@ def __init__(self, stage, output_branches, c):
                         nn.Upsample(scale_factor=(2.0 ** (branch_number - branch_output_number)), mode='nearest'),
                     ))
                 elif branch_number < branch_output_number:
-                    downsampling_fusion = []
-                    for _ in range(branch_output_number - branch_number - 1):
-                        downsampling_fusion.append(nn.Sequential(
-                            nn.Conv2d(c * (2 ** branch_number), c * (2 ** branch_number), kernel_size=3, stride=2,
-                                      padding=1,
-                                      bias=False),
-                            nn.BatchNorm2d(c * (2 ** branch_number), eps=1e-05, momentum=0.1, affine=True,
-                                           track_running_stats=True),
+                    downsampling_fusion = [
+                        nn.Sequential(
+                            nn.Conv2d(
+                                c * (2 ** branch_number),
+                                c * (2 ** branch_number),
+                                kernel_size=3,
+                                stride=2,
+                                padding=1,
+                                bias=False,
+                            ),
+                            nn.BatchNorm2d(
+                                c * (2 ** branch_number),
+                                eps=1e-05,
+                                momentum=0.1,
+                                affine=True,
+                                track_running_stats=True,
+                            ),
                             nn.ReLU(inplace=True),
-                        ))
+                        )
+                        for _ in range(branch_output_number - branch_number - 1)
+                    ]
+
                     downsampling_fusion.append(nn.Sequential(
                         nn.Conv2d(c * (2 ** branch_number), c * (2 ** branch_output_number), kernel_size=3,
                                   stride=2, padding=1,
@@ -224,15 +236,17 @@ def __init__(self, c=48, num_blocks=[1, 4, 3], num_classes=1000):
 
         # Classifier (extra module if want to use for classification):
         # pool, reduce dimensionality, flatten, connect to linear layer for classification:
-        out_channels = sum([c * 2 ** i for i in range(len(num_blocks)+1)])  # total output channels of HRNetV2
+        out_channels = sum(c * 2 ** i for i in range(len(num_blocks)+1))
         pool_feature_map = 8
         self.bn_classifier = nn.Sequential(
             nn.Conv2d(out_channels, out_channels // 4, kernel_size=1, bias=False),
-            nn.BatchNorm2d(out_channels // 4, eps=1e-05, affine=True, track_running_stats=True),
+            nn.BatchNorm2d(
+                out_channels // 4, eps=1e-05, affine=True, track_running_stats=True
+            ),
             nn.ReLU(inplace=True),
             nn.AdaptiveAvgPool2d(pool_feature_map),
             nn.Flatten(),
-            nn.Linear(pool_feature_map * pool_feature_map * (out_channels // 4), num_classes),
+            nn.Linear(pool_feature_map ** 2 * (out_channels // 4), num_classes),
         )
 
     @staticmethod

network/backbone/mobilenetv2.py

@@ -76,10 +76,7 @@ def __init__(self, inp, oup, stride, dilation, expand_ratio):
 
     def forward(self, x):
         x_pad = F.pad(x, self.input_padding)
-        if self.use_res_connect:
-            return x + self.conv(x_pad)
-        else:
-            return self.conv(x_pad)
+        return x + self.conv(x_pad) if self.use_res_connect else self.conv(x_pad)
 
 class MobileNetV2(nn.Module):
     def __init__(self, num_classes=1000, output_stride=8, width_mult=1.0, inverted_residual_setting=None, round_nearest=8):
@@ -95,10 +92,7 @@ def __init__(self, num_classes=1000, output_stride=8, width_mult=1.0, inverted_r
         """
         super(MobileNetV2, self).__init__()
         block = InvertedResidual
-        input_channel = 32
-        last_channel = 1280
         self.output_stride = output_stride
-        current_stride = 1
         if inverted_residual_setting is None:
             inverted_residual_setting = [
                 # t, c, n, s
@@ -113,14 +107,18 @@ def __init__(self, num_classes=1000, output_stride=8, width_mult=1.0, inverted_r
 
         # only check the first element, assuming user knows t,c,n,s are required
         if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
-            raise ValueError("inverted_residual_setting should be non-empty "
-                             "or a 4-element list, got {}".format(inverted_residual_setting))
+            raise ValueError(
+                f"inverted_residual_setting should be non-empty or a 4-element list, got {inverted_residual_setting}"
+            )
+
 
+        input_channel = 32
         # building first layer
         input_channel = _make_divisible(input_channel * width_mult, round_nearest)
+        last_channel = 1280
         self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
         features = [ConvBNReLU(3, input_channel, stride=2)]
-        current_stride *= 2
+        current_stride = 1 * 2
         dilation=1
         previous_dilation = 1
 

network/backbone/resnet.py

@@ -135,8 +135,10 @@ def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
             # the 2x2 stride with a dilated convolution instead
             replace_stride_with_dilation = [False, False, False]
         if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+            raise ValueError(
+                f"replace_stride_with_dilation should be None or a 3-element tuple, got {replace_stride_with_dilation}"
+            )
+
         self.groups = groups
         self.base_width = width_per_group
         self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
@@ -184,14 +186,31 @@ def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
                 norm_layer(planes * block.expansion),
             )
 
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
-                            self.base_width, previous_dilation, norm_layer))
+        layers = [
+            block(
+                self.inplanes,
+                planes,
+                stride,
+                downsample,
+                self.groups,
+                self.base_width,
+                previous_dilation,
+                norm_layer,
+            )
+        ]
+
         self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(block(self.inplanes, planes, groups=self.groups,
-                                base_width=self.base_width, dilation=self.dilation,
-                                norm_layer=norm_layer))
+        layers.extend(
+            block(
+                self.inplanes,
+                planes,
+                groups=self.groups,
+                base_width=self.base_width,
+                dilation=self.dilation,
+                norm_layer=norm_layer,
+            )
+            for _ in range(1, blocks)
+        )
 
         return nn.Sequential(*layers)