commit fix by running pre-commit run -a (#12165)

benchmark/PaddleOCR_DBNet/data_loader/dataset.py

@@ -21,7 +21,7 @@ class ICDAR2015Dataset(BaseDataSet):
         filter_keys,
         ignore_tags,
         transform=None,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(
             data_path, img_mode, pre_processes, filter_keys, ignore_tags, transform
@@ -75,7 +75,7 @@ class DetDataset(BaseDataSet):
         filter_keys,
         ignore_tags,
         transform=None,
-        **kwargs
+        **kwargs,
     ):
         self.load_char_annotation = kwargs["load_char_annotation"]
         self.expand_one_char = kwargs["expand_one_char"]
@@ -138,7 +138,7 @@ class SynthTextDataset(BaseDataSet):
         pre_processes,
         filter_keys,
         transform=None,
-        **kwargs
+        **kwargs,
     ):
         self.transform = transform
         self.dataRoot = pathlib.Path(data_path)

deploy/pdserving/ocr_reader.py

@@ -254,7 +254,7 @@ class CTCLabelDecode(BaseRecLabelDecode):
         # character_dict_path=None,
         # character_type='ch',
         # use_space_char=False,
-        **kwargs
+        **kwargs,
     ):
         super(CTCLabelDecode, self).__init__(config)
 

deploy/pdserving/win/ocr_reader.py

@@ -254,7 +254,7 @@ class CTCLabelDecode(BaseRecLabelDecode):
         # character_dict_path=None,
         # character_type='ch',
         # use_space_char=False,
-        **kwargs
+        **kwargs,
     ):
         super(CTCLabelDecode, self).__init__(config)
 

ppocr/data/imaug/drrg_targets.py

@@ -38,7 +38,7 @@ class DRRGTargets(object):
         min_rand_half_height=8.0,
         max_rand_half_height=24.0,
         jitter_level=0.2,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         self.orientation_thr = orientation_thr

ppocr/data/imaug/east_process.py

@@ -32,7 +32,7 @@ class EASTProcessTrain(object):
         background_ratio=0.125,
         min_crop_side_ratio=0.1,
         min_text_size=10,
-        **kwargs
+        **kwargs,
     ):
         self.input_size = image_shape[1]
         self.random_scale = np.array([0.5, 1, 2.0, 3.0])

ppocr/data/imaug/fce_aug.py

@@ -359,7 +359,7 @@ class RandomRotatePolyInstances:
         max_angle=10,
         pad_with_fixed_color=False,
         pad_value=(0, 0, 0),
-        **kwargs
+        **kwargs,
     ):
         """Randomly rotate images and polygon masks.
 
@@ -487,7 +487,7 @@ class SquareResizePad:
         pad_ratio=0.6,
         pad_with_fixed_color=False,
         pad_value=(0, 0, 0),
-        **kwargs
+        **kwargs,
     ):
         """Resize or pad images to be square shape.
 

ppocr/data/imaug/fce_targets.py

@@ -53,7 +53,7 @@ class FCENetTargets:
         level_size_divisors=(8, 16, 32),
         level_proportion_range=((0, 0.25), (0.2, 0.65), (0.55, 1.0)),
         orientation_thr=2.0,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         assert isinstance(level_size_divisors, tuple)

