修复ips计算过少的问题

ppocr/data/imaug/label_ops.py

@@ -123,7 +123,7 @@ def encode(self, text):
  [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
  length: length of each text. [batch_size]
  """
- if len(text) > self.max_text_len:
+ if len(text) == 0 or len(text) > self.max_text_len:
  return None
  if self.character_type == "en":
  text = text.lower()
@@ -138,9 +138,6 @@ def encode(self, text):
  return None
  return text_list
 
- def get_ignored_tokens(self):
- return [0] # for ctc blank
-
 
 class CTCLabelEncode(BaseRecLabelEncode):
  """ Convert between text-label and text-index """
@@ -160,8 +157,6 @@ def __call__(self, data):
  text = self.encode(text)
  if text is None:
  return None
- if len(text) > self.max_text_len:
- return None
  data['length'] = np.array(len(text))
  text = text + [0] * (self.max_text_len - len(text))
  data['label'] = np.array(text)
@@ -195,11 +190,6 @@ def __call__(self, text):
  text = self.encode(text)
  return text
 
- def get_ignored_tokens(self):
- beg_idx = self.get_beg_end_flag_idx("beg")
- end_idx = self.get_beg_end_flag_idx("end")
- return [beg_idx, end_idx]
-
  def get_beg_end_flag_idx(self, beg_or_end):
  if beg_or_end == "beg":
  idx = np.array(self.dict[self.beg_str])

tools/infer/predict_cls.py

@@ -82,7 +82,7 @@ def __call__(self, img_list):
 
  cls_res = [['', 0.0]] * img_num
  batch_num = self.cls_batch_num
- predict_time = 0
+ elapse = 0
  for beg_img_no in range(0, img_num, batch_num):
  end_img_no = min(img_num, beg_img_no + batch_num)
  norm_img_batch = []
@@ -107,14 +107,14 @@ def __call__(self, img_list):
  self.predictor.run([norm_img_batch])
  prob_out = self.output_tensors[0].copy_to_cpu()
  cls_res = self.postprocess_op(prob_out)
- elapse = time.time() - starttime
+ elapse += time.time() - starttime
  for rno in range(len(cls_res)):
  label, score = cls_res[rno]
  cls_res[indices[beg_img_no + rno]] = [label, score]
  if '180' in label and score > self.cls_thresh:
  img_list[indices[beg_img_no + rno]] = cv2.rotate(
  img_list[indices[beg_img_no + rno]], 1)
- return img_list, cls_res, predict_time
+ return img_list, cls_res, elapse
 
 
 def main(args):
@@ -143,10 +143,10 @@ def main(args):
  "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
  exit()
  for ino in range(len(img_list)):
- print("Predicts of %s:%s" % (valid_image_file_list[ino], cls_res[ino]))
- print("Total predict time for %d images, cost: %.3f" %
- (len(img_list), predict_time))
+ print("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
+ ino]))
+ print("Total predict time for {} images, cost: {:.3f}".format(
+ len(img_list), predict_time))
 
-
-if __name__ == "__main__":
- main(utility.parse_args())
+ if __name__ == "__main__":
+ main(utility.parse_args())

tools/infer/predict_det.py

@@ -174,15 +174,15 @@ def __call__(self, img):
  if img is None:
  logger.info("error in loading image:{}".format(image_file))
  continue
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
  dt_boxes, elapse = text_detector(img)
  if count > 0:
  total_time += elapse
  count += 1
- print("Predict time of %s:" % image_file, elapse)
+ print("Predict time of {}: {}".format(image_file, elapse))
  src_im = utility.draw_text_det_res(dt_boxes, image_file)
- img_name_pure = image_file.split("/")[-1]
- cv2.imwrite(
- os.path.join(draw_img_save, "det_res_%s" % img_name_pure), src_im)
+ img_name_pure = os.path.split(image_file)[-1]
+ img_path = os.path.join(draw_img_save,
+ "det_res_{}".format(img_name_pure))
+ cv2.imwrite(img_path, src_im)
  if count > 1:
  print("Avg Time:", total_time / (count - 1))

tools/infer/predict_rec.py

@@ -115,7 +115,7 @@ def __call__(self, img_list):
  rec_result = self.postprocess_op(preds)
  for rno in range(len(rec_result)):
  rec_res[indices[beg_img_no + rno]] = rec_result[rno]
- elapse = time.time() - starttime
+ elapse += time.time() - starttime
  return rec_res, elapse
 
 
@@ -145,9 +145,10 @@ def main(args):
  "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
  exit()
  for ino in range(len(img_list)):
- print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))
- print("Total predict time for %d images, cost: %.3f" %
- (len(img_list), predict_time))
+ print("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
+ ino]))
+ print("Total predict time for {} images, cost: {:.3f}".format(
+ len(img_list), predict_time))
 
 
 if __name__ == "__main__":

tools/program.py

@@ -236,7 +236,6 @@ def train(config,
  train_batch_cost = 0.0
  train_reader_cost = 0.0
  batch_sum = 0
- batch_start = time.time()
  # eval
  if global_step > start_eval_step and \
  (global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
@@ -275,6 +274,7 @@ def train(config,
  best_model_dict[main_indicator],
  global_step)
  global_step += 1
+ batch_start = time.time()
  if dist.get_rank() == 0:
  save_model(
  model,
@@ -333,20 +333,6 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
  return metirc
 
 
-def save_inference_mode(model, config, logger):
- model.eval()
- save_path = '{}/infer/{}'.format(config['Global']['save_model_dir'],
- config['Architecture']['model_type'])
- if config['Architecture']['model_type'] == 'rec':
- input_shape = [None, 3, 32, None]
- jit_model = paddle.jit.to_static(
- model, input_spec=[paddle.static.InputSpec(input_shape)])
- paddle.jit.save(jit_model, save_path)
- logger.info('inference model save to {}'.format(save_path))
-
- model.train()
-
-
 def preprocess():
  FLAGS = ArgsParser().parse_args()
  config = load_config(FLAGS.config)

tools/train.py

@@ -89,7 +89,6 @@ def main(config, device, logger, vdl_writer):
  program.train(config, train_dataloader, valid_dataloader, device, model,
  loss_class, optimizer, lr_scheduler, post_process_class,
  eval_class, pre_best_model_dict, logger, vdl_writer)
- program.save_inference_mode(model, config, logger)
 
 
 def test_reader(config, device, logger):