update infer doc and fix yml

configs/rec/rec_chinese_lite_train.yml

@@ -1,6 +1,6 @@
 Global:
  algorithm: CRNN
- use_gpu: false
+ use_gpu: true
  epoch_num: 3000
  log_smooth_window: 20
  print_batch_step: 10
@@ -16,7 +16,7 @@ Global:
  character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
  loss_type: ctc
  reader_yml: ./configs/rec/rec_chinese_reader.yml
- pretrain_weights: output/rec_CRNN/rec_mv3_crnn/best_accuracy
+ pretrain_weights:
  checkpoints:
  save_inference_dir:
  infer_img:

configs/rec/rec_icdar15_train.yml

@@ -15,7 +15,7 @@ Global:
  character_type: en
  loss_type: ctc
  reader_yml: ./configs/rec/rec_icdar15_reader.yml
- pretrain_weights:
+ pretrain_weights: ./pretrain_models/rec_mv3_none_bilstm_ctc/best_accuracy
  checkpoints:
  save_inference_dir:
  infer_img:

configs/rec/rec_mv3_none_bilstm_ctc.yml

@@ -1,6 +1,6 @@
 Global:
  algorithm: CRNN
- use_gpu: false
+ use_gpu: true
  epoch_num: 72
  log_smooth_window: 20
  print_batch_step: 10

configs/rec/rec_mv3_tps_bilstm_attn.yml

@@ -1,6 +1,6 @@
 Global:
  algorithm: RARE
- use_gpu: false
+ use_gpu: true
  epoch_num: 72
  log_smooth_window: 20
  print_batch_step: 10
@@ -12,8 +12,7 @@ Global:
  test_batch_size_per_card: 256
  image_shape: [3, 32, 100]
  max_text_length: 25
- character_type: ch
- character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
+ character_type: en
  loss_type: attention
  tps: true
  reader_yml: ./configs/rec/rec_benchmark_reader.yml

doc/inference.md

@@ -165,6 +165,12 @@ STAR-Net文本识别模型推理，可以执行如下命令：
 ```
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
 ```
+
+RARE 文本识别模型推理，可以执行如下命令：
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/sare/" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_algorithm="RARE"
+```
+
 ![](imgs_words_en/word_336.png)
 
 执行命令后，上面图像的识别结果如下：

tools/infer/predict_rec.py

@@ -32,10 +32,14 @@ def __init__(self, args):
  self.rec_image_shape = image_shape
  self.character_type = args.rec_char_type
  self.rec_batch_num = args.rec_batch_num
+ self.rec_algorithm = args.rec_algorithm
  char_ops_params = {}
  char_ops_params["character_type"] = args.rec_char_type
  char_ops_params["character_dict_path"] = args.rec_char_dict_path
- char_ops_params['loss_type'] = 'ctc'
+ if self.rec_algorithm != "RARE":
+ char_ops_params['loss_type'] = 'ctc'
+ else:
+ char_ops_params['loss_type'] = 'attention'
  self.char_ops = CharacterOps(char_ops_params)
 
  def resize_norm_img(self, img, max_wh_ratio):
@@ -81,7 +85,7 @@ def __call__(self, img_list):
  self.input_tensor.copy_from_cpu(norm_img_batch)
  self.predictor.zero_copy_run()
 
- if args.rec_algorithm != "RARE":
+ if self.rec_algorithm != "RARE":
  rec_idx_batch = self.output_tensors[0].copy_to_cpu()
  rec_idx_lod = self.output_tensors[0].lod()[0]
  predict_batch = self.output_tensors[1].copy_to_cpu()
@@ -104,6 +108,8 @@ def __call__(self, img_list):
  else:
  rec_idx_batch = self.output_tensors[0].copy_to_cpu()
  predict_batch = self.output_tensors[1].copy_to_cpu()
+ elapse = time.time() - starttime
+ predict_time += elapse
  for rno in range(len(rec_idx_batch)):
  end_pos = np.where(rec_idx_batch[rno, :] == 1)[0]
  if len(end_pos) <= 1:
@@ -112,8 +118,6 @@ def __call__(self, img_list):
  else:
  preds = rec_idx_batch[rno, 1:end_pos[1]]
  score = np.mean(predict_batch[rno, 1:end_pos[1]])
- #attenton index has 2 offset: beg and end
- preds = preds - 2
  preds_text = self.char_ops.decode(preds)
  rec_res.append([preds_text, score])