modified infer single

configs/rec/rec_benchmark_reader.yml

@@ -10,4 +10,3 @@ EvalReader:
 TestReader:
   reader_function: ppocr.data.rec.dataset_traversal,LMDBReader
   lmdb_sets_dir: ./train_data/data_lmdb_release/evaluation/
-  infer_img: ./infer_img

configs/rec/rec_chinese_lite_train.yml

@@ -18,6 +18,8 @@ Global:
   pretrain_weights: 
   checkpoints:
   save_inference_dir:
+  infer_img:
+
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel
 

configs/rec/rec_chinese_reader.yml

@@ -11,4 +11,3 @@ EvalReader:
 
 TestReader:
   reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
-  infer_img: ./infer_img

configs/rec/rec_icdar15_reader.yml

@@ -11,4 +11,3 @@ EvalReader:
 
 TestReader:
   reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
-  infer_img: ./infer_img

configs/rec/rec_icdar15_train.yml

@@ -17,6 +17,8 @@ Global:
   pretrain_weights: ./pretrain_models/rec_mv3_none_bilstm_ctc/best_accuracy 
   checkpoints:
   save_inference_dir:
+  infer_img:
+
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel
 

configs/rec/rec_mv3_none_bilstm_ctc.yml

 Global:
   algorithm: CRNN
-  use_gpu: true
+  use_gpu: false
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
@@ -14,7 +14,7 @@ Global:
   character_type: en
   loss_type: ctc
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
-  pretrain_weights:
+  pretrain_weights: ./output/rec_CRNN/rec_mv3_none_bilstm_ctc/best_accuracy
   checkpoints:
   save_inference_dir:
   

configs/rec/rec_mv3_none_none_ctc.yml

@@ -17,6 +17,7 @@ Global:
   pretrain_weights: 
   checkpoints:
   save_inference_dir:
+  infer_img:
 
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

configs/rec/rec_mv3_tps_bilstm_attn.yml

@@ -17,7 +17,9 @@ Global:
   pretrain_weights:
   checkpoints:
   save_inference_dir:
-  
+  infer_img:
+
+
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel
 

configs/rec/rec_mv3_tps_bilstm_ctc.yml

@@ -17,6 +17,7 @@ Global:
   pretrain_weights:
   checkpoints:
   save_inference_dir:
+  infer_img:
 
   
 Architecture:

configs/rec/rec_r34_vd_none_bilstm_ctc.yml

@@ -17,7 +17,9 @@ Global:
   pretrain_weights:
   checkpoints:
   save_inference_dir:
-  
+  infer_img:
+
+
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel
 

configs/rec/rec_r34_vd_none_none_ctc.yml

@@ -17,6 +17,7 @@ Global:
   pretrain_weights:
   checkpoints:
   save_inference_dir:
+  infer_img:
   
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

configs/rec/rec_r34_vd_tps_bilstm_attn.yml

@@ -17,6 +17,8 @@ Global:
   pretrain_weights:
   checkpoints:
   save_inference_dir:
+  infer_img:
+
 
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

configs/rec/rec_r34_vd_tps_bilstm_ctc.yml

@@ -17,6 +17,8 @@ Global:
   pretrain_weights:
   checkpoints:
   save_inference_dir:
+  infer_img:
+
 
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

doc/recognition.md

@@ -184,7 +184,7 @@ python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkp
 
 ```
 # 预测英文结果
-python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
 ```
 
 预测图片：

ppocr/data/rec/dataset_traversal.py

@@ -43,11 +43,10 @@ class LMDBReader(object):
         self.mode = params['mode']
         if params['mode'] == 'train':
             self.batch_size = params['train_batch_size_per_card']
-        elif params['mode'] == "eval":
+        else:
             self.batch_size = params['test_batch_size_per_card']
-        elif params['mode'] == "test":
-            self.batch_size = 1
-            self.infer_img = params["infer_img"]
+        self.infer_img = params['infer_img']
+
     def load_hierarchical_lmdb_dataset(self):
         lmdb_sets = {}
         dataset_idx = 0
@@ -100,11 +99,11 @@ class LMDBReader(object):
             process_id = 0
 
         def sample_iter_reader():
-            if self.mode == 'test':
+            if self.infer_img is not None:
                 image_file_list = get_image_file_list(self.infer_img)
                 for single_img in image_file_list:
                     img = cv2.imread(single_img)
-                    if img.shape[-1]==1 or len(list(img.shape))==2:
+                    if img.shape[-1] == 1 or len(list(img.shape)) == 2:
                         img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
                     norm_img = process_image(img, self.image_shape)
                     yield norm_img
@@ -136,6 +135,7 @@ class LMDBReader(object):
                     if finish_read_num == len(lmdb_sets):
                         break
                 self.close_lmdb_dataset(lmdb_sets)
+
         def batch_iter_reader():
             batch_outs = []
             for outs in sample_iter_reader():
@@ -146,7 +146,7 @@ class LMDBReader(object):
             if len(batch_outs) != 0:
                 yield batch_outs
 
