Merge pull request #1235 from WenmuZhou/dygraph_rc

修复ips计算过少的问题

ppocr/data/imaug/label_ops.py

@@ -123,7 +123,7 @@ class BaseRecLabelEncode(object):
                     [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 @@ class BaseRecLabelEncode(object):
             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 @@ class CTCLabelEncode(BaseRecLabelEncode):
         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 @@ class AttnLabelEncode(BaseRecLabelEncode):
         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 @@ class TextClassifier(object):
 
         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 @@ class TextClassifier(object):
                 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 @@ if __name__ == "__main__":
         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 @@ class TextRecognizer(object):
             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):