refine dynamic sampling (#1256)

ppocr/data/simple_dataset.py

@@ -32,12 +32,10 @@ class SimpleDataSet(Dataset):
         self.delimiter = dataset_config.get('delimiter', '\t')
         label_file_list = dataset_config.pop('label_file_list')
         data_source_num = len(label_file_list)
-        if data_source_num == 1:
-            ratio_list = [1.0]
-        else:
-            ratio_list = dataset_config.pop('ratio_list')
+        ratio_list = dataset_config.get("ratio_list", [1.0])
+        if isinstance(ratio_list, (float, int)):
+            ratio_list = [float(ratio_list)] * len(data_source_num)
 
-        assert sum(ratio_list) == 1, "The sum of the ratio_list should be 1."
         assert len(
             ratio_list
         ) == data_source_num, "The length of ratio_list should be the same as the file_list."
@@ -45,62 +43,32 @@ class SimpleDataSet(Dataset):
         self.do_shuffle = loader_config['shuffle']
 
         logger.info("Initialize indexs of datasets:%s" % label_file_list)
-        self.data_lines_list, data_num_list = self.get_image_info_list(
-            label_file_list)
-        self.data_idx_order_list = self.dataset_traversal(
-            data_num_list, ratio_list, batch_size)
-        self.shuffle_data_random()
-
+        self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
+        self.data_idx_order_list = list(range(len(self.data_lines)))
+        if mode.lower() == "train":
+            self.shuffle_data_random()
         self.ops = create_operators(dataset_config['transforms'], global_config)
 
-    def get_image_info_list(self, file_list):
+    def get_image_info_list(self, file_list, ratio_list):
         if isinstance(file_list, str):
             file_list = [file_list]
-        data_lines_list = []
-        data_num_list = []
-        for file in file_list:
+        data_lines = []
+        for idx, file in enumerate(file_list):
             with open(file, "rb") as f:
                 lines = f.readlines()
-                data_lines_list.append(lines)
-                data_num_list.append(len(lines))
-        return data_lines_list, data_num_list
-
-    def dataset_traversal(self, data_num_list, ratio_list, batch_size):
-        select_num_list = []
-        dataset_num = len(data_num_list)
-        for dno in range(dataset_num):
-            select_num = round(batch_size * ratio_list[dno])
-            select_num = max(select_num, 1)
-            select_num_list.append(select_num)
-        data_idx_order_list = []
-        cur_index_sets = [0] * dataset_num
-        while True:
-            finish_read_num = 0
-            for dataset_idx in range(dataset_num):
-                cur_index = cur_index_sets[dataset_idx]
-                if cur_index >= data_num_list[dataset_idx]:
-                    finish_read_num += 1
-                else:
-                    select_num = select_num_list[dataset_idx]
-                    for sno in range(select_num):
-                        cur_index = cur_index_sets[dataset_idx]
-                        if cur_index >= data_num_list[dataset_idx]:
-                            break
-                        data_idx_order_list.append((dataset_idx, cur_index))
-                        cur_index_sets[dataset_idx] += 1
-            if finish_read_num == dataset_num:
-                break
-        return data_idx_order_list
+                lines = random.sample(lines,
+                                      round(len(lines) * ratio_list[idx]))
+                data_lines.extend(lines)
+        return data_lines
 
     def shuffle_data_random(self):
         if self.do_shuffle:
-            for dno in range(len(self.data_lines_list)):
-                random.shuffle(self.data_lines_list[dno])
+            random.shuffle(self.data_lines)
         return
 
     def __getitem__(self, idx):
-        dataset_idx, file_idx = self.data_idx_order_list[idx]
-        data_line = self.data_lines_list[dataset_idx][file_idx]
+        file_idx = self.data_idx_order_list[idx]
+        data_line = self.data_lines[file_idx]
         try:
             data_line = data_line.decode('utf-8')
             substr = data_line.strip("\n").split(self.delimiter)