update common pre-commit configs and commit the results of running pre-commit run -a (#12516)

.pre-commit-config.yaml

@@ -1,26 +1,22 @@
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: a11d9314b22d8f8c7556443875b731ef05965464
+    rev: v4.6.0
     hooks:
+    -   id: check-added-large-files
+        args: ['--maxkb=512']
+    -   id: check-case-conflict
     -   id: check-merge-conflict
     -   id: check-symlinks
     -   id: detect-private-key
-        files: (?!.*paddle)^.*$
     -   id: end-of-file-fixer
-        files: \.md$
     -   id: trailing-whitespace
-        files: \.md$
+        files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|py|md)$
 -   repo: https://github.com/Lucas-C/pre-commit-hooks
-    rev: v1.0.1
+    rev: v1.5.1
     hooks:
-    -   id: forbid-crlf
-        files: \.md$
     -   id: remove-crlf
-        files: \.md$
-    -   id: forbid-tabs
-        files: \.md$
     -   id: remove-tabs
-        files: \.md$
+        files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|py|md)$
 -   repo: local
     hooks:
     -   id: clang-format
@@ -31,7 +27,7 @@ repos:
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$
 # For Python files
 -   repo: https://github.com/psf/black.git
-    rev: 23.3.0
+    rev: 24.4.2
     hooks:
     -   id: black
         files: (.*\.(py|pyi|bzl)|BUILD|.*\.BUILD|WORKSPACE)$
@@ -47,4 +43,3 @@ repos:
             - --show-source
             - --statistics
         exclude: ^benchmark/|^test_tipc/
-

benchmark/PaddleOCR_DBNet/tools/infer.py

@@ -194,7 +194,9 @@ class InferenceEngine(object):
                 box_list = [box_list[i] for i, v in enumerate(idx) if v]
                 score_list = [score_list[i] for i, v in enumerate(idx) if v]
             else:
-                idx = box_list.reshape(box_list.shape[0], -1).sum(axis=1) > 0  # 去掉全为0的框
+                idx = (
+                    box_list.reshape(box_list.shape[0], -1).sum(axis=1) > 0
+                )  # 去掉全为0的框
                 box_list, score_list = box_list[idx], score_list[idx]
         else:
             box_list, score_list = [], []

benchmark/run_benchmark_det.sh

@@ -59,4 +59,3 @@ source ${BENCHMARK_ROOT}/scripts/run_model.sh      # 在该脚本中会对符合
 _set_params $@
 #_train      # 如果只想产出训练log,不解析,可取消注释
 _run         # 该函数在run_model.sh中,执行时会调用_train; 如果不联调只想要产出训练log可以注掉本行,提交时需打开
-

benchmark/run_det.sh

@@ -34,6 +34,3 @@ for model_mode in ${model_mode_list[@]}; do
             done
       done
 done
-
-
-

configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh_udml.yml

@@ -173,5 +173,3 @@ Eval:
     drop_last: False
     batch_size_per_card: 8
     num_workers: 8
-
-

configs/rec/multi_language/generate_multi_language_configs.py

@@ -198,9 +198,9 @@ class ArgsParser(ArgumentParser):
             lang = "cyrillic"
         elif lang in devanagari_lang:
             lang = "devanagari"
-        global_config["Global"][
+        global_config["Global"]["character_dict_path"] = (
-            "character_dict_path"
+            "ppocr/utils/dict/{}_dict.txt".format(lang)
-        ] = "ppocr/utils/dict/{}_dict.txt".format(lang)
+        )
         global_config["Global"]["save_model_dir"] = "./output/rec_{}_lite".format(lang)
         global_config["Train"]["dataset"]["label_file_list"] = [
             "train_data/{}_train.txt".format(lang)

configs/rec/rec_satrn.yml

@@ -114,4 +114,3 @@ Eval:
     batch_size_per_card: 128 
     num_workers: 4
     use_shared_memory: False
-

