Data reader for api

demo/mnist/reader.py

@@ -0,0 +1,51 @@
+import os, sys
+import struct
+import numpy as np
+import paddle.v2 as paddle
+
+
+def load_data(filename, dir='./data/raw_data/'):
+    image = '-images-idx3-ubyte'
+    label = '-labels-idx1-ubyte'
+    if filename is 'train':
+        image_file = os.path.join(dir, filename + image)
+        label_file = os.path.join(dir, filename + label)
+    else:
+        image_file = os.path.join(dir, 't10k' + image)
+        label_file = os.path.join(dir, 't10k' + label)
+
+    with open(image_file, "rb") as f:
+        num_magic, n, num_row, num_col = struct.unpack(">IIII", f.read(16))
+        images = np.fromfile(f, 'ubyte', count=n * num_row * num_col).\
+            reshape(n, num_row * num_col).astype('float32')
+        images = images / 255.0 * 2.0 - 1.0
+
+    with open(label_file, "rb") as fn:
+        num_magic, num_label = struct.unpack(">II", fn.read(8))
+        labels = np.fromfile(fn, 'ubyte', count=num_label).astype('int32')
+
+    return images, labels
+
+
+def data(images, labels):
+    for i in xrange(len(labels)):
+        yield {"pixel": images[i, :], 'label': labels[i]}
+
+
+def main():
+    train_images, train_label = load_data('train')
+    train_gen = data(train_images, train_label)
+    train_data = paddle.data.CacheAllDataPool(train_gen, 128,
+                                              ['pixel', 'label'])
+
+    test_images, test_label = load_data('test')
+    test_gen = data(test_images[0:128], test_label[0:128])
+    test_data = paddle.data.CacheAllDataPool(test_gen, 128, ['pixel', 'label'],
+                                             False)
+
+    for data_batch in test_data:
+        print data_batch
+
+
+if __name__ == "__main__":
+    main()

python/paddle/v2/data.py

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+import collections
+import random
+
+__all__ = [
+    'IDataPool',
+    'CacheAllDataPool',
+]
+
+
+class IDataPool(object):
+    """
+    Interface of DataPool, but note that Python is using Duck-Typing, it is not
+    necessary to inherit this interface.
+
+    NOTE: For Paddle developer, NEVER CHECK isinstance(obj, IDataPool).
+
+    Basically contains two method,
+
+    * next(): User should return the next batch of data in pool. raise
+              StopIteration if there is no more data in pool.
+
+    * reset(): Reset the data pool to initial status.
+
+    The basic usage of this api is as same as normal Python iterator, like
+
+    ..  code-block:: python
+
+        pool = DataPool()
+
+        for batch in pool:
+            process_batch(batch)
+
+
+    NOTE: The Data Pool API is not thread-safe.
+    """
+
+    def __iter__(self):
+        self.reset()
+        return self
+
+    def next(self):
+        raise NotImplementedError()
+
+    def __next__(self):
+        return self.next()
+
+    def reset(self):
+        raise NotImplementedError()
+
+
+def input_order_mapper(iterable, input_order):
+    assert isinstance(input_order, collections.Sequence)
+    for each_input_name in input_order:
+        assert isinstance(each_input_name, basestring)
+
+    tmp = [None] * len(input_order)
+    for each_item in iterable:
+        for i in xrange(len(input_order)):
+            tmp[i] = each_item[input_order[i]]
+        yield tmp
+
+
+class CacheAllDataPool(IDataPool):
+    """
+    load all samples in memory.
+    """
+
+    def __init__(self, iterable, batch_size, input_order, shuffle=True):
+        self.__pool__ = list(
+            input_order_mapper(
+                iterable=iterable, input_order=input_order))
+        self.__batch_size__ = batch_size
+        self.__shuffle__ = shuffle
+        self.__idx__ = 0
+
+    def reset(self):
+        self.__idx__ = 0
+        if self.__shuffle__:
+            random.shuffle(self.__pool__)
+
+    def next(self):
+        if self.__idx__ >= len(self.__pool__):
+            raise StopIteration()
+
+        begin = self.__idx__
+        end = min(self.__idx__ + self.__batch_size__, len(self.__pool__))
+        self.__idx__ = end
+        return self.__pool__[begin:end]