Fixocr

configs/det/det_db_mv3.yml

@@ -4,7 +4,7 @@ Global:
   epoch_num: 1200
   log_smooth_window: 20
   print_batch_step: 2
-  save_model_dir: output
+  save_model_dir: ./output/det_db/
   save_epoch_step: 200
   eval_batch_step: 5000
   train_batch_size_per_card: 16
@@ -13,7 +13,7 @@ Global:
   reader_yml: ./configs/det/det_db_icdar15_reader.yml
   pretrain_weights: ./pretrain_models/MobileNetV3_pretrained/MobileNetV3_large_x0_5_pretrained/
   checkpoints:
-  save_res_path: ./output/predicts_db.txt
+  save_res_path: ./output/det_db/predicts_db.txt
   save_inference_dir:
 
 Architecture:

configs/det/det_db_r50_vd.yml

@@ -4,16 +4,18 @@ Global:
   epoch_num: 1200
   log_smooth_window: 20
   print_batch_step: 2
-  save_model_dir: output
+  save_model_dir: ./output/det_db/
   save_epoch_step: 200
   eval_batch_step: 5000
   train_batch_size_per_card: 8
   test_batch_size_per_card: 16
   image_shape: [3, 640, 640]
   reader_yml: ./configs/det/det_db_icdar15_reader.yml
   pretrain_weights: ./pretrain_models/ResNet50_vd_pretrained/
-  save_res_path: ./output/predicts_db.txt
-
+  save_res_path: ./output/det_db/predicts_db.txt
+  checkpoints:
+  save_inference_dir:
+
 Architecture:
   function: ppocr.modeling.architectures.det_model,DetModel
 

configs/det/det_east_mv3.yml

@@ -4,15 +4,17 @@ Global:
   epoch_num: 100000
   log_smooth_window: 20
   print_batch_step: 5
-  save_model_dir: output
+  save_model_dir: ./output/det_east/
   save_epoch_step: 200
   eval_batch_step: 5000
   train_batch_size_per_card: 16
   test_batch_size_per_card: 16
   image_shape: [3, 512, 512]
   reader_yml: ./configs/det/det_east_icdar15_reader.yml
   pretrain_weights: ./pretrain_models/MobileNetV3_pretrained/MobileNetV3_large_x0_5_pretrained/
-  save_res_path: ./output/predicts_east.txt
+  checkpoints:
+  save_res_path: ./output/det_east/predicts_east.txt
+  save_inference_dir:
 
 Architecture:
   function: ppocr.modeling.architectures.det_model,DetModel

configs/det/det_east_r50_vd.yml

@@ -4,16 +4,18 @@ Global:
   epoch_num: 100000
   log_smooth_window: 20
   print_batch_step: 5
-  save_model_dir: output
+  save_model_dir: ./output/det_east/
   save_epoch_step: 200
   eval_batch_step: 5000
   train_batch_size_per_card: 8
   test_batch_size_per_card: 16
   image_shape: [3, 512, 512]
   reader_yml: ./configs/det/det_east_icdar15_reader.yml
   pretrain_weights: ./pretrain_models/ResNet50_vd_pretrained/
-  save_res_path: ./output/predicts_east.txt
-
+  save_res_path: ./output/det_east/predicts_east.txt
+  checkpoints:
+  save_inference_dir:
+
 Architecture:
   function: ppocr.modeling.architectures.det_model,DetModel
 

configs/rec/rec_mv3_none_bilstm_ctc.yml

@@ -4,7 +4,7 @@ Global:
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: output
+  save_model_dir: output/rec_CRNN
   save_epoch_step: 3
   eval_batch_step: 2000
   train_batch_size_per_card: 256
@@ -14,7 +14,9 @@ Global:
   character_type: en
   loss_type: ctc
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
-  pretrain_weights: 
+  pretrain_weights:
+  checkpoints:
+  save_inference_dir:
 
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

configs/rec/rec_mv3_none_none_ctc.yml

@@ -4,7 +4,7 @@ Global:
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: output
+  save_model_dir: output/rec_Rosetta
   save_epoch_step: 3
   eval_batch_step: 2000
   train_batch_size_per_card: 256
@@ -15,7 +15,9 @@ Global:
   loss_type: ctc
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
   pretrain_weights: 
-
+  checkpoints:
+  save_inference_dir:
+
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel
 

configs/rec/rec_mv3_tps_bilstm_attn.yml

@@ -4,7 +4,7 @@ Global:
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: output
+  save_model_dir: output/rec_RARE
   save_epoch_step: 3
   eval_batch_step: 2000
   train_batch_size_per_card: 256
@@ -14,7 +14,9 @@ Global:
   character_type: en
   loss_type: attention
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
-  pretrain_weights: 
+  pretrain_weights:
+  checkpoints:
+  save_inference_dir:
 
