Clean unnecessary files

others/GDAS/paddlepaddle/lib/utils/__init__.py

@@ -1,6 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
-from .meter        import AverageMeter
-from .time_utils   import time_for_file, time_string, time_string_short, time_print, convert_size2str, convert_secs2time
-from .data_utils   import reader_creator

others/GDAS/paddlepaddle/lib/utils/data_utils.py

@@ -1,64 +0,0 @@
-import random, tarfile
-import numpy, six
-from six.moves import cPickle as pickle
-from PIL import Image, ImageOps
-
-
-def train_cifar_augmentation(image):
-  # flip
-  if random.random() < 0.5: image1 = image.transpose(Image.FLIP_LEFT_RIGHT)
-  else:                     image1 = image
-  # random crop
-  image2 = ImageOps.expand(image1, border=4, fill=0)
-  i = random.randint(0, 40 - 32)
-  j = random.randint(0, 40 - 32)
-  image3 = image2.crop((j,i,j+32,i+32))
-  # to numpy
-  image3 = numpy.array(image3) / 255.0
-  mean   = numpy.array([x / 255 for x in [125.3, 123.0, 113.9]]).reshape(1, 1, 3)
-  std    = numpy.array([x / 255 for x in [63.0, 62.1, 66.7]]).reshape(1, 1, 3)
-  return (image3 - mean) / std
-
-
-def valid_cifar_augmentation(image):
-  image3 = numpy.array(image) / 255.0
-  mean   = numpy.array([x / 255 for x in [125.3, 123.0, 113.9]]).reshape(1, 1, 3)
-  std    = numpy.array([x / 255 for x in [63.0, 62.1, 66.7]]).reshape(1, 1, 3)
-  return (image3 - mean) / std
-
-
-def reader_creator(filename, sub_name, is_train, cycle=False):
-  def read_batch(batch):
-    data = batch[six.b('data')]
-    labels = batch.get(
-      six.b('labels'), batch.get(six.b('fine_labels'), None))
-    assert labels is not None
-    for sample, label in six.moves.zip(data, labels):
-      sample = sample.reshape(3, 32, 32)
-      sample = sample.transpose((1, 2, 0))
-      image  = Image.fromarray(sample)
-      if is_train:
-        ximage = train_cifar_augmentation(image)
-      else:
-        ximage = valid_cifar_augmentation(image)
-      ximage = ximage.transpose((2, 0, 1))
-      yield ximage.astype(numpy.float32), int(label)
-
-  def reader():
-    with tarfile.open(filename, mode='r') as f:
-      names = (each_item.name for each_item in f
-           if sub_name in each_item.name)
-
-      while True:
-        for name in names:
-          if six.PY2:
-            batch = pickle.load(f.extractfile(name))
-          else:
-            batch = pickle.load(
-              f.extractfile(name), encoding='bytes')
-          for item in read_batch(batch):
-            yield item
-        if not cycle:
-          break
-
-  return reader

others/GDAS/paddlepaddle/lib/utils/meter.py

@@ -1,23 +0,0 @@
-import time, sys
-import numpy as np
-
-
-class AverageMeter(object):     
-  """Computes and stores the average and current value"""    
-  def __init__(self):   
-    self.reset()
-  
-  def reset(self):
-    self.val   = 0.0
-    self.avg   = 0.0
-    self.sum   = 0.0
-    self.count = 0.0
-  
-  def update(self, val, n=1): 
-    self.val = val    
-    self.sum += val * n     
-    self.count += n
-    self.avg = self.sum / self.count    
-
-  def __repr__(self):
-    return ('{name}(val={val}, avg={avg}, count={count})'.format(name=self.__class__.__name__, **self.__dict__))

others/GDAS/paddlepaddle/lib/utils/time_utils.py

@@ -1,46 +0,0 @@
-import time, sys
-import numpy as np
-
-def time_for_file():
-  ISOTIMEFORMAT='%d-%h-at-%H-%M-%S'
-  return '{}'.format(time.strftime( ISOTIMEFORMAT, time.gmtime(time.time()) ))
-
-def time_string():
-  ISOTIMEFORMAT='%Y-%m-%d %X'
-  string = '[{}]'.format(time.strftime( ISOTIMEFORMAT, time.gmtime(time.time()) ))
-  return string
-
-def time_string_short():
-  ISOTIMEFORMAT='%Y%m%d'
-  string = '{}'.format(time.strftime( ISOTIMEFORMAT, time.gmtime(time.time()) ))
-  return string
-
-def time_print(string, is_print=True):
-  if (is_print):
-    print('{} : {}'.format(time_string(), string))
-
-def convert_size2str(torch_size):
-  dims = len(torch_size)
-  string = '['
-  for idim in range(dims):
-    string = string + ' {}'.format(torch_size[idim])
-  return string + ']'
-  
-def convert_secs2time(epoch_time, return_str=False):    
-  need_hour = int(epoch_time / 3600)
-  need_mins = int((epoch_time - 3600*need_hour) / 60)  
-  need_secs = int(epoch_time - 3600*need_hour - 60*need_mins)
-  if return_str:
-    str = '[{:02d}:{:02d}:{:02d}]'.format(need_hour, need_mins, need_secs)
-    return str
-  else:
-    return need_hour, need_mins, need_secs
-
-def print_log(print_string, log):
-  #if isinstance(log, Logger): log.log('{:}'.format(print_string))
-  if hasattr(log, 'log'): log.log('{:}'.format(print_string))
-  else:
-    print("{:}".format(print_string))
-    if log is not None:
-      log.write('{:}\n'.format(print_string))
-      log.flush()