configs/sr/sr_telescope.yml

@@ -81,4 +81,3 @@ Eval:
     drop_last: False
     batch_size_per_card: 16
     num_workers: 4
-

configs/sr/sr_tsrn_transformer_strock.yml

@@ -82,4 +82,3 @@ Eval:
     drop_last: False
     batch_size_per_card: 16
     num_workers: 4
-

deploy/android_demo/.gitignore

@@ -6,4 +6,3 @@
 /build
 /captures
 .externalNativeBuild
-

deploy/android_demo/app/src/main/res/values/strings.xml

@@ -17,4 +17,3 @@
     <string name="DET_LONG_SIZE_DEFAULT">960</string>
     <string name="SCORE_THRESHOLD_DEFAULT">0.1</string>
 </resources>
-

deploy/cpp_infer/external-cmake/auto-log.cmake

@@ -11,4 +11,3 @@ FetchContent_Declare(
   GIT_TAG        main
 )
 FetchContent_MakeAvailable(extern_Autolog)
-

deploy/fastdeploy/android/app/src/main/res/drawable/btn_settings.xml

@@ -3,4 +3,3 @@
     <item android:state_pressed="true" android:drawable="@drawable/btn_settings_pressed"/>
     <item android:drawable="@drawable/btn_settings_default"/>
 </selector>
-

deploy/hubserving/kie_ser/config.json

@@ -13,4 +13,3 @@
     "use_multiprocess": false,
     "workers": 2
 }
-

deploy/hubserving/kie_ser_re/config.json

@@ -13,4 +13,3 @@
     "use_multiprocess": false,
     "workers": 2
 }
-

deploy/hubserving/ocr_system/config.json

@@ -13,4 +13,3 @@
     "use_multiprocess": false,
     "workers": 2
 }
-

deploy/hubserving/structure_layout/config.json

@@ -13,4 +13,3 @@
     "use_multiprocess": false,
     "workers": 2
 }
-

deploy/hubserving/structure_system/config.json

@@ -13,4 +13,3 @@
     "use_multiprocess": false,
     "workers": 2
 }
-

deploy/hubserving/structure_table/config.json

@@ -13,4 +13,3 @@
     "use_multiprocess": false,
     "workers": 2
 }
-

ppocr/data/imaug/drrg_targets.py

@@ -439,9 +439,9 @@ class DRRGTargets(object):
             )
 
         inner_center_sample_mask = np.zeros_like(center_sample_mask)
-        inner_center_sample_mask[
+        inner_center_sample_mask[margin : h - margin, margin : w - margin] = (
-            margin : h - margin, margin : w - margin
+            center_sample_mask[margin : h - margin, margin : w - margin]
-        ] = center_sample_mask[margin : h - margin, margin : w - margin]
+        )
         kernel_size = int(np.clip(max_rand_half_height, 7, 21))
         inner_center_sample_mask = cv2.erode(
             inner_center_sample_mask, np.ones((kernel_size, kernel_size), np.uint8)

ppocr/data/imaug/table_ops.py

@@ -48,10 +48,14 @@ class GenTableMask(object):
         in_text = False  # 是否遍历到了字符区内
         box_list = []
         for i in range(len(project_val_array)):
-            if in_text == False and project_val_array[i] > spilt_threshold:  # 进入字符区了
+            if (
+                in_text == False and project_val_array[i] > spilt_threshold
+            ):  # 进入字符区了
                 in_text = True
                 start_idx = i
-            elif project_val_array[i] <= spilt_threshold and in_text == True:  # 进入空白区了
+            elif (
+                project_val_array[i] <= spilt_threshold and in_text == True
+            ):  # 进入空白区了
                 end_idx = i
                 in_text = False
                 if end_idx - start_idx <= 2:
@@ -95,10 +99,14 @@ class GenTableMask(object):
         box_list = []
         spilt_threshold = 0
         for i in range(len(project_val_array)):
-            if in_text == False and project_val_array[i] > spilt_threshold:  # 进入字符区了
+            if (
+                in_text == False and project_val_array[i] > spilt_threshold
+            ):  # 进入字符区了
                 in_text = True
                 start_idx = i
-            elif project_val_array[i] <= spilt_threshold and in_text == True:  # 进入空白区了
+            elif (
+                project_val_array[i] <= spilt_threshold and in_text == True
+            ):  # 进入空白区了
                 end_idx = i
                 in_text = False
                 if end_idx - start_idx <= 2:

