Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into multi_languages

ppocr/data/simple_dataset.py

@@ -20,7 +20,7 @@ from .imaug import transform, create_operators
 
 
 class SimpleDataSet(Dataset):
-    def __init__(self, config, mode, logger):
+    def __init__(self, config, mode, logger, seed=None):
         super(SimpleDataSet, self).__init__()
         self.logger = logger
 
@@ -41,6 +41,7 @@ class SimpleDataSet(Dataset):
         self.data_dir = dataset_config['data_dir']
         self.do_shuffle = loader_config['shuffle']
 
+        self.seed = seed
         logger.info("Initialize indexs of datasets:%s" % label_file_list)
         self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
         self.data_idx_order_list = list(range(len(self.data_lines)))
@@ -55,6 +56,7 @@ class SimpleDataSet(Dataset):
         for idx, file in enumerate(file_list):
             with open(file, "rb") as f:
                 lines = f.readlines()
+                random.seed(self.seed)
                 lines = random.sample(lines,
                                       round(len(lines) * ratio_list[idx]))
                 data_lines.extend(lines)
@@ -62,6 +64,7 @@ class SimpleDataSet(Dataset):
 
     def shuffle_data_random(self):
         if self.do_shuffle:
+            random.seed(self.seed)
             random.shuffle(self.data_lines)
         return
 

ppocr/modeling/transforms/tps.py

@@ -213,16 +213,14 @@ class GridGenerator(nn.Layer):
 
     def build_P_paddle(self, I_r_size):
         I_r_height, I_r_width = I_r_size
-        I_r_grid_x = paddle.divide(
-            paddle.arange(
-                -I_r_width, I_r_width, 2, dtype='float64') + 1.0,
-            paddle.to_tensor(
-                I_r_width, dtype='float64'))
-        I_r_grid_y = paddle.divide(
-            paddle.arange(
-                -I_r_height, I_r_height, 2, dtype='float64') + 1.0,
-            paddle.to_tensor(
-                I_r_height, dtype='float64'))  # self.I_r_height
+        I_r_grid_x = (paddle.arange(
+            -I_r_width, I_r_width, 2, dtype='float64') + 1.0
+                      ) / paddle.to_tensor(np.array([I_r_width]))
+
+        I_r_grid_y = (paddle.arange(
+            -I_r_height, I_r_height, 2, dtype='float64') + 1.0
+                      ) / paddle.to_tensor(np.array([I_r_height]))
+
         # P: self.I_r_width x self.I_r_height x 2
         P = paddle.stack(paddle.meshgrid(I_r_grid_x, I_r_grid_y), axis=2)
         P = paddle.transpose(P, perm=[1, 0, 2])

ppocr/postprocess/rec_postprocess.py

@@ -109,7 +109,7 @@ class CTCLabelDecode(BaseRecLabelDecode):
 
         preds_idx = preds.argmax(axis=2)
         preds_prob = preds.max(axis=2)
-        text = self.decode(preds_idx, preds_prob)
+        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
         if label is None:
             return text
         label = self.decode(label)

tools/program.py

@@ -182,8 +182,8 @@ def train(config,
         start_epoch = 1
 
     for epoch in range(start_epoch, epoch_num + 1):
-        if epoch > 0:
-            train_dataloader = build_dataloader(config, 'Train', device, logger)
+        train_dataloader = build_dataloader(
+            config, 'Train', device, logger, seed=epoch)
         train_batch_cost = 0.0
         train_reader_cost = 0.0
         batch_sum = 0