Add PyUpgrade pre-commit hook

.gitignore

@@ -107,7 +107,7 @@ venv.bak/
 
 # cython generated cpp
 !data/dict
-data/*
+data
 .vscode
 .idea
 

.pre-commit-config.yaml

@@ -43,6 +43,11 @@ repos:
     hooks:
       - id: docformatter
         args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v2.32.1
+    hooks:
+      - id: pyupgrade
+        args: ["--py36-plus"]
   - repo: https://github.com/open-mmlab/pre-commit-hooks
     rev: v0.2.0  # Use the ref you want to point at
     hooks:

docs/en/conf.py

@@ -27,7 +27,7 @@ author = 'OpenMMLab'
 
 # The full version, including alpha/beta/rc tags
 version_file = '../../mmocr/version.py'
-with open(version_file, 'r') as f:
+with open(version_file) as f:
     exec(compile(f.read(), version_file, 'exec'))
 __version__ = locals()['__version__']
 release = __version__

docs/en/stats.py

@@ -21,7 +21,7 @@ files = sorted(glob.glob('*_models.md'))
 stats = []
 
 for f in files:
-    with open(f, 'r') as content_file:
+    with open(f) as content_file:
         content = content_file.read()
 
     # Remove the blackquote notation from the paper link under the title
@@ -39,9 +39,8 @@ for f in files:
     exclude_expr = ''.join(f'(?!{s})' for s in exclude_papertype)
     expr = rf'<!-- \[{exclude_expr}([A-Z]+?)\] -->'\
         r'\s*\n.*?\btitle\s*=\s*{(.*?)}'
-    papers = set(
-        (papertype, titlecase.titlecase(paper.lower().strip()))
-        for (papertype, paper) in re.findall(expr, content, re.DOTALL))
+    papers = {(papertype, titlecase.titlecase(paper.lower().strip()))
+              for (papertype, paper) in re.findall(expr, content, re.DOTALL)}
     print(papers)
     # paper links
     revcontent = '\n'.join(list(reversed(content.splitlines())))
@@ -56,13 +55,17 @@ for f in files:
     paperlist = '\n'.join(
         sorted(f'    - [{t}] {paperlinks[x]}' for t, x in papers))
     # count configs
-    configs = set(x.lower().strip()
-                  for x in re.findall(r'https.*configs/.*\.py', content))
+    configs = {
+        x.lower().strip()
+        for x in re.findall(r'https.*configs/.*\.py', content)
+    }
 
     # count ckpts
-    ckpts = set(x.lower().strip()
-                for x in re.findall(r'https://download.*\.pth', content)
-                if 'mmocr' in x)
+    ckpts = {
+        x.lower().strip()
+        for x in re.findall(r'https://download.*\.pth', content)
+        if 'mmocr' in x
+    }
 
     statsmsg = f"""
 ## [{title}]({f})

docs/zh_cn/conf.py

@@ -27,7 +27,7 @@ author = 'OpenMMLab'
 
 # The full version, including alpha/beta/rc tags
 version_file = '../../mmocr/version.py'
-with open(version_file, 'r') as f:
+with open(version_file) as f:
     exec(compile(f.read(), version_file, 'exec'))
 __version__ = locals()['__version__']
 release = __version__

docs/zh_cn/stats.py

@@ -21,7 +21,7 @@ files = sorted(glob.glob('*_models.md'))
 stats = []
 
 for f in files:
-    with open(f, 'r') as content_file:
+    with open(f) as content_file:
         content = content_file.read()
 
     # Remove the blackquote notation from the paper link under the title
@@ -39,9 +39,8 @@ for f in files:
     exclude_expr = ''.join(f'(?!{s})' for s in exclude_papertype)
     expr = rf'<!-- \[{exclude_expr}([A-Z]+?)\] -->'\
         r'\s*\n.*?\btitle\s*=\s*{(.*?)}'
-    papers = set(
-        (papertype, titlecase.titlecase(paper.lower().strip()))
-        for (papertype, paper) in re.findall(expr, content, re.DOTALL))
+    papers = {(papertype, titlecase.titlecase(paper.lower().strip()))
+              for (papertype, paper) in re.findall(expr, content, re.DOTALL)}
     print(papers)
     # paper links
     revcontent = '\n'.join(list(reversed(content.splitlines())))
@@ -56,13 +55,17 @@ for f in files:
     paperlist = '\n'.join(
         sorted(f'    - [{t}] {paperlinks[x]}' for t, x in papers))
     # count configs
-    configs = set(x.lower().strip()
-                  for x in re.findall(r'https.*configs/.*\.py', content))
+    configs = {
+        x.lower().strip()
+        for x in re.findall(r'https.*configs/.*\.py', content)
+    }
 