ppocr/data/imaug/label_ops.py

@@ -589,7 +589,7 @@ class SRNLabelEncode(BaseRecLabelEncode):
         max_text_length=25,
         character_dict_path=None,
         use_space_char=False,
-        **kwargs
+        **kwargs,
     ):
         super(SRNLabelEncode, self).__init__(
             max_text_length, character_dict_path, use_space_char
@@ -638,7 +638,7 @@ class TableLabelEncode(AttnLabelEncode):
         merge_no_span_structure=False,
         learn_empty_box=False,
         loc_reg_num=4,
-        **kwargs
+        **kwargs,
     ):
         self.max_text_len = max_text_length
         self.lower = False
@@ -786,7 +786,7 @@ class TableMasterLabelEncode(TableLabelEncode):
         merge_no_span_structure=False,
         learn_empty_box=False,
         loc_reg_num=4,
-        **kwargs
+        **kwargs,
     ):
         super(TableMasterLabelEncode, self).__init__(
             max_text_length,
@@ -795,7 +795,7 @@ class TableMasterLabelEncode(TableLabelEncode):
             merge_no_span_structure,
             learn_empty_box,
             loc_reg_num,
-            **kwargs
+            **kwargs,
         )
         self.pad_idx = self.dict[self.pad_str]
         self.unknown_idx = self.dict[self.unknown_str]
@@ -909,7 +909,7 @@ class SATRNLabelEncode(BaseRecLabelEncode):
         character_dict_path=None,
         use_space_char=False,
         lower=False,
-        **kwargs
+        **kwargs,
     ):
         super(SATRNLabelEncode, self).__init__(
             max_text_length, character_dict_path, use_space_char
@@ -1019,7 +1019,7 @@ class VQATokenLabelEncode(object):
         order_method=None,
         infer_mode=False,
         ocr_engine=None,
-        **kwargs
+        **kwargs,
     ):
         super(VQATokenLabelEncode, self).__init__()
         from paddlenlp.transformers import (
@@ -1273,7 +1273,7 @@ class MultiLabelEncode(BaseRecLabelEncode):
         character_dict_path=None,
         use_space_char=False,
         gtc_encode=None,
-        **kwargs
+        **kwargs,
     ):
         super(MultiLabelEncode, self).__init__(
             max_text_length, character_dict_path, use_space_char
@@ -1381,7 +1381,7 @@ class ViTSTRLabelEncode(BaseRecLabelEncode):
         character_dict_path=None,
         use_space_char=False,
         ignore_index=0,
-        **kwargs
+        **kwargs,
     ):
         super(ViTSTRLabelEncode, self).__init__(
             max_text_length, character_dict_path, use_space_char
@@ -1416,7 +1416,7 @@ class ABINetLabelEncode(BaseRecLabelEncode):
         character_dict_path=None,
         use_space_char=False,
         ignore_index=100,
-        **kwargs
+        **kwargs,
     ):
         super(ABINetLabelEncode, self).__init__(
             max_text_length, character_dict_path, use_space_char
@@ -1497,7 +1497,7 @@ class SPINLabelEncode(AttnLabelEncode):
         character_dict_path=None,
         use_space_char=False,
         lower=True,
-        **kwargs
+        **kwargs,
     ):
         super(SPINLabelEncode, self).__init__(
             max_text_length, character_dict_path, use_space_char
@@ -1619,7 +1619,7 @@ class CANLabelEncode(BaseRecLabelEncode):
         max_text_length=100,
         use_space_char=False,
         lower=True,
-        **kwargs
+        **kwargs,
     ):
         super(CANLabelEncode, self).__init__(
             max_text_length, character_dict_path, use_space_char, lower
@@ -1654,7 +1654,7 @@ class CPPDLabelEncode(BaseRecLabelEncode):
         use_space_char=False,
         ch=False,
         ignore_index=100,
-        **kwargs
+        **kwargs,
     ):
         super(CPPDLabelEncode, self).__init__(
             max_text_length, character_dict_path, use_space_char

ppocr/data/imaug/operators.py

@@ -465,7 +465,7 @@ class SRResize(object):
         min_ratio=1,
         mask=False,
         infer_mode=False,
-        **kwargs
+        **kwargs,
     ):
         self.imgH = imgH
         self.imgW = imgW

ppocr/data/imaug/pg_process.py

@@ -37,7 +37,7 @@ class PGProcessTrain(object):
         min_text_size=4,
         max_text_size=512,
         point_gather_mode=None,
-        **kwargs
+        **kwargs,
     ):
         self.tcl_len = tcl_len
         self.max_text_length = max_text_length