ppocr/losses/basic_loss.py

@@ -231,9 +231,7 @@ class DKDLoss(nn.Layer):
         pred_student = self._cat_mask(pred_student, gt_mask, other_mask)
         pred_teacher = self._cat_mask(pred_teacher, gt_mask, other_mask)
         log_pred_student = paddle.log(pred_student)
-        tckd_loss = self._kl_div(log_pred_student, pred_teacher) * (
+        tckd_loss = self._kl_div(log_pred_student, pred_teacher) * (self.temperature**2)
-            self.temperature**2
-        )
         pred_teacher_part2 = F.softmax(
             logits_teacher / self.temperature - 1000.0 * gt_mask, axis=1
         )

ppocr/losses/distillation_loss.py

@@ -113,9 +113,9 @@ class DistillationDMLLoss(DMLLoss):
                     loss = super().forward(out1, out2)
                 if isinstance(loss, dict):
                     for key in loss:
-                        loss_dict[
+                        loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1], idx)] = (
-                            "{}_{}_{}_{}".format(key, pair[0], pair[1], idx)
+                            loss[key]
-                        ] = loss[key]
+                        )
                 else:
                     loss_dict["{}_{}".format(self.name, idx)] = loss
             else:
@@ -218,9 +218,9 @@ class DistillationKLDivLoss(KLDivLoss):
                     loss = super().forward(out1, out2)
                 if isinstance(loss, dict):
                     for key in loss:
-                        loss_dict[
+                        loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1], idx)] = (
-                            "{}_{}_{}_{}".format(key, pair[0], pair[1], idx)
+                            loss[key]
-                        ] = loss[key]
+                        )
                 else:
                     loss_dict["{}_{}".format(self.name, idx)] = loss
             else:
@@ -329,9 +329,9 @@ class DistillationDKDLoss(DKDLoss):
                     loss = super().forward(out1, out2)
                 if isinstance(loss, dict):
                     for key in loss:
-                        loss_dict[
+                        loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1], idx)] = (
-                            "{}_{}_{}_{}".format(key, pair[0], pair[1], idx)
+                            loss[key]
-                        ] = loss[key]
+                        )
                 else:
                     loss_dict["{}_{}".format(self.name, idx)] = loss
             else:
@@ -472,9 +472,9 @@ class DistillationKLDivLoss(KLDivLoss):
                     loss = super().forward(out1, out2)
                 if isinstance(loss, dict):
                     for key in loss:
-                        loss_dict[
+                        loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1], idx)] = (
-                            "{}_{}_{}_{}".format(key, pair[0], pair[1], idx)
+                            loss[key]
-                        ] = loss[key]
+                        )
                 else:
                     loss_dict["{}_{}".format(self.name, idx)] = loss
             else:
@@ -583,9 +583,9 @@ class DistillationDKDLoss(DKDLoss):
                     loss = super().forward(out1, out2)
                 if isinstance(loss, dict):
                     for key in loss:
-                        loss_dict[
+                        loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1], idx)] = (
-                            "{}_{}_{}_{}".format(key, pair[0], pair[1], idx)
+                            loss[key]
-                        ] = loss[key]
+                        )
                 else:
                     loss_dict["{}_{}".format(self.name, idx)] = loss
             else:

ppocr/modeling/backbones/det_resnet.py

@@ -185,9 +185,11 @@ class ResNet(nn.Layer):
                     bottleneck_block = self.add_sublayer(
                         conv_name,
                         BottleneckBlock(
-                            num_channels=num_channels[block]
+                            num_channels=(
+                                num_channels[block]
                                 if i == 0
-                            else num_filters[block] * 4,
+                                else num_filters[block] * 4
+                            ),
                             num_filters=num_filters[block],
                             stride=2 if i == 0 and block != 0 else 1,
                             shortcut=shortcut,
@@ -208,9 +210,9 @@ class ResNet(nn.Layer):
                     basic_block = self.add_sublayer(
                         conv_name,
                         BasicBlock(
-                            num_channels=num_channels[block]
+                            num_channels=(
-                            if i == 0
+                                num_channels[block] if i == 0 else num_filters[block]
-                            else num_filters[block],
+                            ),
                             num_filters=num_filters[block],
                             stride=2 if i == 0 and block != 0 else 1,
                             shortcut=shortcut,

ppocr/modeling/backbones/det_resnet_vd.py

@@ -316,9 +316,11 @@ class ResNet_vd(nn.Layer):
                     bottleneck_block = self.add_sublayer(
                         "bb_%d_%d" % (block, i),
                         BottleneckBlock(
-                            in_channels=num_channels[block]
+                            in_channels=(
+                                num_channels[block]
                                 if i == 0
-                            else num_filters[block] * 4,
+                                else num_filters[block] * 4
+                            ),
                             out_channels=num_filters[block],
                             stride=2 if i == 0 and block != 0 else 1,
                             shortcut=shortcut,
@@ -339,9 +341,9 @@ class ResNet_vd(nn.Layer):
                     basic_block = self.add_sublayer(
                         "bb_%d_%d" % (block, i),
                         BasicBlock(
-                            in_channels=num_channels[block]
+                            in_channels=(
-                            if i == 0
+                                num_channels[block] if i == 0 else num_filters[block]
-                            else num_filters[block],
+                            ),
                             out_channels=num_filters[block],
                             stride=2 if i == 0 and block != 0 else 1,
                             shortcut=shortcut,

ppocr/modeling/backbones/det_resnet_vd_sast.py

@@ -261,9 +261,11 @@ class ResNet_SAST(nn.Layer):
                     bottleneck_block = self.add_sublayer(
                         "bb_%d_%d" % (block, i),
                         BottleneckBlock(
-                            in_channels=num_channels[block]
+                            in_channels=(
+                                num_channels[block]
                                 if i == 0
-                            else num_filters[block] * 4,
+                                else num_filters[block] * 4
+                            ),
                             out_channels=num_filters[block],
                             stride=2 if i == 0 and block != 0 else 1,
                             shortcut=shortcut,
@@ -284,9 +286,9 @@ class ResNet_SAST(nn.Layer):
                     basic_block = self.add_sublayer(
                         "bb_%d_%d" % (block, i),
                         BasicBlock(
-                            in_channels=num_channels[block]
+                            in_channels=(
-                            if i == 0
+                                num_channels[block] if i == 0 else num_filters[block]
-                            else num_filters[block],
+                            ),
                             out_channels=num_filters[block],
                             stride=2 if i == 0 and block != 0 else 1,
                             shortcut=shortcut,