     # count ckpts
-    ckpts = set(x.lower().strip()
-                for x in re.findall(r'https://download.*\.pth', content)
-                if 'mmocr' in x)
+    ckpts = {
+        x.lower().strip()
+        for x in re.findall(r'https://download.*\.pth', content)
+        if 'mmocr' in x
+    }
 
     statsmsg = f"""
 ## [{title}]({f})

mmocr/core/evaluation/ocr_metric.py

@@ -118,12 +118,12 @@ def eval_ocr_metric(pred_texts, gt_texts, metric='acc'):
             'word_acc', 'word_acc_ignore_case', 'word_acc_ignore_case_symbol',
             'char_recall', 'char_precision', 'one_minus_ned'
         ]
-    metric = set([metric]) if isinstance(metric, str) else set(metric)
+    metric = {metric} if isinstance(metric, str) else set(metric)
 
-    supported_metrics = set([
+    supported_metrics = {
         'word_acc', 'word_acc_ignore_case', 'word_acc_ignore_case_symbol',
         'char_recall', 'char_precision', 'one_minus_ned'
-    ])
+    }
     assert metric.issubset(supported_metrics)
 
     match_res = count_matches(pred_texts, gt_texts)
@@ -160,6 +160,6 @@ def eval_ocr_metric(pred_texts, gt_texts, metric='acc'):
         eval_res['1-N.E.D'] = 1.0 - match_res['ned']
 
     for key, value in eval_res.items():
-        eval_res[key] = float('{:.4f}'.format(value))
+        eval_res[key] = float(f'{value:.4f}')
 
     return eval_res

mmocr/core/visualize.py

@@ -406,14 +406,14 @@ def imshow_node(img,
             True,
             color=(255, 255, 0),
             thickness=1)
-        x_min = int(min([point[0] for point in new_box]))
-        y_min = int(min([point[1] for point in new_box]))
+        x_min = int(min(point[0] for point in new_box))
+        y_min = int(min(point[1] for point in new_box))
 
         # text
         pred_label = str(node_pred_label[i])
         if pred_label in idx_to_cls:
             pred_label = idx_to_cls[pred_label]
-        pred_score = '{:.2f}'.format(node_pred_score[i])
+        pred_score = f'{node_pred_score[i]:.2f}'
         text = pred_label + '(' + pred_score + ')'
         texts.append(text)
 
@@ -635,7 +635,7 @@ def is_contain_chinese(check_str):
     Return True if contains Chinese, else False.
     """
     for ch in check_str:
-        if u'\u4e00' <= ch <= u'\u9fff':
+        if '\u4e00' <= ch <= '\u9fff':
             return True
     return False
 
@@ -800,7 +800,7 @@ def draw_edge_result(img, result, edge_thresh=0.5, keynode_thresh=0.5):
                 (pos_current[0] + bbox_x1 + dist_key_to_value - 5) / 2.)
             score_pos_y = int((pos_current[1] + bbox_y1 + 10) / 2.)
         # draw edge score
-        cv2.putText(pred_edge_img, '{:.2f}'.format(pair_score),
+        cv2.putText(pred_edge_img, f'{pair_score:.2f}',
                     (score_pos_x, score_pos_y), cv2.FONT_HERSHEY_COMPLEX, 0.4,
                     score_color)
         # draw text for value

mmocr/datasets/kie_dataset.py

@@ -208,7 +208,7 @@ class KIEDataset(BaseDataset):
 
     def pad_text_indices(self, text_inds):
         """Pad text index to same length."""
-        max_len = max([len(text_ind) for text_ind in text_inds])
+        max_len = max(len(text_ind) for text_ind in text_inds)
         padded_text_inds = -np.ones((len(text_inds), max_len), np.int32)
         for idx, text_ind in enumerate(text_inds):
             padded_text_inds[idx, :len(text_ind)] = np.array(text_ind)

mmocr/datasets/pipelines/loading.py

@@ -138,7 +138,7 @@ class LoadImageFromNdarray(LoadImageFromFile):
 
 
 @TRANSFORMS.register_module()
-class LoadImageFromLMDB(object):
+class LoadImageFromLMDB:
     """Load an image from lmdb file.
 