ppocr/data/imaug/random_crop_data.py

@@ -133,7 +133,7 @@ class EastRandomCropData(object):
         max_tries=10,
         min_crop_side_ratio=0.1,
         keep_ratio=True,
-        **kwargs
+        **kwargs,
     ):
         self.size = size
         self.max_tries = max_tries

ppocr/data/imaug/rec_img_aug.py

@@ -41,7 +41,7 @@ class RecAug(object):
         jitter_prob=0.4,
         blur_prob=0.4,
         hsv_aug_prob=0.4,
-        **kwargs
+        **kwargs,
     ):
         self.tia_prob = tia_prob
         self.bda = BaseDataAugmentation(
@@ -74,7 +74,7 @@ class BaseDataAugmentation(object):
         jitter_prob=0.4,
         blur_prob=0.4,
         hsv_aug_prob=0.4,
-        **kwargs
+        **kwargs,
     ):
         self.crop_prob = crop_prob
         self.reverse_prob = reverse_prob
@@ -151,7 +151,7 @@ class RecConAug(object):
         image_shape=(32, 320, 3),
         max_text_length=25,
         ext_data_num=1,
-        **kwargs
+        **kwargs,
     ):
         self.ext_data_num = ext_data_num
         self.prob = prob
@@ -199,7 +199,7 @@ class SVTRRecAug(object):
         geometry_p=0.5,
         deterioration_p=0.25,
         colorjitter_p=0.25,
-        **kwargs
+        **kwargs,
     ):
         self.transforms = Compose(
             [
@@ -237,7 +237,7 @@ class ParseQRecAug(object):
         geometry_p=0.5,
         deterioration_p=0.25,
         colorjitter_p=0.25,
-        **kwargs
+        **kwargs,
     ):
         self.transforms = Compose(
             [
@@ -289,7 +289,7 @@ class RecResizeImg(object):
         eval_mode=False,
         character_dict_path="./ppocr/utils/ppocr_keys_v1.txt",
         padding=True,
-        **kwargs
+        **kwargs,
     ):
         self.image_shape = image_shape
         self.infer_mode = infer_mode
@@ -315,7 +315,7 @@ class VLRecResizeImg(object):
         infer_mode=False,
         character_dict_path="./ppocr/utils/ppocr_keys_v1.txt",
         padding=True,
-        **kwargs
+        **kwargs,
     ):
         self.image_shape = image_shape
         self.infer_mode = infer_mode
@@ -437,7 +437,7 @@ class SPINRecResizeImg(object):
         interpolation=2,
         mean=(127.5, 127.5, 127.5),
         std=(127.5, 127.5, 127.5),
-        **kwargs
+        **kwargs,
     ):
         self.image_shape = image_shape
 
@@ -485,7 +485,7 @@ class GrayRecResizeImg(object):
         inter_type="Image.Resampling.LANCZOS",
         scale=True,
         padding=False,
-        **kwargs
+        **kwargs,
     ):
         self.image_shape = image_shape
         self.resize_type = resize_type

ppocr/data/imaug/sast_process.py

@@ -33,7 +33,7 @@ class SASTProcessTrain(object):
         min_crop_side_ratio=0.3,
         min_text_size=10,
         max_text_size=512,
-        **kwargs
+        **kwargs,
     ):
         self.input_size = image_shape[1]
         self.min_crop_size = min_crop_size

ppocr/data/imaug/vqa/token/vqa_token_pad.py

@@ -26,7 +26,7 @@ class VQATokenPad(object):
         return_overflowing_tokens=False,
         return_special_tokens_mask=False,
         infer_mode=False,
-        **kwargs
+        **kwargs,
     ):
         self.max_seq_len = max_seq_len
         self.pad_to_max_seq_len = max_seq_len

