Merge pull request #140 from starsdeep/fix_for_resize_to_224_when_training_AlexNet

fix for resize to 224 when training AlextNet in alexnet-gluon.md

utils.py

@@ -16,10 +16,11 @@ 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):
+    def __init__(self, dataset, batch_size, shuffle, transform):
         self.dataset = dataset
         self.batch_size = batch_size
         self.shuffle = shuffle
+        self.transform = transform
 
     def __iter__(self):
         data = self.dataset[:]
@@ -33,8 +34,12 @@ class DataLoader(object):
             y = nd.array(y.asnumpy()[idx])
 
         for i in range(n//self.batch_size):
-            yield (X[i*self.batch_size:(i+1)*self.batch_size],
-                   y[i*self.batch_size:(i+1)*self.batch_size])
+            if self.transform is not None:
+                yield self.transform(X[i*self.batch_size:(i+1)*self.batch_size], 
+                                     y[i*self.batch_size:(i+1)*self.batch_size])
+            else:
+                yield (X[i*self.batch_size:(i+1)*self.batch_size],
+                       y[i*self.batch_size:(i+1)*self.batch_size])
 
     def __len__(self):
         return len(self.dataset)//self.batch_size
@@ -51,10 +56,11 @@ def load_data_fashion_mnist(batch_size, resize=None, root="~/.mxnet/datasets/fas
             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=transform_mnist)
-    mnist_test = gluon.data.vision.FashionMNIST(root=root, train=False, transform=transform_mnist)
-    train_data = DataLoader(mnist_train, batch_size, shuffle=True)
-    test_data = DataLoader(mnist_test, batch_size, shuffle=False)
+    
+    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)
     return (train_data, test_data)
 
 def try_gpu():