Added Python conversion script, updated readme.txt.

Examples/Image/Miscellaneous/CIFAR-10/CifarConverter.py

@@ -0,0 +1,64 @@
+import os
+import sys
+import struct
+import cPickle as cp
+from PIL import Image
+import numpy as np
+import xml.etree.cElementTree as et
+import xml.dom.minidom
+
+imgSize = 32
+
+def saveImage(fname, data, label, mapFile, pad, **key_parms):
+    # data in CIFAR-10 dataset is in CHW format.
+    pixData = data.reshape((3, imgSize, imgSize))
+    if ('mean' in key_parms):
+        key_parms['mean'] += pixData
+
+    if pad > 0:
+        pixData = np.pad(pixData, ((0, 0), (pad, pad), (pad, pad)), mode = 'edge')
+
+    img = Image.new('RGB', (imgSize + 2 * pad, imgSize + 2 * pad))
+    pixels = img.load()
+    for x in range(img.size[0]):
+        for y in range(img.size[1]):
+            pixels[x, y] = (pixData[0][y][x], pixData[1][y][x], pixData[2][y][x])
+    img.save(fname)
+    mapFile.write("%s\t%d\n" % (fname, label))
+
+def saveMean(fname, data):
+    root = et.Element('opencv_storage')
+    et.SubElement(root, 'Channel').text = '3'
+    et.SubElement(root, 'Row').text = str(imgSize)
+    et.SubElement(root, 'Col').text = str(imgSize)
+    meanImg = et.SubElement(root, 'MeanImg', type_id='opencv-matrix')
+    et.SubElement(meanImg, 'rows').text = '1'
+    et.SubElement(meanImg, 'cols').text = str(imgSize * imgSize * 3)
+    et.SubElement(meanImg, 'dt').text = 'f'
+    et.SubElement(meanImg, 'data').text = ' '.join(['%e' % n for n in np.reshape(data, (imgSize * imgSize * 3))])
+
+    tree = et.ElementTree(root)
+    tree.write(fname)
+    x = xml.dom.minidom.parse(fname)
+    with open(fname, 'w') as f:
+        f.write(x.toprettyxml(indent = '  '))
+
+if __name__ == "__main__":
+    rootDir = r'C:\Data\CIFAR-10' + '\\'
+    data = {}
+    dataMean = np.zeros((3, imgSize, imgSize)) # mean is in CHW format.
+    with open(rootDir + 'train_map.txt', 'w') as mapFile:
+        for ifile in range(1, 6):
+            with open(r'C:\Data\CIFAR-10\Python\data_batch_' + str(ifile), 'rb') as f:
+                data = cp.load(f)
+                for i in range(10000):
+                    fname = '%sdata\\train\\%05d.png' % (rootDir, i + (ifile - 1) * 10000)
+                    saveImage(fname, data['data'][i, :], data['labels'][i], mapFile, 4, mean=dataMean)
+    dataMean = dataMean / (50 * 1000)
+    saveMean('%sdata\\CIFAR-10_mean.xml' % rootDir, dataMean)
+    with open(rootDir + 'test_map.txt', 'w') as mapFile:
+        with open(r'C:\Data\CIFAR-10\Python\test_batch', 'rb') as f:
+            data = cp.load(f)
+            for i in range(10000):
+                fname = '%sdata\\test\\%05d.png' % (rootDir, i)
+                saveImage(fname, data['data'][i, :], data['labels'][i], mapFile, 0)

Examples/Image/Miscellaneous/CIFAR-10/readme.txt

@@ -19,5 +19,10 @@ The network produces 21% of error after training for about 3 minutes on GPU.
 To run the sample, navigate to this folder and run the following command:
 <path to CNTK executable> configFile=01_Conv.config configName=01_Conv
 
+02_BatchNormConv.ndl is a convolutional network which uses batch normalization technique (http://arxiv.org/abs/1502.03167).
+
+03_ResNet.ndl and 04_ResNet_56.ndl are very deep convolutional networks that use ResNet architecture and have 20 and 56 layers respectively (http://arxiv.org/abs/1512.03385).
+With 03_ResNet.config you should get around 10% of error.
+
 For more details, refer to .ndl and corresponding .config files.