ppocr/losses/det_basic_loss.py

@@ -34,7 +34,7 @@ class BalanceLoss(nn.Layer):
         negative_ratio=3,
         return_origin=False,
         eps=1e-6,
-        **kwargs
+        **kwargs,
     ):
         """
         The BalanceLoss for Differentiable Binarization text detection

ppocr/losses/det_db_loss.py

@@ -41,7 +41,7 @@ class DBLoss(nn.Layer):
         beta=10,
         ohem_ratio=3,
         eps=1e-6,
-        **kwargs
+        **kwargs,
     ):
         super(DBLoss, self).__init__()
         self.alpha = alpha

ppocr/losses/det_pse_loss.py

@@ -31,7 +31,7 @@ class PSELoss(nn.Layer):
         kernel_sample_mask="pred",
         reduction="sum",
         eps=1e-6,
-        **kwargs
+        **kwargs,
     ):
         """Implement PSE Loss."""
         super(PSELoss, self).__init__()

ppocr/losses/distillation_loss.py

@@ -675,7 +675,7 @@ class DistillationNRTRLoss(CELoss):
         multi_head=False,
         smoothing=True,
         name="loss_nrtr",
-        **kwargs
+        **kwargs,
     ):
         super().__init__(smoothing=smoothing)
         self.model_name_list = model_name_list
@@ -713,7 +713,7 @@ class DistillationDBLoss(DBLoss):
         ohem_ratio=3,
         eps=1e-6,
         name="db",
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         self.model_name_list = model_name_list
@@ -935,7 +935,7 @@ class DistillationVQADistanceLoss(DistanceLoss):
         key=None,
         index=None,
         name="loss_distance",
-        **kargs
+        **kargs,
     ):
         super().__init__(mode=mode, **kargs)
         assert isinstance(model_name_pairs, list)

ppocr/losses/rec_aster_loss.py

@@ -50,7 +50,7 @@ class AsterLoss(nn.Layer):
         ignore_index=-100,
         sequence_normalize=False,
         sample_normalize=True,
-        **kwargs
+        **kwargs,
     ):
         super(AsterLoss, self).__init__()
         self.weight = weight

ppocr/losses/rec_enhanced_ctc_loss.py

@@ -35,7 +35,7 @@ class EnhancedCTCLoss(nn.Layer):
         feat_dim=96,
         init_center=False,
         center_file_path=None,
-        **kwargs
+        **kwargs,
     ):
         super(EnhancedCTCLoss, self).__init__()
         self.ctc_loss_func = CTCLoss(use_focal_loss=use_focal_loss)

ppocr/metrics/e2e_metric.py

@@ -29,7 +29,7 @@ class E2EMetric(object):
         gt_mat_dir,
         character_dict_path,
         main_indicator="f_score_e2e",
-        **kwargs
+        **kwargs,
     ):
         self.mode = mode
         self.gt_mat_dir = gt_mat_dir

ppocr/metrics/table_metric.py

@@ -77,7 +77,7 @@ class TableMetric(object):
         compute_bbox_metric=False,
         box_format="xyxy",
         del_thead_tbody=False,
-        **kwargs
+        **kwargs,
     ):
         """
 

ppocr/modeling/backbones/rec_hgnet.py

@@ -335,7 +335,7 @@ def PPHGNet_small(pretrained=False, use_ssld=False, det=False, **kwargs):
         stage_config=stage_config_det if det else stage_config_rec,
         layer_num=6,
         det=det,
-        **kwargs
+        **kwargs,
     )
     return model
 
