replace print with logger

scripts/cnocr_evaluate.py

@@ -22,6 +22,7 @@ from __future__ import print_function
 import sys
 import os
 import time
+import logging
 import argparse
 from operator import itemgetter
 from pathlib import Path
@@ -32,6 +33,10 @@ import Levenshtein
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from cnocr import CnOcr
+from cnocr.utils import set_logger
+
+
+logger = set_logger(log_level=logging.INFO)
 
 
 def evaluate():
@@ -76,7 +81,7 @@ def evaluate():
     bad_cnt = 0
     badcases = []
     while start_idx < len(fn_labels_list):
-        print('start_idx: ', start_idx)
+        logger.info('start_idx: %d', start_idx)
         batch = fn_labels_list[start_idx : start_idx + args.batch_size]
         batch_img_fns = []
         batch_labels = []
@@ -95,7 +100,7 @@ def evaluate():
             batch_preds, batch_labels, batch_img_fns, alphabet
         ):
             if args.verbose:
-                print('\t'.join(bad_info))
+                logger.info('\t'.join(bad_info))
             distance = Levenshtein.distance(bad_info[1], bad_info[2])
             bad_info.insert(0, distance)
             badcases.append(bad_info)
@@ -133,7 +138,7 @@ def evaluate():
         for word, num in redundant_cnt.most_common():
             f.write('\t'.join([word, str(num)]) + '\n')
 
-    print(
+    logger.info(
         "number of total cases: %d, time cost per image: %f, number of bad cases: %d"
         % (len(fn_labels_list), model_time_cost / len(fn_labels_list), bad_cnt)
     )

scripts/cnocr_predict.py

@@ -21,11 +21,16 @@ from __future__ import print_function
 
 import sys
 import os
+import logging
 import argparse
 
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from cnocr import CnOcr
+from cnocr.utils import set_logger
+
+
+logger = set_logger(log_level=logging.INFO)
 
 
 def main():
@@ -48,7 +53,7 @@ def main():
         res = ocr.ocr_for_single_line(args.file)
     else:
         res = ocr.ocr(args.file)
-    print("Predicted Chars:", res)
+    logger.info("Predicted Chars: %s", res)
 
 
 if __name__ == '__main__':

scripts/cnocr_train.py

@@ -27,7 +27,7 @@ sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from cnocr.__version__ import __version__
 from cnocr.consts import EMB_MODEL_TYPES, SEQ_MODEL_TYPES
-from cnocr.utils import data_dir
+from cnocr.utils import data_dir, set_logger
 from cnocr.hyperparams.cn_hyperparams import CnHyperparams
 from cnocr.data_utils.data_iter import GrayImageIter
 from cnocr.data_utils.aug import FgBgFlipAug
@@ -36,6 +36,9 @@ from cnocr.fit.ctc_metrics import CtcMetrics
 from cnocr.fit.fit import fit
 
 
+logger = set_logger(log_level=logging.INFO)
+
+
 def parse_args():
     # Parse command line arguments
     parser = argparse.ArgumentParser()
@@ -114,7 +117,7 @@ def train_cnocr(args):
     logging.basicConfig(level=logging.DEBUG, format=head)
     args.model_name = args.emb_model_type + '-' + args.seq_model_type
     out_dir = os.path.join(args.out_model_dir, args.model_name)
-    print('save models to dir: %s' % out_dir, flush=True)
+    logger.info('save models to dir: %s' % out_dir)
     if not os.path.exists(out_dir):
         os.makedirs(out_dir)
     args.prefix = os.path.join(