Merge branch 'master' into patch-1

Author: kkaehler

.circleci/config.yml

@@ -6,14 +6,17 @@ pytorch_basics
 
 #data things
 _data/
-beginner_source/hymenoptera_data
-beginner_source/blitz/data
-beginner_source/faces
-beginner_source/hybrid_frontend/data
-intermediate_source/data/
 advanced_source/images/
-*data.zip
-faces.zip
+advanced_source/data/
+beginner_source/.data/
+beginner_source/data/
+beginner_source/blitz/data/
+beginner_source/faces/
+beginner_source/hybrid_frontend/data/
+beginner_source/hymenoptera_data/
+intermediate_source/data/
+*.zip
+MNIST/
 
 #builds
 _build/
@@ -94,7 +97,7 @@ target/
 .python-version
 
 # celery beat schedule file
-celerybeat-schedule 
+celerybeat-schedule
 # dotenv
 .env
 

.gitignore

@@ -40,7 +40,7 @@ if [[ "${JOB_BASE_NAME}" == *worker_* ]]; then
   # Step 1: Remove runnable code from tutorials that are not supposed to be run
   python $DIR/remove_runnable_code.py beginner_source/aws_distributed_training_tutorial.py beginner_source/aws_distributed_training_tutorial.py || true
   # TODO: Fix bugs in these tutorials to make them runnable again
-  python $DIR/remove_runnable_code.py beginner_source/audio_classifier_tutorial.py beginner_source/audio_classifier_tutorial.py || true
+  # python $DIR/remove_runnable_code.py beginner_source/audio_classifier_tutorial.py beginner_source/audio_classifier_tutorial.py || true
 
   # Step 2: Keep certain tutorials based on file count, and remove runnable code in all other tutorials
   # IMPORTANT NOTE: We assume that each tutorial has a UNIQUE filename.

.jenkins/build.sh

@@ -5,14 +5,17 @@
 export LC_ALL=C
 
 # You can set these variables from the command line.
-SPHINXOPTS    =
+SPHINXOPTS    ?=
 SPHINXBUILD   = sphinx-build
 SPHINXPROJ    = PyTorchTutorials
 SOURCEDIR     = .
 BUILDDIR      = _build
 DATADIR       = _data
 GH_PAGES_SOURCES = $(SOURCEDIR) Makefile
 
+ZIPOPTS       ?= -qo
+TAROPTS       ?=
+
 # Put it first so that "make" without argument is like "make help".
 help:
 	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
@@ -43,15 +46,15 @@ download:
 
 	# transfer learning tutorial data
 	wget -N https://download.pytorch.org/tutorial/hymenoptera_data.zip -P $(DATADIR)
-	unzip -o $(DATADIR)/hymenoptera_data.zip -d beginner_source/data/
+	unzip $(ZIPOPTS) $(DATADIR)/hymenoptera_data.zip -d beginner_source/data/
 
 	# nlp tutorial data
 	wget -N https://download.pytorch.org/tutorial/data.zip -P $(DATADIR)
-	unzip -o $(DATADIR)/data.zip -d intermediate_source/  # This will unzip all files in data.zip to intermediate_source/data/ folder
+	unzip $(ZIPOPTS) $(DATADIR)/data.zip -d intermediate_source/  # This will unzip all files in data.zip to intermediate_source/data/ folder
 
 	# data loader tutorial
 	wget -N https://download.pytorch.org/tutorial/faces.zip -P $(DATADIR)
-	unzip -o $(DATADIR)/faces.zip -d beginner_source/data/
+	unzip $(ZIPOPTS) $(DATADIR)/faces.zip -d beginner_source/data/
 
 	wget -N https://download.pytorch.org/models/tutorials/4000_checkpoint.tar -P $(DATADIR)
 	cp $(DATADIR)/4000_checkpoint.tar beginner_source/data/
@@ -63,19 +66,19 @@ download:
 
 	# Download dataset for beginner_source/dcgan_faces_tutorial.py
 	wget -N https://s3.amazonaws.com/pytorch-tutorial-assets/img_align_celeba.zip -P $(DATADIR)
-	unzip -q -o $(DATADIR)/img_align_celeba.zip -d beginner_source/data/celeba
+	unzip $(ZIPOPTS) $(DATADIR)/img_align_celeba.zip -d beginner_source/data/celeba
 
 	# Download dataset for beginner_source/hybrid_frontend/introduction_to_hybrid_frontend_tutorial.py
 	wget -N https://s3.amazonaws.com/pytorch-tutorial-assets/iris.data -P $(DATADIR)
 	cp $(DATADIR)/iris.data beginner_source/data/
 