@@ -363,6 +363,6 @@ def PPHGNet_base(pretrained=False, use_ssld=True, **kwargs):
         stage_config=stage_config,
         layer_num=7,
         dropout_prob=0.2,
-        **kwargs
+        **kwargs,
     )
     return model

ppocr/modeling/backbones/rec_lcnetv3.py

@@ -395,7 +395,7 @@ class PPLCNetV3(nn.Layer):
         lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
         lab_lr=0.1,
         det=False,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         self.scale = scale

ppocr/modeling/backbones/rec_mobilenet_v3.py

@@ -32,7 +32,7 @@ class MobileNetV3(nn.Layer):
         large_stride=None,
         small_stride=None,
         disable_se=False,
-        **kwargs
+        **kwargs,
     ):
         super(MobileNetV3, self).__init__()
         self.disable_se = disable_se

ppocr/modeling/backbones/rec_mv1_enhance.py

@@ -118,7 +118,7 @@ class MobileNetV1Enhance(nn.Layer):
         last_conv_stride=1,
         last_pool_type="max",
         last_pool_kernel_size=[3, 2],
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         self.scale = scale

ppocr/modeling/backbones/rec_svtrv2.py

@@ -323,7 +323,7 @@ class SVTRStage(nn.Layer):
         act=nn.GELU,
         eps=1e-6,
         downsample=None,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         self.dim = dim
@@ -506,7 +506,7 @@ class SVTRv2(nn.Layer):
         eps=1e-6,
         use_pool=False,
         feat2d=False,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         num_stages = len(depths)

ppocr/modeling/backbones/rec_vit.py

@@ -196,7 +196,7 @@ class ViT(nn.Layer):
         epsilon=1e-6,
         act="nn.GELU",
         prenorm=False,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         self.embed_dim = embed_dim

ppocr/modeling/backbones/rec_vitstr.py

@@ -51,7 +51,7 @@ class ViTSTR(nn.Layer):
         act_layer="nn.GELU",
         epsilon=1e-6,
         out_channels=None,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         self.seqlen = seqlen

ppocr/modeling/backbones/vqa_layoutlm.py

@@ -58,7 +58,7 @@ class NLPBaseModel(nn.Layer):
         type="ser",
         pretrained=True,
         checkpoints=None,
-        **kwargs
+        **kwargs,
     ):
         super(NLPBaseModel, self).__init__()
         if checkpoints is not None:  # load the trained model

ppocr/modeling/heads/det_drrg_head.py

@@ -50,7 +50,7 @@ class DRRGHead(nn.Layer):
         center_region_thr=0.2,
         center_region_area_thr=50,
         local_graph_thr=0.7,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
 

ppocr/modeling/heads/rec_abinet_head.py

@@ -117,7 +117,7 @@ class PositionAttention(nn.Layer):
         h=8,
         w=32,
         mode="nearest",
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         self.max_length = max_length

ppocr/modeling/heads/rec_aster_head.py

@@ -36,7 +36,7 @@ class AsterHead(nn.Layer):
         max_len_labels,
         time_step=25,
         beam_width=5,
-        **kwargs
+        **kwargs,
     ):
         super(AsterHead, self).__init__()
         self.num_classes = out_channels

ppocr/modeling/heads/rec_cppd_head.py

@@ -217,7 +217,7 @@ class CPPDHead(nn.Layer):
         max_len=25,
         vis_seq=50,
         ch=False,
-        **kwargs
+        **kwargs,
     ):
         super(CPPDHead, self).__init__()
 

ppocr/modeling/heads/rec_ctc_head.py

@@ -40,7 +40,7 @@ class CTCHead(nn.Layer):
         fc_decay=0.0004,
         mid_channels=None,
         return_feats=False,
-        **kwargs
+        **kwargs,
     ):
         super(CTCHead, self).__init__()
         if mid_channels is None:

ppocr/modeling/heads/rec_multi_head.py

@@ -83,7 +83,7 @@ class MultiHead(nn.Layer):
                 self.sar_head = eval(name)(
                     in_channels=in_channels,
                     out_channels=out_channels_list["SARLabelDecode"],
-                    **sar_args
+                    **sar_args,
                 )
             elif name == "NRTRHead":
                 gtc_args = self.head_list[idx][name]
@@ -124,7 +124,7 @@ class MultiHead(nn.Layer):
                 self.ctc_head = eval(name)(
                     in_channels=self.ctc_encoder.out_channels,
                     out_channels=out_channels_list["CTCLabelDecode"],
-                    **head_args
+                    **head_args,
                 )
             else:
                 raise NotImplementedError(