     Similar with :obj:'LoadImageFromFile', but the image read from
@@ -169,8 +169,8 @@ class LoadImageFromLMDB(object):
             imgbuf = txn.get(img_key.encode('utf-8'))
             try:
                 img = mmcv.imfrombytes(imgbuf, flag=self.color_type)
-            except IOError:
-                print('Corrupted image for {}'.format(img_key))
+            except OSError:
+                print(f'Corrupted image for {img_key}')
                 return None
 
             results['filename'] = img_key

mmocr/datasets/uniform_concat_dataset.py

@@ -79,7 +79,7 @@ class UniformConcatDataset(ConcatDataset):
         self.show_mean_scores = show_mean_scores
         if show_mean_scores is True or show_mean_scores == 'auto' and len(
                 self.datasets) > 1:
-            if len(set([type(ds) for ds in self.datasets])) != 1:
+            if len({type(ds) for ds in self.datasets}) != 1:
                 raise NotImplementedError(
                     'To compute mean evaluation scores, all datasets'
                     'must have the same type')

mmocr/datasets/utils/backend.py

@@ -34,8 +34,7 @@ class LmdbAnnFileBackend:
         with env.begin(write=False) as txn:
             try:
                 self.total_number = int(
-                    txn.get('num-samples'.encode('utf-8')).decode(
-                        self.encoding))
+                    txn.get(b'num-samples').decode(self.encoding))
             except AttributeError:
                 warnings.warn(
                     'DeprecationWarning: The lmdb dataset generated with '
@@ -45,8 +44,7 @@ class LmdbAnnFileBackend:
                     'convert-text-recognition-dataset-to-lmdb-format for '
                     'details.')
                 self.total_number = int(
-                    txn.get('total_number'.encode('utf-8')).decode(
-                        self.encoding))
+                    txn.get(b'total_number').decode(self.encoding))
                 self.deprecated_format = True
             # The lmdb file may contain only the label, or it may contain both
             # the label and the image, so we use image_key here for probing.

mmocr/models/common/backbones/unet.py

@@ -477,7 +477,7 @@ class UNet(BaseModule):
                     act_cfg=act_cfg,
                     dcn=None,
                     plugins=None))
-            self.encoder.append((nn.Sequential(*enc_conv_block)))
+            self.encoder.append(nn.Sequential(*enc_conv_block))
             in_channels = base_channels * 2**i
 
     def forward(self, x):

mmocr/models/kie/heads/sdmgr_head.py

@@ -53,7 +53,7 @@ class SDMGRHead(BaseModule):
             node_nums.append(text.size(0))
             char_nums.append((text > 0).sum(-1))
 
-        max_num = max([char_num.max() for char_num in char_nums])
+        max_num = max(char_num.max() for char_num in char_nums)
         all_nodes = torch.cat([
             torch.cat(
                 [text,

mmocr/models/textdet/modules/gcn.py

@@ -43,7 +43,7 @@ class GCN(nn.Module):
     """
 
     def __init__(self, feat_len):
-        super(GCN, self).__init__()
+        super().__init__()
         self.bn0 = nn.BatchNorm1d(feat_len, affine=False).float()
         self.conv1 = GraphConv(feat_len, 512)
         self.conv2 = GraphConv(512, 256)

mmocr/models/textdet/modules/local_graph.py

@@ -142,10 +142,9 @@ class LocalGraphs:
         assert isinstance(knn_batch, list)
         assert isinstance(sorted_dist_ind_batch, list)
 
-        num_max_nodes = max([
+        num_max_nodes = max(
             len(pivot_local_graph) for pivot_local_graphs in local_graph_batch
-            for pivot_local_graph in pivot_local_graphs
-        ])
+            for pivot_local_graph in pivot_local_graphs)
 
         local_graphs_node_feat = []
         adjacent_matrices = []

mmocr/models/textdet/modules/proposal_local_graph.py

@@ -279,9 +279,8 @@ class ProposalLocalGraphs:
             pivot_local_graphs.append(pivot_local_graph)
             pivot_knns.append(pivot_knn)
 
