Add more algorithms

others/paddlepaddle/lib/utils/data_utils.py

@@ -0,0 +1,64 @@
+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