ppocr/modeling/heads/rec_parseq_head.py

@@ -222,7 +222,7 @@ class ParseQHead(nn.Layer):
         decode_ar,
         refine_iters,
         dropout,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
 

ppocr/modeling/heads/rec_rfl_head.py

@@ -58,7 +58,7 @@ class RFLHead(nn.Layer):
         out_channels=38,
         use_cnt=True,
         use_seq=True,
-        **kwargs
+        **kwargs,
     ):
         super(RFLHead, self).__init__()
         assert use_cnt or use_seq
@@ -69,14 +69,14 @@ class RFLHead(nn.Layer):
                 embed_size=in_channels,
                 encode_length=batch_max_legnth + 1,
                 out_channels=out_channels,
-                **kwargs
+                **kwargs,
             )
         if self.use_seq:
             self.seq_head = AttentionLSTM(
                 in_channels=in_channels,
                 out_channels=out_channels,
                 hidden_size=hidden_size,
-                **kwargs
+                **kwargs,
             )
         self.batch_max_legnth = batch_max_legnth
         self.num_class = out_channels

ppocr/modeling/heads/rec_robustscanner_head.py

@@ -695,7 +695,7 @@ class RobustScannerHead(nn.Layer):
         mask=True,
         padding_idx=None,
         encode_value=False,
-        **kwargs
+        **kwargs,
     ):
         super(RobustScannerHead, self).__init__()
 

ppocr/modeling/heads/rec_sar_head.py

@@ -47,7 +47,7 @@ class SAREncoder(nn.Layer):
         d_model=512,
         d_enc=512,
         mask=True,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         assert isinstance(enc_bi_rnn, bool)
@@ -167,7 +167,7 @@ class ParallelSARDecoder(BaseDecoder):
         max_text_length=30,
         mask=True,
         pred_concat=True,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
 
@@ -361,7 +361,7 @@ class SARHead(nn.Layer):
         d_k=512,
         pred_dropout=0.1,
         pred_concat=True,
-        **kwargs
+        **kwargs,
     ):
         super(SARHead, self).__init__()
 

ppocr/modeling/heads/rec_srn_head.py

@@ -249,7 +249,7 @@ class SRNHead(nn.Layer):
         num_encoder_TUs,
         num_decoder_TUs,
         hidden_dims,
-        **kwargs
+        **kwargs,
     ):
         super(SRNHead, self).__init__()
         self.char_num = out_channels

ppocr/modeling/heads/table_att_head.py

@@ -49,7 +49,7 @@ class TableAttentionHead(nn.Layer):
         max_text_length=800,
         out_channels=30,
         loc_reg_num=4,
-        **kwargs
+        **kwargs,
     ):
         super(TableAttentionHead, self).__init__()
         self.input_size = in_channels[-1]
@@ -259,7 +259,7 @@ class SLAHead(nn.Layer):
         loc_reg_num=4,
         fc_decay=0.0,
         use_attn=False,
-        **kwargs
+        **kwargs,
     ):
         """
         @param in_channels: input shape

ppocr/modeling/heads/table_master_head.py

@@ -39,7 +39,7 @@ class TableMasterHead(nn.Layer):
         dropout=0,
         max_text_length=500,
         loc_reg_num=4,
-        **kwargs
+        **kwargs,
     ):
         super(TableMasterHead, self).__init__()
         hidden_size = in_channels[-1]

ppocr/modeling/necks/db_fpn.py

@@ -42,7 +42,7 @@ class DSConv(nn.Layer):
         groups=None,
         if_act=True,
         act="relu",
-        **kwargs
+        **kwargs,
     ):
         super(DSConv, self).__init__()
         if groups == None:

