Merge pull request sjvasquez#2 from cclauss/modernize-python2-code

Modernize Python 2 code to get ready for Python 3

drawing.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 from collections import defaultdict
 
 import matplotlib.pyplot as plt
@@ -209,7 +210,7 @@ def draw(
 
  if save_file is not None:
  plt.savefig(save_file)
- print 'saved to {}'.format(save_file)
+ print('saved to {}'.format(save_file))
  else:
  plt.show()
  plt.close('all')

prepare_data.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 import os
 from xml.etree import ElementTree
 
@@ -54,7 +55,7 @@ def collect_data():
 
  stroke_fnames, transcriptions, writer_ids = [], [], []
  for i, fname in enumerate(fnames):
- print i, fname
+ print(i, fname)
  if fname == 'data/raw/ascii/z01/z01-000/z01-000z.txt':
  continue
 
@@ -98,10 +99,10 @@ def collect_data():
 
 
 if __name__ == '__main__':
- print 'traversing data directory...'
+ print('traversing data directory...')
  stroke_fnames, transcriptions, writer_ids = collect_data()
 
- print 'dumping to numpy arrays...'
+ print('dumping to numpy arrays...')
  x = np.zeros([len(stroke_fnames), drawing.MAX_STROKE_LEN, 3], dtype=np.float32)
  x_len = np.zeros([len(stroke_fnames)], dtype=np.int16)
  c = np.zeros([len(stroke_fnames), drawing.MAX_CHAR_LEN], dtype=np.int8)
@@ -111,7 +112,7 @@ def collect_data():
 
  for i, (stroke_fname, c_i, w_id_i) in enumerate(zip(stroke_fnames, transcriptions, writer_ids)):
  if i % 200 == 0:
- print i, '\t', '/', len(stroke_fnames)
+ print(i, '\t', '/', len(stroke_fnames))
  x_i = get_stroke_sequence(stroke_fname)
  valid_mask[i] = ~np.any(np.linalg.norm(x_i[:, :2], axis=1) > 60)
 

rnn.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 import os
 
 import numpy as np
@@ -20,9 +21,9 @@ def __init__(self, data_dir):
  self.test_df = DataFrame(columns=data_cols, data=data)
  self.train_df, self.val_df = self.test_df.train_test_split(train_size=0.95, random_state=2018)
 
- print 'train size', len(self.train_df)
- print 'val size', len(self.val_df)
- print 'test size', len(self.test_df)
+ print('train size', len(self.train_df))
+ print('val size', len(self.val_df))
+ print('test size', len(self.test_df))
 
  def train_batch_generator(self, batch_size):
  return self.batch_generator(

tf_base_model.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 from collections import deque
 from datetime import datetime
 import logging
@@ -146,7 +147,7 @@ def fit(self):
 
  # validation evaluation
  val_start = time.time()
- val_batch_df = val_generator.next()
+ val_batch_df = next(val_generator)
  val_feed_dict = {
  getattr(self, placeholder_name, None): data
  for placeholder_name, data in val_batch_df.items() if hasattr(self, placeholder_name)
@@ -173,19 +174,19 @@ def fit(self):
  if hasattr(self, 'monitor_tensors'):
  for name, tensor in self.monitor_tensors.items():
  [np_val] = self.session.run([tensor], feed_dict=val_feed_dict)
- print name
- print 'min', np_val.min()
- print 'max', np_val.max()
- print 'mean', np_val.mean()
- print 'std', np_val.std()
- print 'nans', np.isnan(np_val).sum()
- print
- print
- print
+ print(name)
+ print('min', np_val.min())
+ print('max', np_val.max())
+ print('mean', np_val.mean())
+ print('std', np_val.std())
+ print('nans', np.isnan(np_val).sum())
+ print()
+ print()
+ print()
 
  # train step
  train_start = time.time()
- train_batch_df = train_generator.next()
+ train_batch_df = next(train_generator)
  train_feed_dict = {
  getattr(self, placeholder_name, None): data
  for placeholder_name, data in train_batch_df.items() if hasattr(self, placeholder_name)
@@ -272,7 +273,7 @@ def predict(self, chunk_size=256):
  test_generator = self.reader.test_batch_generator(chunk_size)
  for i, test_batch_df in enumerate(test_generator):
  if i % 10 == 0:
- print i*len(test_batch_df)
+ print(i*len(test_batch_df))
 
  test_feed_dict = {
  getattr(self, placeholder_name, None): data
@@ -337,7 +338,10 @@ def init_logging(self, log_dir):
  date_str = datetime.now().strftime('%Y-%m-%d_%H-%M')
  log_file = 'log_{}.txt'.format(date_str)
 
- reload(logging) # bad
+ try: # Python 2
+ reload(logging) # bad
+ except NameError: # Python 3
+ import logging
  logging.basicConfig(
  filename=os.path.join(log_dir, log_file),
  level=self.logging_level,