-        num_max_nodes = max([
-            len(pivot_local_graph) for pivot_local_graph in pivot_local_graphs
-        ])
+        num_max_nodes = max(
+            len(pivot_local_graph) for pivot_local_graph in pivot_local_graphs)
 
         local_graphs_node_feat = []
         adjacent_matrices = []

mmocr/models/textdet/postprocess/utils.py

@@ -256,10 +256,11 @@ def connected_components(nodes, score_dict, link_thr):
         node_queue = [node]
         while node_queue:
             node = node_queue.pop(0)
-            neighbors = set([
-                neighbor for neighbor in node.links if
+            neighbors = {
+                neighbor
+                for neighbor in node.links if
                 score_dict[tuple(sorted([node.ind, neighbor.ind]))] >= link_thr
-            ])
+            }
             neighbors.difference_update(cluster)
             nodes.difference_update(neighbors)
             cluster.update(neighbors)

mmocr/models/textrecog/backbones/resnet31_ocr.py

@@ -140,6 +140,6 @@ class ResNet31OCR(BaseModule):
             outs.append(x)
 
         if self.out_indices is not None:
-            return tuple([outs[i] for i in self.out_indices])
+            return tuple(outs[i] for i in self.out_indices)
 
         return x

mmocr/models/textrecog/backbones/very_deep_vgg.py

@@ -37,28 +37,27 @@ class VeryDeepVgg(BaseModule):
         def conv_relu(i, batch_normalization=False):
             n_in = input_channels if i == 0 else nm[i - 1]
             n_out = nm[i]
-            cnn.add_module('conv{0}'.format(i),
+            cnn.add_module(f'conv{i}',
                            nn.Conv2d(n_in, n_out, ks[i], ss[i], ps[i]))
             if batch_normalization:
-                cnn.add_module('batchnorm{0}'.format(i), nn.BatchNorm2d(n_out))
+                cnn.add_module(f'batchnorm{i}', nn.BatchNorm2d(n_out))
             if leaky_relu:
-                cnn.add_module('relu{0}'.format(i),
-                               nn.LeakyReLU(0.2, inplace=True))
+                cnn.add_module(f'relu{i}', nn.LeakyReLU(0.2, inplace=True))
             else:
-                cnn.add_module('relu{0}'.format(i), nn.ReLU(True))
+                cnn.add_module(f'relu{i}', nn.ReLU(True))
 
         conv_relu(0)
-        cnn.add_module('pooling{0}'.format(0), nn.MaxPool2d(2, 2))  # 64x16x64
+        cnn.add_module(f'pooling{0}', nn.MaxPool2d(2, 2))  # 64x16x64
         conv_relu(1)
-        cnn.add_module('pooling{0}'.format(1), nn.MaxPool2d(2, 2))  # 128x8x32
+        cnn.add_module(f'pooling{1}', nn.MaxPool2d(2, 2))  # 128x8x32
         conv_relu(2, True)
         conv_relu(3)
-        cnn.add_module('pooling{0}'.format(2),
-                       nn.MaxPool2d((2, 2), (2, 1), (0, 1)))  # 256x4x16
+        cnn.add_module(f'pooling{2}', nn.MaxPool2d((2, 2), (2, 1),
+                                                   (0, 1)))  # 256x4x16
         conv_relu(4, True)
         conv_relu(5)
-        cnn.add_module('pooling{0}'.format(3),
-                       nn.MaxPool2d((2, 2), (2, 1), (0, 1)))  # 512x2x16
+        cnn.add_module(f'pooling{3}', nn.MaxPool2d((2, 2), (2, 1),
+                                                   (0, 1)))  # 512x2x16
         conv_relu(6, True)  # 512x1x16
 
         self.cnn = cnn

mmocr/models/textrecog/decoders/master_decoder.py

@@ -21,7 +21,7 @@ def clones(module, N):
 class Embeddings(nn.Module):
 
     def __init__(self, d_model, vocab):
-        super(Embeddings, self).__init__()
+        super().__init__()
         self.lut = nn.Embedding(vocab, d_model)
         self.d_model = d_model
 