ppocr/modeling/transforms/tsrn.py

@@ -46,7 +46,7 @@ class TSRN(nn.Layer):
         mask=False,
         hidden_units=32,
         infer_mode=False,
-        **kwargs
+        **kwargs,
     ):
         super(TSRN, self).__init__()
         in_planes = 3

ppocr/optimizer/learning_rate.py

@@ -41,7 +41,7 @@ class Linear(object):
         power=1.0,
         warmup_epoch=0,
         last_epoch=-1,
-        **kwargs
+        **kwargs,
     ):
         super(Linear, self).__init__()
         self.learning_rate = learning_rate
@@ -88,7 +88,7 @@ class Cosine(object):
         epochs,
         warmup_epoch=0,
         last_epoch=-1,
-        **kwargs
+        **kwargs,
     ):
         super(Cosine, self).__init__()
         self.learning_rate = learning_rate
@@ -133,7 +133,7 @@ class Step(object):
         gamma,
         warmup_epoch=0,
         last_epoch=-1,
-        **kwargs
+        **kwargs,
     ):
         super(Step, self).__init__()
         self.step_size = step_each_epoch * step_size
@@ -177,7 +177,7 @@ class Piecewise(object):
         values,
         warmup_epoch=0,
         last_epoch=-1,
-        **kwargs
+        **kwargs,
     ):
         super(Piecewise, self).__init__()
         self.boundaries = [step_each_epoch * e for e in decay_epochs]
@@ -219,7 +219,7 @@ class CyclicalCosine(object):
         cycle,
         warmup_epoch=0,
         last_epoch=-1,
-        **kwargs
+        **kwargs,
     ):
         super(CyclicalCosine, self).__init__()
         self.learning_rate = learning_rate
@@ -269,7 +269,7 @@ class OneCycle(object):
         three_phase=False,
         warmup_epoch=0,
         last_epoch=-1,
-        **kwargs
+        **kwargs,
     ):
         super(OneCycle, self).__init__()
         self.max_lr = max_lr
@@ -382,7 +382,7 @@ class MultiStepDecay(object):
         gamma,
         warmup_epoch=0,
         last_epoch=-1,
-        **kwargs
+        **kwargs,
     ):
         super(MultiStepDecay, self).__init__()
         self.milestones = [step_each_epoch * e for e in milestones]
@@ -427,7 +427,7 @@ class TwoStepCosine(object):
         epochs,
         warmup_epoch=0,
         last_epoch=-1,
-        **kwargs
+        **kwargs,
     ):
         super(TwoStepCosine, self).__init__()
         self.learning_rate = learning_rate

ppocr/optimizer/optimizer.py

@@ -65,7 +65,7 @@ class Adam(object):
         grad_clip=None,
         name=None,
         lazy_mode=False,
-        **kwargs
+        **kwargs,
     ):
         self.learning_rate = learning_rate
         self.beta1 = beta1
@@ -164,7 +164,7 @@ class RMSProp(object):
         epsilon=1e-6,
         weight_decay=None,
         grad_clip=None,
-        **args
+        **args,
     ):
         super(RMSProp, self).__init__()
         self.learning_rate = learning_rate
@@ -200,7 +200,7 @@ class Adadelta(object):
         weight_decay=None,
         grad_clip=None,
         name=None,
-        **kwargs
+        **kwargs,
     ):
         self.learning_rate = learning_rate
         self.epsilon = epsilon
@@ -241,7 +241,7 @@ class AdamW(object):
         one_dim_param_no_weight_decay=False,
         name=None,
         lazy_mode=False,
-        **args
+        **args,
     ):
         super().__init__()
         self.learning_rate = learning_rate