ppocr/modeling/backbones/e2e_resnet_vd_pg.py

@@ -241,9 +241,11 @@ class ResNet(nn.Layer):
                     bottleneck_block = self.add_sublayer(
                         "bb_%d_%d" % (block, i),
                         BottleneckBlock(
-                            in_channels=num_channels[block]
+                            in_channels=(
+                                num_channels[block]
                                 if i == 0
-                            else num_filters[block] * 4,
+                                else num_filters[block] * 4
+                            ),
                             out_channels=num_filters[block],
                             stride=2 if i == 0 and block != 0 else 1,
                             shortcut=shortcut,
@@ -264,9 +266,9 @@ class ResNet(nn.Layer):
                     basic_block = self.add_sublayer(
                         "bb_%d_%d" % (block, i),
                         BasicBlock(
-                            in_channels=num_channels[block]
+                            in_channels=(
-                            if i == 0
+                                num_channels[block] if i == 0 else num_filters[block]
-                            else num_filters[block],
+                            ),
                             out_channels=num_filters[block],
                             stride=2 if i == 0 and block != 0 else 1,
                             shortcut=shortcut,

ppocr/modeling/backbones/rec_micronet.py

@@ -364,6 +364,7 @@ class DYMicroBlock(nn.Layer):
         if gs1[0] == 0:
             self.layers = nn.Sequential(
                 DepthSpatialSepConv(inp, t1, kernel_size, stride),
+                (
                     DYShiftMax(
                         hidden_dim2,
                         hidden_dim2,
@@ -376,12 +377,16 @@ class DYMicroBlock(nn.Layer):
                         expansion=False,
                     )
                     if y2 > 0
-                else nn.ReLU6(),
+                    else nn.ReLU6()
+                ),
                 ChannelShuffle(gs1[1]) if shuffle else nn.Sequential(),
+                (
                     ChannelShuffle(hidden_dim2 // 2)
                     if shuffle and y2 != 0
-                else nn.Sequential(),
+                    else nn.Sequential()
+                ),
                 GroupConv(hidden_dim2, oup, (g1, g2)),
+                (
                     DYShiftMax(
                         oup,
                         oup,
@@ -394,15 +399,19 @@ class DYMicroBlock(nn.Layer):
                         expansion=False,
                     )
                     if y3 > 0
-                else nn.Sequential(),
+                    else nn.Sequential()
+                ),
                 ChannelShuffle(g2) if shuffle else nn.Sequential(),
+                (
                     ChannelShuffle(oup // 2)
                     if shuffle and oup % 2 == 0 and y3 != 0
-                else nn.Sequential(),
+                    else nn.Sequential()
+                ),
             )
         elif g2 == 0:
             self.layers = nn.Sequential(
                 GroupConv(inp, hidden_dim2, gs1),
+                (
                     DYShiftMax(
                         hidden_dim2,
                         hidden_dim2,
@@ -415,11 +424,13 @@ class DYMicroBlock(nn.Layer):
                         expansion=False,
                     )
                     if y3 > 0
-                else nn.Sequential(),
+                    else nn.Sequential()
+                ),
             )
         else:
             self.layers = nn.Sequential(
                 GroupConv(inp, hidden_dim2, gs1),
+                (
                     DYShiftMax(
                         hidden_dim2,
                         hidden_dim2,
@@ -432,12 +443,16 @@ class DYMicroBlock(nn.Layer):
                         expansion=False,
                     )
                     if y1 > 0
-                else nn.ReLU6(),
+                    else nn.ReLU6()
+                ),
                 ChannelShuffle(gs1[1]) if shuffle else nn.Sequential(),
+                (
                     DepthSpatialSepConv(hidden_dim2, (1, 1), kernel_size, stride)
                     if depthsep
-                else DepthConv(hidden_dim2, hidden_dim2, kernel_size, stride),
+                    else DepthConv(hidden_dim2, hidden_dim2, kernel_size, stride)
+                ),
                 nn.Sequential(),
+                (
                     DYShiftMax(
                         hidden_dim2,
                         hidden_dim2,
@@ -450,28 +465,35 @@ class DYMicroBlock(nn.Layer):
                         expansion=True,
                     )
                     if y2 > 0
-                else nn.ReLU6(),
+                    else nn.ReLU6()
+                ),
+                (
                     ChannelShuffle(hidden_dim2 // 4)
                     if shuffle and y1 != 0 and y2 != 0
-                else nn.Sequential()
+                    else (
+                        nn.Sequential()
                         if y1 == 0 and y2 == 0
-                else ChannelShuffle(hidden_dim2 // 2),
+                        else ChannelShuffle(hidden_dim2 // 2)
+                    )
+                ),
                 GroupConv(hidden_dim2, oup, (g1, g2)),
+                (
                     DYShiftMax(
                         oup,
                         oup,
                         act_max=2.0,
                         act_relu=False,
                         init_a=[1.0, 0.0],
-                    reduction=act_reduction // 2
+                        reduction=(
-                    if oup < hidden_dim2
+                            act_reduction // 2 if oup < hidden_dim2 else act_reduction
-                    else act_reduction,
+                        ),
                         init_b=[0.0, 0.0],
                         g=(g1, g2),
                         expansion=False,
                     )
                     if y3 > 0
-                else nn.Sequential(),
+                    else nn.Sequential()
+                ),
                 ChannelShuffle(g2) if shuffle else nn.Sequential(),
                 ChannelShuffle(oup // 2) if shuffle and y3 != 0 else nn.Sequential(),
             )