@@ -70,7 +70,7 @@ class MasterDecoder(BaseDecoder):
         max_seq_len=30,
         init_cfg=None,
     ):
-        super(MasterDecoder, self).__init__(init_cfg=init_cfg)
+        super().__init__(init_cfg=init_cfg)
 
         operation_order = ('norm', 'self_attn', 'norm', 'cross_attn', 'norm',
                            'ffn')

mmocr/models/textrecog/layers/conv_layer.py

@@ -31,7 +31,7 @@ class BasicBlock(nn.Module):
                  downsample=None,
                  use_conv1x1=False,
                  plugins=None):
-        super(BasicBlock, self).__init__()
+        super().__init__()
 
         if use_conv1x1:
             self.conv1 = conv1x1(inplanes, planes)

mmocr/models/textrecog/losses/mix_loss.py

@@ -40,7 +40,7 @@ class ABILoss(nn.Module):
 
     def _flatten(self, logits, target_lens):
         flatten_logits = torch.cat(
-            [s[:target_lens[i]] for i, s in enumerate((logits))])
+            [s[:target_lens[i]] for i, s in enumerate(logits)])
         return flatten_logits
 
     def _ce_loss(self, logits, targets):

mmocr/models/textrecog/plugins/common.py

@@ -15,7 +15,7 @@ class Maxpool2d(nn.Module):
     """
 
     def __init__(self, kernel_size, stride, padding=0, **kwargs):
-        super(Maxpool2d, self).__init__()
+        super().__init__()
         self.model = nn.MaxPool2d(kernel_size, stride, padding)
 
     def forward(self, x):
@@ -53,7 +53,7 @@ class GCAModule(nn.Module):
                  scale_attn=False,
                  fusion_type='channel_add',
                  **kwargs):
-        super(GCAModule, self).__init__()
+        super().__init__()
 
         assert pooling_type in ['avg', 'att']
         assert fusion_type in ['channel_add', 'channel_mul', 'channel_concat']

mmocr/utils/fileio.py

@@ -32,7 +32,7 @@ def list_from_file(filename, encoding='utf-8'):
         list[str]: A list of strings.
     """
     item_list = []
-    with open(filename, 'r', encoding=encoding) as f:
+    with open(filename, encoding=encoding) as f:
         for line in f:
             item_list.append(line.rstrip('\n\r'))
     return item_list

old_tests/test_apis/test_single_gpu_test.py

@@ -49,9 +49,10 @@ def generate_sample_dataloader(cfg, curr_dir, img_prefix='', ann_file=''):
     dataset = DATASETS.build(cfg.data.test)
 
     loader_cfg = {
-        **dict((k, cfg.data[k]) for k in [
-                   'workers_per_gpu', 'samples_per_gpu'
-               ] if k in cfg.data)
+        **{
+            k: cfg.data[k]
+            for k in ['workers_per_gpu', 'samples_per_gpu'] if k in cfg.data
+        }
     }
     test_loader_cfg = {
         **loader_cfg,
@@ -144,9 +145,10 @@ def gene_sdmgr_model_dataloader(cfg, dirname, curr_dir, empty_img=False):
     dataset = DATASETS.build(cfg.data.test)
 
     loader_cfg = {
-        **dict((k, cfg.data[k]) for k in [
-                   'workers_per_gpu', 'samples_per_gpu'
-               ] if k in cfg.data)
+        **{
+            k: cfg.data[k]
+            for k in ['workers_per_gpu', 'samples_per_gpu'] if k in cfg.data
+        }
     }
     test_loader_cfg = {
         **loader_cfg,

old_tests/test_metrics/test_hmean_detect.py

@@ -47,7 +47,7 @@ def test_get_gt_mask():
 
 
 def test_eval_hmean():
-    metrics = set(['hmean-iou', 'hmean-ic13'])
+    metrics = {'hmean-iou', 'hmean-ic13'}
     results = [{
         'boundary_result': [[50, 70, 80, 70, 80, 100, 50, 100, 1],
                             [120, 140, 200, 140, 200, 200, 120, 200, 1]]

