Merge branch 'master' into 1.13-RC-TEST

Author: Svetlana Karslioglu

beginner_source/blitz/cifar10_tutorial.py

@@ -105,7 +105,7 @@ def imshow(img):
 
 # get some random training images
 dataiter = iter(trainloader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # show images
 imshow(torchvision.utils.make_grid(images))
@@ -210,7 +210,7 @@ def forward(self, x):
 # Okay, first step. Let us display an image from the test set to get familiar.
 
 dataiter = iter(testloader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # print images
 imshow(torchvision.utils.make_grid(images))

beginner_source/introyt/introyt1_tutorial.py

@@ -369,7 +369,7 @@ def imshow(img):
 
 # get some random training images
 dataiter = iter(trainloader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # show images
 imshow(torchvision.utils.make_grid(images))
@@ -446,7 +446,7 @@ def imshow(img):
 
 # get some random training images
 dataiter = iter(trainloader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # show images
 imshow(torchvision.utils.make_grid(images))

beginner_source/introyt/tensorboardyt_tutorial.py

@@ -115,7 +115,7 @@ def matplotlib_imshow(img, one_channel=False):
 
 # Extract a batch of 4 images
 dataiter = iter(training_loader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # Create a grid from the images and show them
 img_grid = torchvision.utils.make_grid(images)
@@ -242,7 +242,7 @@ def forward(self, x):
 
 # Again, grab a single mini-batch of images
 dataiter = iter(training_loader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # add_graph() will trace the sample input through your model,
 # and render it as a graph.

beginner_source/introyt/trainingyt.py

@@ -112,7 +112,7 @@ def matplotlib_imshow(img, one_channel=False):
         plt.imshow(np.transpose(npimg, (1, 2, 0)))
 
 dataiter = iter(training_loader)
-images, labels = dataiter.next()
+images, labels = next(dataiter)
 
 # Create a grid from the images and show them
 img_grid = torchvision.utils.make_grid(images)

intermediate_source/tensorboard_tutorial.rst

@@ -144,7 +144,7 @@ using `make_grid <https://pytorch.org/vision/stable/utils.html#torchvision.utils
 
     # get some random training images
     dataiter = iter(trainloader)
-    images, labels = dataiter.next()
+    images, labels = next(dataiter)
 
     # create grid of images
     img_grid = torchvision.utils.make_grid(images)