add multi language config file imgs and dict

configs/rec/multi_language/generate_multi_language_configs.py

@@ -0,0 +1,108 @@
+import yaml
+from argparse import ArgumentParser, RawDescriptionHelpFormatter
+
+support_list = {
+    'it':'italian', 'xi':'spanish', 'pu':'portuguese', 'ru':'russian', 'ar':'arabic',
+    'ta':'tamil', 'ug':'uyghur', 'fa':'persian', 'ur':'urdu', 'rs':'serbian latin',
+    'oc':'occitan', 'rsc':'serbian cyrillic', 'bg':'bulgarian', 'uk':'ukranian', 'be':'belarusian',
+    'te':'telugu', 'ka':'kannada', 'chinese_cht':'chinese tradition','hi':'hindi','mr':'marathi',
+    'ne':'nepali',
+}
+global_config = yaml.load(open("./rec_multi_language_lite_train.yml", 'rb'), Loader=yaml.Loader)
+
+class ArgsParser(ArgumentParser):
+    def __init__(self):
+        super(ArgsParser, self).__init__(
+            formatter_class=RawDescriptionHelpFormatter)
+        self.add_argument(
+            "-o", "--opt", nargs='+', help="set configuration options")
+        self.add_argument(
+            "-t", "--type", nargs='+', help="set language type, support {}".format(support_list))
+        self.add_argument(
+            "--train",type=str,help="you can use this command to change the default path")
+        self.add_argument(
+            "--val",type=str,help="you can use this command to change the default path")
+        self.add_argument(
+            "--dict",type=str,help="you can use this command to change the default path")
+
+    def parse_args(self, argv=None):
+        args = super(ArgsParser, self).parse_args(argv)
+        args.opt = self._parse_opt(args.opt)
+        args.type,args.config = self._set_language(args.type)
+        return args
+
+    def _parse_opt(self, opts):
+        config = {}
+        if not opts:
+            return config
+        for s in opts:
+            s = s.strip()
+            k, v = s.split('=')
+            config[k] = yaml.load(v, Loader=yaml.Loader)
+        return config
+
+    def _set_language(self, type):
+        config={
+            'Global':{'character_dict_path':None,'save_model_dir':None},
+            'Train':{'dataset':{'label_file_list':None}},
+            'Eval': {'dataset': {'label_file_list': None}},
+        }
+
+        assert(type),"please use -t or --type to choose language type"
+        assert(
+                type[0] in support_list.keys()
+               ),"the sub_keys(-t or --type) can only be one of support list: \n{},\nbut get: {}, " \
+                 "please check your running command".format(support_list, type)
+        config['Global']['character_dict_path'] = 'ppocr/utils/dict/{}_dict.txt'.format(type[0])
+        config['Global']['save_model_dir'] = './output/rec_{}_lite'.format(type[0])
+        config['Train']['dataset']['label_file_list'] = ["./train_data/{}_train.txt".format(type[0])]
+        config['Eval']['dataset']['label_file_list'] = ["./train_data/{}_val.txt".format(type[0])]
+        return type[0],config
+
+
+def merge_config(global_config,new_config):
+    """
+    Merge config into global config.
+    Args:
+        global_config: Source config
+        new_config (dict): Config to be merged.
+    Returns: global config
+    """
+    for key, value in new_config.items():
+        if "." not in key:
+            if isinstance(value, dict) and key in global_config:
+                global_config[key].update(value)
+            else:
+                global_config[key] = value
+        else:
+            sub_keys = key.split('.')
+            assert (
+                sub_keys[0] in global_config
+            ), "the sub_keys can only be one of global_config: {}, but get: {}, " \
+               "please check your running command".format(
+                global_config.keys(), sub_keys[0])
+            cur = global_config[sub_keys[0]]
+            for idx, sub_key in enumerate(sub_keys[1:]):
+                if idx == len(sub_keys) - 2:
+                    cur[sub_key] = value
+                else:
+                    cur = cur[sub_key]
+    return global_config
+
+if __name__ == '__main__':
+    FLAGS = ArgsParser().parse_args()
+    global_config = merge_config(global_config,FLAGS.opt)
+    global_config = merge_config(global_config, FLAGS.config)
+    if FLAGS.train:
+        global_config['Train']['dataset']['label_file_list'] = [FLAGS.train]
+    if FLAGS.val:
+        global_config['Eval']['dataset']['label_file_list'] = [FLAGS.val]
+    if FLAGS.dict:
+        global_config['Global']['character_dict_path'] = FLAGS.dict
+
+    print("train list path set to:{}".format(global_config['Train']['dataset']['label_file_list'][0]))
+    print("Eval list path set to :{}".format(global_config['Eval']['dataset']['label_file_list'][0]))
+    print("dict path set to      :{}".format(global_config['Global']['character_dict_path']))
+    with open('rec_{}_lite_train.yml'.format(FLAGS.type), 'w') as f:
+        yaml.dump(dict(global_config), f, default_flow_style=False, sort_keys=False)
+    print("config file set to    :configs/rec/multi_language/rec_{}_lite_train.yml".format(FLAGS.type))

configs/rec/multi_language/rec_multi_language_lite_train.yml

@@ -0,0 +1,103 @@
+Global:
+  use_gpu: True
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_multi_language_lite
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model: 
+  checkpoints: 
+  save_inference_dir: 
+  use_visualdl: False
+  infer_img:
+  # for data or label process
+  character_dict_path: 
+  # Set the language of training, if set, select the default dictionary file
+  character_type: 
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/train_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/val_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8