Adaptation of Chinese and r34/18

ppocr/data/rec/dataset_traversal.py

@@ -214,6 +214,8 @@ class SimpleReader(object):
         self.mode = params['mode']
         self.infer_img = params['infer_img']
         self.use_tps = False
+        if "num_heads" in params:
+            self.num_heads = params['num_heads']
         if "tps" in params:
             self.use_tps = True
         self.use_distort = False
@@ -251,12 +253,19 @@ class SimpleReader(object):
                     img = cv2.imread(single_img)
                     if img.shape[-1] == 1 or len(list(img.shape)) == 2:
                         img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-                    norm_img = process_image(
-                        img=img,
-                        image_shape=self.image_shape,
-                        char_ops=self.char_ops,
-                        tps=self.use_tps,
-                        infer_mode=True)
+                    if self.loss_type == 'srn':
+                        norm_img = process_image_srn(
+                            img=img,
+                            image_shape=self.image_shape,
+                            num_heads=self.num_heads,
+                            max_text_length=self.max_text_length)
+                    else:
+                        norm_img = process_image(
+                            img=img,
+                            image_shape=self.image_shape,
+                            char_ops=self.char_ops,
+                            tps=self.use_tps,
+                            infer_mode=True)
                     yield norm_img
             else:
                 with open(self.label_file_path, "rb") as fin:
@@ -286,14 +295,25 @@ class SimpleReader(object):
                         img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
 
                     label = substr[1]
-                    outs = process_image(
-                        img=img,
-                        image_shape=self.image_shape,
-                        label=label,
-                        char_ops=self.char_ops,
-                        loss_type=self.loss_type,
-                        max_text_length=self.max_text_length,
-                        distort=self.use_distort)
+                    if self.loss_type == "srn":
+                        outs = process_image_srn(
+                            img=img,
+                            image_shape=self.image_shape,
+                            num_heads=self.num_heads,
+                            max_text_length=self.max_text_length,
+                            label=label,
+                            char_ops=self.char_ops,
+                            loss_type=self.loss_type)
+
+                    else:
+                        outs = process_image(
+                            img=img,
+                            image_shape=self.image_shape,
+                            label=label,
+                            char_ops=self.char_ops,
+                            loss_type=self.loss_type,
+                            max_text_length=self.max_text_length,
+                            distort=self.use_distort)
                     if outs is None:
                         continue
                     yield outs

ppocr/data/rec/img_tools.py

@@ -410,7 +410,8 @@ def resize_norm_img_srn(img, image_shape):
 
 def srn_other_inputs(image_shape,
                      num_heads,
-                     max_text_length):
+                     max_text_length,
+                     char_num):
 
     imgC, imgH, imgW = image_shape
     feature_dim = int((imgH / 8) * (imgW / 8))
@@ -418,7 +419,7 @@ def srn_other_inputs(image_shape,
     encoder_word_pos = np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype('int64')
     gsrm_word_pos = np.array(range(0, max_text_length)).reshape((max_text_length, 1)).astype('int64')
 
-    lbl_weight = np.array([37] * max_text_length).reshape((-1,1)).astype('int64')
+    lbl_weight = np.array([int(char_num-1)] * max_text_length).reshape((-1,1)).astype('int64')
 
     gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) 
     gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape([-1, 1, max_text_length, max_text_length])
@@ -441,17 +442,18 @@ def process_image_srn(img,
                       loss_type=None):
     norm_img = resize_norm_img_srn(img, image_shape)
     norm_img = norm_img[np.newaxis, :]
+    char_num = char_ops.get_char_num()
+
     [lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
-        srn_other_inputs(image_shape, num_heads, max_text_length)
+        srn_other_inputs(image_shape, num_heads, max_text_length,char_num)
 
     if label is not None:
-        char_num = char_ops.get_char_num()
         text = char_ops.encode(label)
         if len(text) == 0 or len(text) > max_text_length:
             return None
         else:
             if loss_type == "srn":
-                text_padded = [37] * max_text_length
+                text_padded = [int(char_num-1)] * max_text_length
                 for i in range(len(text)):
                     text_padded[i] = text[i]
                     lbl_weight[i] = [1.0]

ppocr/modeling/backbones/rec_resnet50_fpn.py

@@ -81,6 +81,23 @@ class ResNet():
                         num_filters=num_filters[block],
                         stride=stride_list[block]  if i == 0 else 1, name=conv_name)
                 F.append(conv)
+        else:
+            for block in range(len(depth)):
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+
+                    if i == 0 and block != 0:
+                        stride = (2, 1)
+                    else:
+                        stride = (1, 1)
+
+                    conv = self.basic_block(
+                        input=conv,
+                        num_filters=num_filters[block],
+                        stride=stride,
+                        if_first=block == i == 0,
+                        name=conv_name)
+                F.append(conv)
 
         base = F[-1]
         for i in [-2, -3]:  

ppocr/utils/character.py

@@ -26,8 +26,6 @@ class CharacterOps(object):
         self.character_type = config['character_type']
         self.loss_type = config['loss_type']
         self.max_text_len = config['max_text_length']
-        if self.loss_type == "srn" and self.character_type != "en":
-            raise Exception("SRN can only support in character_type == en")
         if self.character_type == "en":
             self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
             dict_character = list(self.character_str)
@@ -160,13 +158,15 @@ def cal_predicts_accuracy_srn(char_ops,
     acc_num = 0
     img_num = 0
 
+    char_num = char_ops.get_char_num()
+
     total_len = preds.shape[0]
     img_num = int(total_len / max_text_len)
     for i in range(img_num):
         cur_label = []
         cur_pred = []
         for j in range(max_text_len):
-            if labels[j + i * max_text_len] != 37:  #0
+            if labels[j + i * max_text_len] != int(char_num-1):  #0
                 cur_label.append(labels[j + i * max_text_len][0])
             else:
                 break
@@ -178,7 +178,7 @@ def cal_predicts_accuracy_srn(char_ops,
             elif j == len(cur_label) and j == max_text_len:
                 acc_num += 1
                 break
-            elif j == len(cur_label) and preds[j + i * max_text_len][0] == 37:
+            elif j == len(cur_label) and preds[j + i * max_text_len][0] == int(char_num-1):
                 acc_num += 1
                 break
     acc = acc_num * 1.0 / img_num

tools/infer_rec.py

@@ -140,12 +140,12 @@ def main():
             preds = preds.reshape(-1)
             preds_text = char_ops.decode(preds)
         elif loss_type == "srn":
-            cur_pred = []
+            char_num = char_ops.get_char_num()
             preds = np.array(predict[0])
             preds = preds.reshape(-1)
             probs = np.array(predict[1])
             ind = np.argmax(probs, axis=1)
-            valid_ind = np.where(preds != 37)[0]
+            valid_ind = np.where(preds != int(char_num-1))[0]
             if len(valid_ind) == 0:
                 continue
             score = np.mean(probs[valid_ind, ind[valid_ind]])