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

configs/rec/rec_mv3_tps_bilstm_ctc.yml

@@ -4,7 +4,7 @@ Global:
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: output
+  save_model_dir: output/rec_STARNet
   save_epoch_step: 3
   eval_batch_step: 2000
   train_batch_size_per_card: 256
@@ -14,7 +14,10 @@ Global:
   character_type: en
   loss_type: ctc
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
-  pretrain_weights: 
+  pretrain_weights:
+  checkpoints:
+  save_inference_dir:
+
 
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

configs/rec/rec_r34_vd_none_bilstm_ctc.yml

@@ -4,7 +4,7 @@ Global:
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: output
+  save_model_dir: output/rec_CRNN
   save_epoch_step: 3
   eval_batch_step: 2000
   train_batch_size_per_card: 256
@@ -14,7 +14,9 @@ Global:
   character_type: en
   loss_type: ctc
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
-  pretrain_weights: 
+  pretrain_weights:
+  checkpoints:
+  save_inference_dir:
 
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

configs/rec/rec_r34_vd_none_none_ctc.yml

@@ -4,7 +4,7 @@ Global:
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: output
+  save_model_dir: output/rec_Rosetta
   save_epoch_step: 3
   eval_batch_step: 2000
   train_batch_size_per_card: 256
@@ -14,7 +14,9 @@ Global:
   character_type: en
   loss_type: ctc
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
-  pretrain_weights: 
+  pretrain_weights:
+  checkpoints:
+  save_inference_dir:
 
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

configs/rec/rec_r34_vd_tps_bilstm_attn.yml

@@ -4,7 +4,7 @@ Global:
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: output
+  save_model_dir: output/rec_RARE
   save_epoch_step: 3
   eval_batch_step: 2000
   train_batch_size_per_card: 256
@@ -14,7 +14,9 @@ Global:
   character_type: en
   loss_type: attention
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
-  pretrain_weights: 
+  pretrain_weights:
+  checkpoints:
+  save_inference_dir:
 
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

configs/rec/rec_r34_vd_tps_bilstm_ctc.yml

@@ -4,7 +4,7 @@ Global:
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
-  save_model_dir: output
+  save_model_dir: output/rec_STARNet
   save_epoch_step: 3
   eval_batch_step: 2000
   train_batch_size_per_card: 256
@@ -15,6 +15,8 @@ Global:
   loss_type: ctc
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
   pretrain_weights:
+  checkpoints:
+  save_inference_dir:
 
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

ppocr/modeling/heads/det_db_head.py

@@ -196,7 +196,7 @@ def __call__(self, conv_features, mode="train"):
         fuse = fluid.layers.concat(input=[p5, p4, p3, p2], axis=1)
         shrink_maps = self.binarize(fuse)
         if mode != "train":
-            return shrink_maps
+            return {"maps", shrink_maps}
         threshold_maps = self.thresh(fuse)
         binary_maps = self.step_function(shrink_maps, threshold_maps)
         y = fluid.layers.concat(

ppocr/postprocess/db_postprocess.py

@@ -128,6 +128,7 @@ def box_score_fast(self, bitmap, _box):
 
     def __call__(self, outs_dict, ratio_list):
         pred = outs_dict['maps']
+
         pred = pred[:, 0, :, :]
         segmentation = pred > self.thresh
 

tools/infer/predict_det.py

@@ -24,6 +24,7 @@
 import numpy as np
 import math
 import time
+import sys
 
 
 class TextDetector(object):
@@ -52,10 +53,10 @@ def __init__(self, args):
             utility.create_predictor(args, mode="det")
 
     def order_points_clockwise(self, pts):
-        #######
-        ## https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
-        ########
+        """
+        reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
         # sort the points based on their x-coordinates
+        """
         xSorted = pts[np.argsort(pts[:, 0]), :]
 
         # grab the left-most and right-most points from the sorted
@@ -141,7 +142,7 @@ def __call__(self, img):
             outs_dict['f_score'] = outputs[0]
             outs_dict['f_geo'] = outputs[1]
         else:
-            outs_dict['maps'] = [outputs[0]]
+            outs_dict['maps'] = outputs[0]
         dt_boxes_list = self.postprocess_op(outs_dict, [ratio_list])
         dt_boxes = dt_boxes_list[0]
         dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)

tools/program.py

@@ -219,6 +219,8 @@ def train_eval_det_run(config, exe, train_info_dict, eval_info_dict):
     eval_batch_step = config['Global']['eval_batch_step']
     save_epoch_step = config['Global']['save_epoch_step']
     save_model_dir = config['Global']['save_model_dir']
+    if not os.path.exists(save_model_dir):
+        os.makedirs(save_model_dir)
     train_stats = TrainingStats(log_smooth_window,
                                 train_info_dict['fetch_name_list'])
     best_eval_hmean = -1
@@ -282,6 +284,8 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
     eval_batch_step = config['Global']['eval_batch_step']
     save_epoch_step = config['Global']['save_epoch_step']
     save_model_dir = config['Global']['save_model_dir']
+    if not os.path.exists(save_model_dir):
+        os.makedirs(save_model_dir)
     train_stats = TrainingStats(log_smooth_window, ['loss', 'acc'])
     best_eval_acc = -1
     best_batch_id = 0