tiny fix for fcn

chapter_computer-vision/fcn.md

@@ -17,7 +17,7 @@
 
 ```{.python .input  n=1}
 import os
-import tarfile 
+import tarfile
 from mxnet import gluon
 
 data_root = '../data'
@@ -65,7 +65,7 @@ train_images, train_labels = read_images()
 imgs = []
 for i in range(3):
     imgs += [train_images[i], train_labels[i]]
-        
+
 utils.show_images(imgs, nrows=3, ncols=2, figsize=(12,8))
 [im.shape for im in imgs]
 ```
@@ -82,9 +82,9 @@ def rand_crop(data, label, height, width):
 
 imgs = []
 for _ in range(3):
-    imgs += rand_crop(train_images[0], train_labels[0], 
+    imgs += rand_crop(train_images[0], train_labels[0],
                       200, 300)
-    
+
 utils.show_images(imgs, nrows=3, ncols=2, figsize=(12,8))
 ```
 
@@ -96,7 +96,7 @@ classes = ['background','aeroplane','bicycle','bird','boat',
            'dog','horse','motorbike','person','potted plant',
            'sheep','sofa','train','tv/monitor']
 # RGB color for each class
-colormap = [[0,0,0],[128,0,0],[0,128,0], [128,128,0], [0,0,128], 
+colormap = [[0,0,0],[128,0,0],[0,128,0], [128,128,0], [0,0,128],
             [128,0,128],[0,128,128],[128,128,128],[64,0,0],[192,0,0],
             [64,128,0],[192,128,0],[64,0,128],[192,0,128],
             [64,128,128],[192,128,128],[0,64,0],[128,64,0],
@@ -112,9 +112,9 @@ import numpy as np
 from mxnet import nd
 
 cm2lbl = np.zeros(256**3)
-for i,cm in enumerate(colormap):    
+for i,cm in enumerate(colormap):
     cm2lbl[(cm[0]*256+cm[1])*256+cm[2]] = i
-    
+
 def image2label(im):
     data = im.astype('int32').asnumpy()
     idx = (data[:,:,0]*256+data[:,:,1])*256+data[:,:,2]
@@ -142,12 +142,12 @@ def normalize_image(data):
     return (data.astype('float32') / 255 - rgb_mean) / rgb_std
 
 class VOCSegDataset(gluon.data.Dataset):
-    
+
     def _filter(self, images):
         return [im for im in images if (
             im.shape[0] >= self.crop_size[0] and
-            im.shape[1] >= self.crop_size[1])]  
-            
+            im.shape[1] >= self.crop_size[1])]
+
     def __init__(self, train, crop_size):
         self.crop_size = crop_size
         data, label = read_images(train=train)
@@ -155,15 +155,15 @@ class VOCSegDataset(gluon.data.Dataset):
         self.data = [normalize_image(im) for im in data]
         self.label = self._filter(label)
         print('Read '+str(len(self.data))+' examples')
-        
+
     def __getitem__(self, idx):
         data, label = rand_crop(
             self.data[idx], self.label[idx],
             *self.crop_size)
-        data = data.transpose((2,0,1))        
+        data = data.transpose((2,0,1))
         label = image2label(label)
         return data, label
-        
+
     def __len__(self):
         return len(self.data)
 ```
@@ -244,7 +244,7 @@ pretrained_net = models.resnet18_v2(pretrained=True)
 net = nn.HybridSequential()
 for layer in pretrained_net.features[:-2]:
     net.add(layer)
-    
+
 x = nd.random.uniform(shape=(1,3,*input_shape))
 print('Input:', x.shape)
 print('Output:', net(x).shape)
@@ -259,7 +259,7 @@ with net.name_scope():
     net.add(
         nn.Conv2D(num_classes, kernel_size=1),
         nn.Conv2DTranspose(num_classes, kernel_size=64, padding=16,strides=32)
-    )    
+    )
 ```
 
 ## 训练
@@ -316,8 +316,8 @@ plt.show()
 from mxnet import init
 
 conv_trans = net[-1]
-conv_trans.initialize(init=init.Xavier())
-net[-2].initialize(init=init.Zero())
+conv_trans.initialize(init=init.Zero())
+net[-2].initialize(init=init.Xavier())
 
 x = nd.zeros((batch_size, 3, *input_shape))
 net(x)
@@ -334,7 +334,7 @@ import sys
 sys.path.append('..')
 import utils
 
-loss = gluon.loss.SoftmaxCrossEntropyLoss(axis=1)  
+loss = gluon.loss.SoftmaxCrossEntropyLoss(axis=1)
 
 ctx = utils.try_all_gpus()
 net.collect_params().reset_ctx(ctx)
@@ -375,7 +375,7 @@ for i in range(n):
     x = test_images[i]
     pred = label2image(predict(x))
     imgs += [x, pred, test_labels[i]]
-    
+
 utils.show_images(imgs, nrows=n, ncols=3, figsize=(6,10))
 ```