Merge branch 'master' into add-youtube-tutorials

Author: brianjo

beginner_source/README.txt

@@ -23,4 +23,4 @@ Beginner Tutorials
 
 6. transformer_translation.py
 	Language Translation with Transformers
-	https://pytorch.org/tutorials/beginner/transformer_translation.html
+	https://pytorch.org/tutorials/beginner/transformer_tutorial.html

beginner_source/blitz/autograd_tutorial.py

@@ -170,14 +170,12 @@
 # vector-Jacobian product. That is, given any vector :math:`\vec{v}`, compute the product
 # :math:`J^{T}\cdot \vec{v}`
 #
-# If :math:`v` happens to be the gradient of a scalar function
+# If :math:`\vec{v}` happens to be the gradient of a scalar function :math:`l=g\left(\vec{y}\right)`:
 #
 # .. math::
 #
 #
-#    l
-#    =
-#    g\left(\vec{y}\right)
+#   \vec{v}
 #    =
 #    \left(\begin{array}{ccc}\frac{\partial l}{\partial y_{1}} & \cdots & \frac{\partial l}{\partial y_{m}}\end{array}\right)^{T}
 #

beginner_source/blitz/cifar10_tutorial.py

@@ -62,6 +62,8 @@
 ########################################################################
 # The output of torchvision datasets are PILImage images of range [0, 1].
 # We transform them to Tensors of normalized range [-1, 1].
+
+########################################################################
 # .. note::
 #     If running on Windows and you get a BrokenPipeError, try setting
 #     the num_worker of torch.utils.data.DataLoader() to 0.
@@ -70,14 +72,16 @@
     [transforms.ToTensor(),
      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
 
+batch_size = 4
+
 trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                         download=True, transform=transform)
-trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
+trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                           shuffle=True, num_workers=2)
 
 testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                        download=True, transform=transform)
-testloader = torch.utils.data.DataLoader(testset, batch_size=4,
+testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                          shuffle=False, num_workers=2)
 
 classes = ('plane', 'car', 'bird', 'cat',
@@ -106,7 +110,7 @@ def imshow(img):
 # show images
 imshow(torchvision.utils.make_grid(images))
 # print labels
-print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
+print(' '.join('%5s' % classes[labels[j]] for j in range(batch_size)))
 
 
 ########################################################################

beginner_source/examples_nn/polynomial_module.py

@@ -49,8 +49,8 @@ def string(self):
 model = Polynomial3()
 
 # Construct our loss function and an Optimizer. The call to model.parameters()
-# in the SGD constructor will contain the learnable parameters of the nn.Linear
-# module which is members of the model.
+# in the SGD constructor will contain the learnable parameters (defined 
+# with torch.nn.Parameter) which are members of the model.
 criterion = torch.nn.MSELoss(reduction='sum')
 optimizer = torch.optim.SGD(model.parameters(), lr=1e-6)
 for t in range(2000):

beginner_source/nlp/pytorch_tutorial.py

@@ -14,10 +14,6 @@
 # Author: Robert Guthrie
 
 import torch
-import torch.autograd as autograd
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
 
 torch.manual_seed(1)
 

intermediate_source/README.txt

@@ -31,4 +31,4 @@ Intermediate tutorials
 
 8. flask_rest_api_tutorial.py
 	Deploying PyTorch and Building a REST API using Flask
-	https://pytorch.org/tutorials/beginner/flask_rest_api_tutorial.html
+	https://pytorch.org/tutorials/intermediate/flask_rest_api_tutorial.html

intermediate_source/fx_conv_bn_fuser.py

@@ -51,7 +51,7 @@ def __init__(self):
             nn.BatchNorm2d(1),
             nn.Conv2d(1, 1, 1),
         )
-        self.wrapped = WrappedBatchnorm()
+        self.wrapped = WrappedBatchNorm()
 
     def forward(self, x):
         x = self.conv1(x)
@@ -259,4 +259,4 @@ def benchmark(model, iters=20):
 # feedback you have about using it. Please feel free to use the
 # PyTorch Forums (https://discuss.pytorch.org/) and the issue tracker
 # (https://github.com/pytorch/pytorch/issues) to provide any feedback
-# you might have.
+# you might have.

intermediate_source/torchvision_tutorial.rst

@@ -54,7 +54,7 @@ should return:
 
 If your model returns the above methods, they will make it work for both
 training and evaluation, and will use the evaluation scripts from
-``pycocotools``.
+``pycocotools`` which can be installed with ``pip install pycocotools``.
 
 .. note ::
   For Windows, please install ``pycocotools`` from `gautamchitnis <https://github.com/gautamchitnis/cocoapi>`__ with command 
@@ -317,8 +317,8 @@ Putting everything together
 In ``references/detection/``, we have a number of helper functions to
 simplify training and evaluating detection models. Here, we will use
 ``references/detection/engine.py``, ``references/detection/utils.py``
-and ``references/detection/transforms.py``. Just copy them to your
-folder and use them here.
+and ``references/detection/transforms.py``. Just copy everything under 
+``references/detection`` to your folder and use them here.
 
 Let’s write some helper functions for data augmentation /
 transformation: