Merge pull request #212 from astonzhang/nin

Add transform=None in utils.DataLoader

utils.py

@@ -16,7 +16,7 @@ class DataLoader(object):
     time. But the limits are 1) all examples in dataset have the same shape, 2)
     data transfomer needs to process multiple examples at each time
     """
-    def __init__(self, dataset, batch_size, shuffle, transform):
+    def __init__(self, dataset, batch_size, shuffle, transform=None):
         self.dataset = dataset
         self.batch_size = batch_size
         self.shuffle = shuffle
@@ -47,7 +47,7 @@ def __len__(self):
 def load_data_fashion_mnist(batch_size, resize=None, root="~/.mxnet/datasets/fashion-mnist"):
     """download the fashion mnist dataest and then load into memory"""
     def transform_mnist(data, label):
-        # transform a batch of examples
+        # Transform a batch of examples.
         if resize:
             n = data.shape[0]
             new_data = nd.zeros((n, resize, resize, data.shape[3]))
@@ -56,11 +56,12 @@ def transform_mnist(data, label):
             data = new_data
         # change data from batch x height x width x channel to batch x channel x height x width
         return nd.transpose(data.astype('float32'), (0,3,1,2))/255, label.astype('float32')
-    
+
     mnist_train = gluon.data.vision.FashionMNIST(root=root, train=True, transform=None)
     mnist_test = gluon.data.vision.FashionMNIST(root=root, train=False, transform=None)
-    train_data = DataLoader(mnist_train, batch_size, shuffle=True, transform = transform_mnist)
-    test_data = DataLoader(mnist_test, batch_size, shuffle=False, transform = transform_mnist)
+	# Transform later to avoid memory explosion. 
+	train_data = DataLoader(mnist_train, batch_size, shuffle=True, transform=transform_mnist)
+    test_data = DataLoader(mnist_test, batch_size, shuffle=False, transform=transform_mnist)
     return (train_data, test_data)
 
 def try_gpu():