ppocr/postprocess/db_postprocess.py

@@ -40,7 +40,7 @@ class DBPostProcess(object):
         use_dilation=False,
         score_mode="fast",
         box_type="quad",
-        **kwargs
+        **kwargs,
     ):
         self.thresh = thresh
         self.box_thresh = box_thresh
@@ -268,7 +268,7 @@ class DistillationDBPostProcess(object):
         use_dilation=False,
         score_mode="fast",
         box_type="quad",
-        **kwargs
+        **kwargs,
     ):
         self.model_name = model_name
         self.key = key

ppocr/postprocess/fce_postprocess.py

@@ -76,7 +76,7 @@ class FCEPostProcess(object):
         alpha=1.0,
         beta=1.0,
         box_type="poly",
-        **kwargs
+        **kwargs,
     ):
         self.scales = scales
         self.fourier_degree = fourier_degree

ppocr/postprocess/pg_postprocess.py

@@ -37,7 +37,7 @@ class PGPostProcess(object):
         score_thresh,
         mode,
         point_gather_mode=None,
-        **kwargs
+        **kwargs,
     ):
         self.character_dict_path = character_dict_path
         self.valid_set = valid_set

ppocr/postprocess/pse_postprocess/pse_postprocess.py

@@ -40,7 +40,7 @@ class PSEPostProcess(object):
         min_area=16,
         box_type="quad",
         scale=4,
-        **kwargs
+        **kwargs,
     ):
         assert box_type in ["quad", "poly"], "Only quad and poly is supported"
         self.thresh = thresh

ppocr/postprocess/rec_postprocess.py

@@ -237,7 +237,7 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
         model_name=["student"],
         key=None,
         multi_head=False,
-        **kwargs
+        **kwargs,
     ):
         super(DistillationCTCLabelDecode, self).__init__(
             character_dict_path, use_space_char
@@ -836,7 +836,7 @@ class DistillationSARLabelDecode(SARLabelDecode):
         model_name=["student"],
         key=None,
         multi_head=False,
-        **kwargs
+        **kwargs,
     ):
         super(DistillationSARLabelDecode, self).__init__(
             character_dict_path, use_space_char

ppocr/postprocess/sast_postprocess.py

@@ -43,7 +43,7 @@ class SASTPostProcess(object):
         shrink_ratio_of_width=0.3,
         expand_scale=1.0,
         tcl_map_thresh=0.5,
-        **kwargs
+        **kwargs,
     ):
         self.score_thresh = score_thresh
         self.nms_thresh = nms_thresh

ppocr/postprocess/table_postprocess.py

@@ -142,7 +142,7 @@ class TableMasterLabelDecode(TableLabelDecode):
         character_dict_path,
         box_shape="ori",
         merge_no_span_structure=True,
-        **kwargs
+        **kwargs,
     ):
         super(TableMasterLabelDecode, self).__init__(
             character_dict_path, merge_no_span_structure

ppocr/utils/save_load.py

@@ -204,7 +204,7 @@ def save_model(
     config,
     is_best=False,
     prefix="ppocr",
-    **kwargs
+    **kwargs,
 ):
     """
     save model to the target path

ppstructure/table/table_metric/table_metric.py

@@ -145,7 +145,7 @@ class TEDS(object):
                 int(node.attrib.get("colspan", "1")),
                 int(node.attrib.get("rowspan", "1")),
                 cell,
-                *deque()
+                *deque(),
             )
         else:
             new_node = TableTree(node.tag, None, None, None, *deque())

test_tipc/supplementary/optimizer.py

@@ -99,7 +99,7 @@ class ExponentialWarmup(LinearWarmup):
         decay_epochs=2.4,
         decay_rate=0.97,
         warmup_epoch=5,
-        **kwargs
+        **kwargs,
     ):
         warmup_step = warmup_epoch * step_each_epoch
         start_lr = 0.0
@@ -222,7 +222,7 @@ class RMSProp(object):
         epsilon=1e-6,
         parameter_list=None,
         regularization=None,
-        **args
+        **args,
     ):
         super(RMSProp, self).__init__()
         self.learning_rate = learning_rate