old_tests/test_utils/test_textio.py

@@ -63,7 +63,7 @@ def test_list_to_file():
             list_to_file(filename, lines)
             lines2 = [
                 line.rstrip('\r\n')
-                for line in open(filename, 'r', encoding='utf-8').readlines()
+                for line in open(filename, encoding='utf-8').readlines()
             ]
             lines = list(map(str, lines))
             assert len(lines) == len(lines2)
@@ -74,7 +74,7 @@ def test_list_to_file():
             list_to_file(filename, [json.dumps(line) for line in lines])
             lines2 = [
                 json.loads(line.rstrip('\r\n'))['text']
-                for line in open(filename, 'r', encoding='utf-8').readlines()
+                for line in open(filename, encoding='utf-8').readlines()
             ][0]
 
             lines = list(lines[0]['text'])

setup.py

@@ -77,7 +77,7 @@ def add_mim_extension():
 
 
 def get_version():
-    with open(version_file, 'r') as f:
+    with open(version_file) as f:
         exec(compile(f.read(), version_file, 'exec'))
     import sys
 
@@ -137,12 +137,11 @@ def parse_requirements(fname='requirements.txt', with_version=True):
             yield info
 
     def parse_require_file(fpath):
-        with open(fpath, 'r') as f:
+        with open(fpath) as f:
             for line in f.readlines():
                 line = line.strip()
                 if line and not line.startswith('#'):
-                    for info in parse_line(line):
-                        yield info
+                    yield from parse_line(line)
 
     def gen_packages_items():
         if exists(require_fpath):

tools/analyze_logs.py

@@ -160,7 +160,7 @@ def load_json_logs(json_logs):
     # value of sub dict is a list of corresponding values of all iterations
     log_dicts = [dict() for _ in json_logs]
     for json_log, log_dict in zip(json_logs, log_dicts):
-        with open(json_log, 'r') as log_file:
+        with open(json_log) as log_file:
             for line in log_file:
                 log = json.loads(line.strip())
                 # skip lines without `epoch` field

tools/data/textdet/bid_converter.py

@@ -106,7 +106,7 @@ def load_txt_info(gt_file, img_info):
     Returns:
         img_info (dict): The dict of the img and annotation information
     """
-    with open(gt_file, 'r', encoding='latin1') as f:
+    with open(gt_file, encoding='latin1') as f:
         anno_info = []
         for line in f:
             line = line.strip('\n')

tools/data/textdet/detext_converter.py

@@ -101,7 +101,7 @@ def load_txt_info(gt_file, img_info):
         img_info (dict): The dict of the img and annotation information
     """
 
-    with open(gt_file, 'r') as f:
+    with open(gt_file) as f:
         anno_info = []
         annotations = f.readlines()
         for ann in annotations:

tools/data/textdet/hiertext_converter.py

@@ -92,7 +92,7 @@ def collect_hiertext_info(root_path, level, split, print_every=1000):
         raise Exception(
             f'{annotation_path} not exists, please check and try again.')
 
-    annotation = json.load(open(annotation_path, 'r'))['annotations']
+    annotation = json.load(open(annotation_path))['annotations']
     img_infos = []
     for i, img_annos in enumerate(annotation):
         if i > 0 and i % print_every == 0:

tools/data/textdet/ic11_converter.py

@@ -119,7 +119,7 @@ def load_txt_info(gt_file, img_info):
         img_info (dict): The dict of the img and annotation information
     """
     anno_info = []
-    with open(gt_file, 'r') as f:
+    with open(gt_file) as f:
         lines = f.readlines()
         for line in lines:
             xmin, ymin, xmax, ymax = line.split(',')[0:4]

tools/data/textdet/ic13_converter.py

@@ -112,7 +112,7 @@ def load_txt_info(gt_file, img_info, separator):
         img_info (dict): The dict of the img and annotation information
     """
     anno_info = []
-    with open(gt_file, 'r') as f:
+    with open(gt_file) as f:
         lines = f.readlines()
         for line in lines:
             xmin, ymin, xmax, ymax = line.split(separator)[0:4]

tools/data/textdet/mtwi_converter.py

@@ -136,7 +136,7 @@ def load_txt_info(gt_file, img_info):
     """
 
     anno_info = []
-    with open(gt_file, 'r') as f:
+    with open(gt_file) as f:
         lines = f.readlines()
     for line in lines:
         points = line.split(',')[0:8]

tools/data/textdet/rctw_converter.py

@@ -120,7 +120,7 @@ def load_txt_info(gt_file, img_info):
     """
 
     anno_info = []
-    with open(gt_file, 'r', encoding='utf-8-sig') as f:
+    with open(gt_file, encoding='utf-8-sig') as f:
         lines = f.readlines()
     for line in lines:
         points = line.split(',')[0:8]

tools/data/textdet/sroie_converter.py

@@ -107,7 +107,7 @@ def load_txt_info(gt_file, img_info):
         img_info (list): The dict of the img and annotation information
     """
 