ppocr/modeling/backbones/rec_nrtr_mtb.py

@@ -35,9 +35,7 @@ class MTB(nn.Layer):
                     ),
                 )
                 self.block.add_sublayer("relu_{}".format(i), nn.ReLU())
-                self.block.add_sublayer(
+                self.block.add_sublayer("bn_{}".format(i), nn.BatchNorm2D(32 * (2**i)))
-                    "bn_{}".format(i), nn.BatchNorm2D(32 * (2**i))
-                )
 
     def forward(self, images):
         x = self.block(images)

ppocr/modeling/backbones/rec_resnet_vd.py

@@ -259,9 +259,11 @@ class ResNet(nn.Layer):
                     bottleneck_block = self.add_sublayer(
                         "bb_%d_%d" % (block, i),
                         BottleneckBlock(
-                            in_channels=num_channels[block]
+                            in_channels=(
+                                num_channels[block]
                                 if i == 0
-                            else num_filters[block] * 4,
+                                else num_filters[block] * 4
+                            ),
                             out_channels=num_filters[block],
                             stride=stride,
                             shortcut=shortcut,
@@ -285,9 +287,9 @@ class ResNet(nn.Layer):
                     basic_block = self.add_sublayer(
                         "bb_%d_%d" % (block, i),
                         BasicBlock(
-                            in_channels=num_channels[block]
+                            in_channels=(
-                            if i == 0
+                                num_channels[block] if i == 0 else num_filters[block]
-                            else num_filters[block],
+                            ),
                             out_channels=num_filters[block],
                             stride=stride,
                             shortcut=shortcut,

ppocr/modeling/heads/kie_sdmgr_head.py

@@ -141,8 +141,7 @@ class GNNLayer(nn.Layer):
             )
             residuals.append(
                 (
-                    residual
+                    residual * cat_nodes[start : start + num**2].reshape([num, num, -1])
-                    * cat_nodes[start : start + num**2].reshape([num, num, -1])
                 ).sum(1)
             )
             start += num**2

ppocr/modeling/heads/self_attention.py

@@ -341,10 +341,12 @@ class PrePostProcessLayer(nn.Layer):
                 )
             elif cmd == "d":  # add dropout
                 self.functors.append(
-                    lambda x: F.dropout(x, p=dropout_rate, mode="downscale_in_infer")
+                    lambda x: (
+                        F.dropout(x, p=dropout_rate, mode="downscale_in_infer")
                         if dropout_rate
                         else x
                     )
+                )
 
     def forward(self, x, residual=None):
         for i, cmd in enumerate(self.process_cmd):

