Add more algorithms

others/paddlepaddle/lib/utils/__init__.py

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+##################################################
+# 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/paddlepaddle/lib/utils/data_utils.py

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+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/paddlepaddle/lib/utils/meter.py

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+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+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/paddlepaddle/lib/utils/time_utils.py

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+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+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()