-    with open(gt_file, 'r', encoding='unicode_escape') as f:
+    with open(gt_file, encoding='unicode_escape') as f:
         anno_info = []
         for ann in f.readlines():
 

tools/data/textdet/synthtext_converter.py

@@ -150,7 +150,7 @@ def convert_annotations(root_path, gt_name, lmdb_name):
                 img_json['annotations'].append(anno_info)
             string = json.dumps(img_json)
             txn.put(str(img_id).encode('utf8'), string.encode('utf8'))
-        key = 'total_number'.encode('utf8')
+        key = b'total_number'
         value = str(img_num).encode('utf8')
         txn.put(key, value)
 

tools/data/textdet/totaltext_converter.py

@@ -41,7 +41,7 @@ def collect_files(img_dir, gt_dir):
 
     imgs_list = sorted(imgs_list)
     ann_list = sorted(
-        [osp.join(gt_dir, gt_file) for gt_file in os.listdir(gt_dir)])
+        osp.join(gt_dir, gt_file) for gt_file in os.listdir(gt_dir))
 
     files = list(zip(imgs_list, ann_list))
     assert len(files), f'No images found in {img_dir}'
@@ -230,7 +230,7 @@ def get_contours_txt(gt_path):
     contours = []
     words = []
 
-    with open(gt_path, 'r') as f:
+    with open(gt_path) as f:
         tmp_line = ''
         for idx, line in enumerate(f):
             line = line.strip()

tools/data/textdet/vintext_converter.py

@@ -109,7 +109,7 @@ def load_txt_info(gt_file, img_info):
         img_info (dict): The dict of the img and annotation information
     """
 
-    with open(gt_file, 'r', encoding='utf-8') as f:
+    with open(gt_file, encoding='utf-8') as f:
         anno_info = []
         for line in f:
             line = line.strip('\n')

tools/data/textrecog/bid_converter.py

@@ -108,7 +108,7 @@ def load_txt_info(gt_file, img_info):
     Returns:
         img_info (dict): The dict of the img and annotation information
     """
-    with open(gt_file, 'r', encoding='latin1') as f:
+    with open(gt_file, encoding='latin1') as f:
         anno_info = []
         for line in f:
             line = line.strip('\n')

tools/data/textrecog/detext_converter.py

@@ -100,7 +100,7 @@ def load_txt_info(gt_file, img_info):
         img_info (dict): The dict of the img and annotation information
     """
 
-    with open(gt_file, 'r') as f:
+    with open(gt_file) as f:
         anno_info = []
         annotations = f.readlines()
         for ann in annotations:

tools/data/textrecog/hiertext_converter.py

@@ -170,7 +170,7 @@ def convert_hiertext(
         raise Exception(
             f'{annotation_path} not exists, please check and try again.')
 
-    annotation = json.load(open(annotation_path, 'r'))['annotations']
+    annotation = json.load(open(annotation_path))['annotations']
     # outputs
     dst_label_file = osp.join(root_path, f'{split}_label.{format}')
     dst_image_root = osp.join(root_path, 'crops', split)

tools/data/textrecog/ic11_converter.py

@@ -36,7 +36,6 @@ def convert_annotations(root_path, split, format):
     with open(
             osp.join(root_path, 'annotations',
                      f'Challenge1_{split}_Task3_GT.txt'),
-            'r',
             encoding='"utf-8-sig') as f:
         annos = f.readlines()
     dst_image_root = osp.join(root_path, split.lower())

tools/data/textrecog/ic13_converter.py

@@ -36,7 +36,6 @@ def convert_annotations(root_path, split, format):
     with open(
             osp.join(root_path, 'annotations',
                      f'Challenge2_{split}_Task3_GT.txt'),
-            'r',
             encoding='"utf-8-sig') as f:
         annos = f.readlines()
     dst_image_root = osp.join(root_path, split.lower())