ppocr/modeling/transforms/tbsrn.py

@@ -150,7 +150,7 @@ class TBSRN(nn.Layer):
         upsample_block_num = int(math.log(scale_factor, 2))
         self.block1 = nn.Sequential(
             nn.Conv2D(in_planes, 2 * hidden_units, kernel_size=9, padding=4),
-            nn.PReLU()
+            nn.PReLU(),
             # nn.ReLU()
         )
         self.srb_nums = srb_nums

ppocr/postprocess/rec_postprocess.py

@@ -1133,9 +1133,11 @@ class VLLabelDecode(BaseRecLabelDecode):
             preds_idx = net_out[start_idx:end_idx].topk(1)[1][:, 0].tolist()
             preds_text = "".join(
                 [
+                    (
                         self.character[idx - 1]
                         if idx > 0 and idx <= len(self.character)
                         else ""
+                    )
                     for idx in preds_idx
                 ]
             )

ppstructure/table/predict_structure.py

@@ -45,12 +45,16 @@ def build_pre_process_list(args):
     pad_op = {"PaddingTableImage": {"size": [args.table_max_len, args.table_max_len]}}
     normalize_op = {
         "NormalizeImage": {
-            "std": [0.229, 0.224, 0.225]
+            "std": (
+                [0.229, 0.224, 0.225]
                 if args.table_algorithm not in ["TableMaster"]
-            else [0.5, 0.5, 0.5],
+                else [0.5, 0.5, 0.5]
-            "mean": [0.485, 0.456, 0.406]
+            ),
+            "mean": (
+                [0.485, 0.456, 0.406]
                 if args.table_algorithm not in ["TableMaster"]
-            else [0.5, 0.5, 0.5],
+                else [0.5, 0.5, 0.5]
+            ),
             "scale": "1./255.",
             "order": "hwc",
         }

test_tipc/common_func.sh

@@ -65,4 +65,3 @@ function status_check(){
         echo -e "\033[33m Run failed with command - ${model_name} - ${run_command} - ${log_path} \033[0m" | tee -a ${run_log}
     fi
 }
-

test_tipc/configs/ch_ppocr_mobile_v2_0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt

@@ -49,4 +49,3 @@ inference:tools/infer/predict_det.py
 null:null
 --benchmark:True
 null:null
-

test_tipc/configs/det_r50_vd_sast_icdar15_v2_0/det_r50_vd_sast_icdar2015.yml

@@ -108,4 +108,3 @@ Eval:
     drop_last: False
     batch_size_per_card: 1 # must be 1
     num_workers: 2
-

test_tipc/configs/rec_r31_robustscanner/train_infer_python.txt

@@ -51,4 +51,3 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict90.t
 null:null
 ===========================infer_benchmark_params==========================
 random_infer_input:[{float32,[3,48,160]}]
-

test_tipc/configs/sr_telescope/sr_telescope.yml

@@ -81,4 +81,3 @@ Eval:
     drop_last: False
     batch_size_per_card: 16
     num_workers: 4
-

test_tipc/supplementary/__init__.py

@@ -1 +0,0 @@
-

test_tipc/supplementary/mv3_distill.yml

@@ -28,4 +28,3 @@ TRAIN:
 VALID:
     batch_size: 64
     num_workers: 4
-

test_tipc/supplementary/mv3_large_x0_5.yml

@@ -46,4 +46,3 @@ TRAIN:
 VALID:
     batch_size: 64
     num_workers: 4
-

test_tipc/supplementary/test_tipc/test_train_python.sh

@@ -112,7 +112,3 @@ if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "whole_train_whole_infer
         done
     done
 fi
-
-
-
-

test_tipc/test_inference_python.sh

@@ -167,5 +167,3 @@ if [ ${MODE} = "whole_infer" ]; then
         Count=$(($Count + 1))
     done
 fi
-
-

test_tipc/test_ptq_inference_python.sh

@@ -155,4 +155,3 @@ if [ ${MODE} = "whole_infer" ]; then
         Count=$(($Count + 1))
     done
 fi
-