fix eval

ppocr/data/rec/dataset_traversal.py

@@ -41,6 +41,7 @@ class LMDBReader(object):
         self.loss_type = params['loss_type']
         self.max_text_length = params['max_text_length']
         self.mode = params['mode']
+        self.drop_last = False
         if "tps" in params:
             self.tps = True
         if params['mode'] == 'train':
@@ -112,7 +113,8 @@ class LMDBReader(object):
                         img=img,
                         image_shape=self.image_shape,
                         char_ops=self.char_ops,
-                        tps=self.tps)
+                        tps=self.tps,
+                        infer_mode=True)
                     yield norm_img
             else:
                 lmdb_sets = self.load_hierarchical_lmdb_dataset()
@@ -132,9 +134,13 @@ class LMDBReader(object):
                             if sample_info is None:
                                 continue
                             img, label = sample_info
-                            outs = process_image(img, self.image_shape, label,
-                                                 self.char_ops, self.loss_type,
-                                                 self.max_text_length)
+                            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)
                             if outs is None:
                                 continue
                             yield outs
@@ -154,7 +160,7 @@ class LMDBReader(object):
                 if len(batch_outs) != 0:
                     yield batch_outs
 
-        if self.mode != 'train' and self.infer_img is None:
+        if self.infer_img is None:
             return batch_iter_reader
         return sample_iter_reader
 
@@ -174,6 +180,7 @@ class SimpleReader(object):
         self.max_text_length = params['max_text_length']
         self.mode = params['mode']
         self.infer_img = params['infer_img']
+        self.drop_last = False
         if params['mode'] == 'train':
             self.batch_size = params['train_batch_size_per_card']
             self.drop_last = params['drop_last']

ppocr/data/rec/img_tools.py

@@ -93,11 +93,12 @@ def process_image(img,
                   char_ops=None,
                   loss_type=None,
                   max_text_length=None,
-                  tps=None):
-    if char_ops.character_type == "en":
+                  tps=None,
+                  infer_mode=False):
+    if not infer_mode or char_ops.character_type == "en":
         norm_img = resize_norm_img(img, image_shape)
     else:
-        if tps:
+        if tps != None and char_ops.character_type == "ch":
             image_shape = [3, 32, 320]
             norm_img = resize_norm_img(img, image_shape)
         else:

ppocr/modeling/architectures/rec_model.py

@@ -31,6 +31,7 @@ class RecModel(object):
         char_num = global_params['char_ops'].get_char_num()
         global_params['char_num'] = char_num
         self.char_type = global_params['character_type']
+        self.infer_img = global_params['infer_img']
         if "TPS" in params:
             tps_params = deepcopy(params["TPS"])
             tps_params.update(global_params)
@@ -87,7 +88,7 @@ class RecModel(object):
                 use_double_buffer=True,
                 iterable=False)
         else:
-            if self.char_type == "ch":
+            if self.char_type == "ch" and self.infer_img:
                 image_shape[-1] = -1
                 if self.tps != None:
                     logger.info(
@@ -96,8 +97,7 @@ class RecModel(object):
                         "We set default shape=[3,32,320], it may affect the inference effect"
                     )
                     image_shape[-1] = 320
-                image = fluid.data(
-                    name='image', shape=image_shape, dtype='float32')
+            image = fluid.data(name='image', shape=image_shape, dtype='float32')
             labels = None
             loader = None
         return image, labels, loader