tools/data/textrecog/lv_converter.py

@@ -36,7 +36,6 @@ def convert_annotations(root_path, split, format):
         lines = []
         with open(
                 osp.join(root_path, f'{split}_label.txt'),
-                'r',
                 encoding='"utf-8-sig') as f:
             annos = f.readlines()
         for anno in annos:

tools/data/textrecog/mtwi_converter.py

@@ -138,7 +138,7 @@ def load_txt_info(gt_file, img_info):
     """
 
     anno_info = []
-    with open(gt_file, 'r') as f:
+    with open(gt_file) as f:
         lines = f.readlines()
     for line in lines:
         points = line.split(',')[0:8]

tools/data/textrecog/rctw_converter.py

@@ -122,7 +122,7 @@ def load_txt_info(gt_file, img_info):
     """
 
     anno_info = []
-    with open(gt_file, 'r', encoding='utf-8-sig') as f:
+    with open(gt_file, encoding='utf-8-sig') as f:
         lines = f.readlines()
     for line in lines:
         points = line.split(',')[0:8]

tools/data/textrecog/sroie_converter.py

@@ -112,7 +112,7 @@ def load_txt_info(gt_file, img_info):
         img_info (list): The dict of the img and annotation information
     """
 
-    with open(gt_file, 'r', encoding='unicode_escape') as f:
+    with open(gt_file, encoding='unicode_escape') as f:
         anno_info = []
         for ann in f.readlines():
             # skip invalid annotation line

tools/data/textrecog/totaltext_converter.py

@@ -41,7 +41,7 @@ def collect_files(img_dir, gt_dir):
 
     imgs_list = sorted(imgs_list)
     ann_list = sorted(
-        [osp.join(gt_dir, gt_file) for gt_file in os.listdir(gt_dir)])
+        osp.join(gt_dir, gt_file) for gt_file in os.listdir(gt_dir))
 
     files = [(img_file, gt_file)
              for (img_file, gt_file) in zip(imgs_list, ann_list)]
@@ -218,7 +218,7 @@ def get_contours_txt(gt_path):
     contours = []
     words = []
 
-    with open(gt_path, 'r') as f:
+    with open(gt_path) as f:
         tmp_line = ''
         for idx, line in enumerate(f):
             line = line.strip()

tools/data/textrecog/vintext_converter.py

@@ -111,7 +111,7 @@ def load_txt_info(gt_file, img_info):
         img_info (dict): The dict of the img and annotation information
     """
 
-    with open(gt_file, 'r', encoding='utf-8') as f:
+    with open(gt_file, encoding='utf-8') as f:
         anno_info = []
         for line in f:
             line = line.strip('\n')

tools/deployment/onnx2tensorrt.py

@@ -161,7 +161,7 @@ def onnx2tensorrt(onnx_file: str,
                         atol=1e-4):
                     same_diff = 'different'
                     break
-        print('The outputs are {} between TensorRT and ONNX'.format(same_diff))
+        print(f'The outputs are {same_diff} between TensorRT and ONNX')
 
         if show:
             onnx_img = onnx_model.show_result(
@@ -259,7 +259,7 @@ if __name__ == '__main__':
     assert osp.exists(args.model_config), 'Config {} not found.'.format(
         args.model_config)
     assert osp.exists(args.onnx_file), \
-        'ONNX model {} not found.'.format(args.onnx_file)
+        f'ONNX model {args.onnx_file} not found.'
     assert args.workspace_size >= 0, 'Workspace size less than 0.'
     for max_value, min_value in zip(args.max_shape, args.min_shape):
         assert max_value >= min_value, \

tools/deployment/pytorch2onnx.py

@@ -259,7 +259,7 @@ def pytorch2onnx(model: nn.Module,
                         atol=1e-4):
                     same_diff = 'different'
                     break
-        print('The outputs are {} between PyTorch and ONNX'.format(same_diff))
+        print(f'The outputs are {same_diff} between PyTorch and ONNX')
 
         if show:
             onnx_img = onnx_model.show_result(

tools/publish_model.py

@@ -26,7 +26,7 @@ def process_checkpoint(in_file, out_file):
         checkpoint['meta'] = {'CLASSES': 0}
     torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False)
     sha = subprocess.check_output(['sha256sum', out_file]).decode()
-    final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8])
+    final_file = out_file.rstrip('.pth') + f'-{sha[:8]}.pth'
     subprocess.Popen(['mv', out_file, final_file])