-        if self.mode != 'test':
+        if self.infer_img is None:
             return batch_iter_reader
         return sample_iter_reader
 
@@ -165,24 +165,22 @@ class SimpleReader(object):
         self.loss_type = params['loss_type']
         self.max_text_length = params['max_text_length']
         self.mode = params['mode']
+        self.infer_img = params['infer_img']
         if params['mode'] == 'train':
             self.batch_size = params['train_batch_size_per_card']
-        elif params['mode'] == 'eval':
-            self.batch_size = params['test_batch_size_per_card']
         else:
-            self.batch_size = 1
-            self.infer_img = params['infer_img']
+            self.batch_size = params['test_batch_size_per_card']
 
     def __call__(self, process_id):
         if self.mode != 'train':
             process_id = 0
 
         def sample_iter_reader():
-            if self.mode == 'test':
+            if self.infer_img is not None:
                 image_file_list = get_image_file_list(self.infer_img)
                 for single_img in image_file_list:
                     img = cv2.imread(single_img)
-                    if img.shape[-1]==1 or len(list(img.shape))==2:
+                    if img.shape[-1] == 1 or len(list(img.shape)) == 2:
                         img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
                     norm_img = process_image(img, self.image_shape)
                     yield norm_img
@@ -192,7 +190,7 @@ class SimpleReader(object):
                 img_num = len(label_infor_list)
                 img_id_list = list(range(img_num))
                 random.shuffle(img_id_list)
-                if sys.platform=="win32":
+                if sys.platform == "win32":
                     print("multiprocess is not fully compatible with Windows."
                           "num_workers will be 1.")
                     self.num_workers = 1
@@ -204,7 +202,7 @@ class SimpleReader(object):
                     if img is None:
                         logger.info("{} does not exist!".format(img_path))
                         continue
-                    if img.shape[-1]==1 or len(list(img.shape))==2:
+                    if img.shape[-1] == 1 or len(list(img.shape)) == 2:
                         img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
 
                     label = substr[1]
@@ -225,6 +223,6 @@ class SimpleReader(object):
             if len(batch_outs) != 0:
                 yield batch_outs
 
-        if self.mode != 'test':
+        if self.infer_img is None:
             return batch_iter_reader
         return sample_iter_reader

tools/eval_utils/eval_rec_utils.py

@@ -48,7 +48,7 @@ def eval_rec_run(exe, config, eval_info_dict, mode):
     total_sample_num = 0
     total_acc_num = 0
     total_batch_num = 0
-    if mode == "test":
+    if mode == "eval":
         is_remove_duplicate = False
     else:
         is_remove_duplicate = True
@@ -91,11 +91,11 @@ def test_rec_benchmark(exe, config, eval_info_dict):
     total_correct_number = 0
     eval_data_acc_info = {}
     for eval_data in eval_data_list:
-        config['EvalReader']['lmdb_sets_dir'] = \
+        config['TestReader']['lmdb_sets_dir'] = \
             eval_data_dir + "/" + eval_data
-        eval_reader = reader_main(config=config, mode="eval")
+        eval_reader = reader_main(config=config, mode="test")
         eval_info_dict['reader'] = eval_reader
-        metrics = eval_rec_run(exe, config, eval_info_dict, "eval")
+        metrics = eval_rec_run(exe, config, eval_info_dict, "test")
         total_evaluation_data_number += metrics['total_sample_num']
         total_correct_number += metrics['total_acc_num']
         eval_data_acc_info[eval_data] = metrics

tools/infer_rec.py

@@ -21,6 +21,7 @@ import time
 import multiprocessing
 import numpy as np
 
+
 def set_paddle_flags(**kwargs):
     for key, value in kwargs.items():
         if os.environ.get(key, None) is None:
@@ -78,13 +79,13 @@ def main():
     init_model(config, eval_prog, exe)
 
     blobs = reader_main(config, 'test')()
-    infer_img = config['TestReader']['infer_img']
+    infer_img = config['Global']['infer_img']
     infer_list = get_image_file_list(infer_img)
     max_img_num = len(infer_list)
     if len(infer_list) == 0:
         logger.info("Can not find img in infer_img dir.")
     for i in range(max_img_num):
-        print("infer_img:",infer_list[i])
+        print("infer_img:", infer_list[i])
         img = next(blobs)
         predict = exe.run(program=eval_prog,
                           feed={"image": img},
@@ -105,8 +106,8 @@ def main():
             preds_text = preds_text.reshape(-1)
             preds_text = char_ops.decode(preds_text)
 
-        print("\t index:",preds)
-        print("\t word :",preds_text)
+        print("\t index:", preds)
+        print("\t word :", preds_text)
 
     # save for inference model
     target_var = []