add ano

ppocr/modeling/heads/rec_ctc_head.py

@@ -27,6 +27,14 @@ import numpy as np
 
 
 class CTCPredict(object):
+    """
+    CTC predict
+
+    Args:
+        params(object): Params from yaml file and settings from command line
+
+    """
+
     def __init__(self, params):
         super(CTCPredict, self).__init__()
         self.char_num = params['char_num']

ppocr/utils/character.py

@@ -149,12 +149,16 @@ def cal_predicts_accuracy(char_ops,
     Args:
         char_ops: CharacterOps
         preds: preds result,text index
-        preds_lod:
-        labels:
-        labels_lod:
-        is_remove_duplicate:
+        preds_lod: lod tensor of preds
+        labels: label of input image, text index
+        labels_lod:  lod tensor of label
+        is_remove_duplicate: Whether to remove duplicate characters,
+                                 The default is False
 
     Return:
+        acc: The accuracy of test set
+        acc_num: The correct number of samples predicted
+        img_num: The total sample number of the test set
 
     """
     acc_num = 0
@@ -178,6 +182,16 @@ def cal_predicts_accuracy(char_ops,
 
 
 def convert_rec_attention_infer_res(preds):
+    """
+    Convert recognition attention predict result with lod information
+
+    Args:
+        preds: the output of the model
+
+    Return:
+        convert_ids: A 1-D Tensor represents all the predicted results.
+        target_lod: The lod information of the predicted results
+    """
     img_num = preds.shape[0]
     target_lod = [0]
     convert_ids = []
@@ -195,6 +209,16 @@ def convert_rec_attention_infer_res(preds):
 
 
 def convert_rec_label_to_lod(ori_labels):
+    """
+    Convert recognition label to lod tensor
+
+    Args:
+        ori_labels: origin labels of total images
+    Return:
+        convert_ids: A 1-D Tensor represents all labels
+        target_lod: The lod information of the labels
+
+    """
     img_num = len(ori_labels)
     target_lod = [0]
     convert_ids = []

tools/eval_utils/eval_rec_utils.py

@@ -83,7 +83,9 @@ def eval_rec_run(exe, config, eval_info_dict, mode):
 
 
 def test_rec_benchmark(exe, config, eval_info_dict):
-    " 评估lmdb 数据"
+    """
+    eval rec benchmark
+    """
     eval_data_list = ['IIIT5k_3000', 'SVT', 'IC03_860', 'IC03_867', \
                       'IC13_857', 'IC13_1015', 'IC15_1811', 'IC15_2077', 'SVTP', 'CUTE80']
     eval_data_dir = config['TestReader']['lmdb_sets_dir']

tools/program.py

@@ -35,6 +35,10 @@ from ppocr.utils.character import cal_predicts_accuracy
 
 
 class ArgsParser(ArgumentParser):
+    """
+    Parase arguments
+    """
+
     def __init__(self):
         super(ArgsParser, self).__init__(
             formatter_class=RawDescriptionHelpFormatter)
@@ -61,7 +65,9 @@ class ArgsParser(ArgumentParser):
 
 
 class AttrDict(dict):
-    """Single level attribute dict, NOT recursive"""
+    """
+    Single level attribute dict, NOT recursive
+    """
 
     def __init__(self, **kwargs):
         super(AttrDict, self).__init__()
@@ -146,21 +152,22 @@ def check_gpu(use_gpu):
 def build(config, main_prog, startup_prog, mode):
     """
     Build a program using a model and an optimizer
-        1. create feeds
-        2. create a dataloader
-        3. create a model
-        4. create fetchs
-        5. create an optimizer
+        1. create a dataloader
+        2. create a model
+        3. create fetchs
+        4. create an optimizer
 
     Args:
         config(dict): config
         main_prog(): main program
         startup_prog(): startup program
-        is_train(bool): train or valid
+        mode(str): train or valid
 
     Returns:
         dataloader(): a bridge between the model and the data
-        fetchs(dict): dict of model outputs(included loss and measures)
+        fetch_name_list(dict): dict of model outputs(included loss and measures)
+        fetch_varname_list(list): list of outputs' varname
+        opt_loss_name(str): name of loss
     """
     with fluid.program_guard(main_prog, startup_prog):
         with fluid.unique_name.guard():
@@ -185,6 +192,19 @@ def build(config, main_prog, startup_prog, mode):
 
 def build_export(config, main_prog, startup_prog):
     """
+    Build a program for export model
+        1. create a model
+        2. create fetchs
+
+    Args:
+        config(dict): config
+        main_prog(): main program
+        startup_prog(): startup program
+
+    Returns:
+        feeded_var_names(list): list of feeded var names
+        target_vars(list): list of output[fetches_var]
+        fetches_var_name(list): list of fetch var name
     """
     with fluid.program_guard(main_prog, startup_prog):
         with fluid.unique_name.guard():
@@ -212,6 +232,16 @@ def create_multi_devices_program(program, loss_var_name):
 
 
 def train_eval_det_run(config, exe, train_info_dict, eval_info_dict):
+    """
+    Feed data to the model and fetch the measures and loss for detection
+
+    Args:
+        config: config
+        exe:
+        train_info_dict: information dict for training
+        eval_info_dict: information dict for evaluation
+
+    """
     train_batch_id = 0
     log_smooth_window = config['Global']['log_smooth_window']
     epoch_num = config['Global']['epoch_num']
@@ -277,6 +307,16 @@ def train_eval_det_run(config, exe, train_info_dict, eval_info_dict):
 
 
 def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
+    """
+    Feed data to the model and fetch the measures and loss for recognition
+
+    Args:
+        config: config
+        exe:
+        train_info_dict: information dict for training
+        eval_info_dict: information dict for evaluation
+
+    """
     train_batch_id = 0
     log_smooth_window = config['Global']['log_smooth_window']
     epoch_num = config['Global']['epoch_num']