 	# Download dataset for beginner_source/chatbot_tutorial.py
 	wget -N https://s3.amazonaws.com/pytorch-tutorial-assets/cornell_movie_dialogs_corpus.zip -P $(DATADIR)
-	unzip -q -o $(DATADIR)/cornell_movie_dialogs_corpus.zip -d beginner_source/data/
+	unzip $(ZIPOPTS) $(DATADIR)/cornell_movie_dialogs_corpus.zip -d beginner_source/data/
 
 	# Download dataset for beginner_source/audio_classifier_tutorial.py
 	wget -N https://s3.amazonaws.com/pytorch-tutorial-assets/UrbanSound8K.tar.gz -P $(DATADIR)
-	tar -xzf $(DATADIR)/UrbanSound8K.tar.gz -C ./beginner_source/data/
+	tar $(TAROPTS) -xzf $(DATADIR)/UrbanSound8K.tar.gz -C ./beginner_source/data/
 
 	# Download model for beginner_source/fgsm_tutorial.py
 	wget -N https://s3.amazonaws.com/pytorch-tutorial-assets/lenet_mnist_model.pth -P $(DATADIR)
@@ -87,16 +90,15 @@ download:
 
 	# Download data for advanced_source/dynamic_quantization_tutorial.py
 	wget -N https://s3.amazonaws.com/pytorch-tutorial-assets/wikitext-2.zip -P $(DATADIR)
-	unzip -q -o $(DATADIR)/wikitext-2.zip -d advanced_source/data/
+	unzip $(ZIPOPTS) $(DATADIR)/wikitext-2.zip -d advanced_source/data/
 
 	# Download model for advanced_source/static_quantization_tutorial.py
-	wget -N https://s3.amazonaws.com/pytorch-tutorial-assets/mobilenet_quantization.pth -P $(DATADIR)
-	cp $(DATADIR)/mobilenet_quantization.pth advanced_source/data/mobilenet_quantization.pth
+	wget -N https://download.pytorch.org/models/mobilenet_v2-b0353104.pth -P $(DATADIR)
+	cp $(DATADIR)/mobilenet_v2-b0353104.pth advanced_source/data/mobilenet_pretrained_float.pth
 
 	# Download dataset for advanced_source/static_quantization_tutorial.py
 	wget -N https://s3.amazonaws.com/pytorch-tutorial-assets/imagenet_1k.zip -P $(DATADIR)
-	unzip -q -o $(DATADIR)/imagenet_1k.zip -d advanced_source/data/
-
+	unzip $(ZIPOPTS) $(DATADIR)/imagenet_1k.zip -d advanced_source/data/
 
 docs:
 	make download

Makefile

@@ -9,7 +9,7 @@ All the tutorials are now presented as sphinx style documentation at:
 
 # Contributing
 
-We use sphinx-gallery's [notebook styled examples](https://sphinx-gallery.github.io/tutorials/plot_notebook.html#sphx-glr-tutorials-plot-notebook-py) to create the tutorials. Syntax is very simple. In essence, you write a slightly well formatted python file and it shows up as documentation page.
+We use sphinx-gallery's [notebook styled examples](https://sphinx-gallery.github.io/stable/tutorials/index.html) to create the tutorials. Syntax is very simple. In essence, you write a slightly well formatted python file and it shows up as documentation page.
 
 Here's how to create a new tutorial:
 1. Create a notebook styled python file. If you want it executed while inserted into documentation, save the file with suffix `tutorial` so that file name is `your_tutorial.py`.
@@ -27,3 +27,5 @@ In case you prefer to write your tutorial in jupyter, you can use [this script](
 
 - Then you can build using `make docs`. This will download the data, execute the tutorials and build the documentation to `docs/` directory. This will take about 60-120 min for systems with GPUs. If you do not have a GPU installed on your system, then see next step.
 - You can skip the computationally intensive graph generation by running `make html-noplot` to build basic html documentation to `_build/html`. This way, you can quickly preview your tutorial.
+
+> If you get **ModuleNotFoundError: No module named 'pytorch_sphinx_theme' make: *** [html-noplot] Error 2**, from /tutorials/src/pytorch_sphinx_theme run `python setup.py install`.

README.md

@@ -67,6 +67,10 @@
       eventAction: 'click',
       eventLabel: $(this).attr("data-response")
     });
+    gtag('event', $(this).attr("data-response"), {
+      'event_category': 'Was this Helpful?',
+      'event_label': $("h1").first().text()
+    });
    });
 
    if (location.pathname == "/") {

_static/img/bert.png

@@ -1,8 +1,6 @@
 Loading a TorchScript Model in C++
 =====================================
 
-**This tutorial was updated to work with PyTorch 1.2**
-
 As its name suggests, the primary interface to PyTorch is the Python
 programming language. While Python is a suitable and preferred language for
 many scenarios requiring dynamism and ease of iteration, there are equally many
@@ -205,7 +203,7 @@ minimal ``CMakeLists.txt`` to build it could look as simple as:
 
   add_executable(example-app example-app.cpp)
   target_link_libraries(example-app "${TORCH_LIBRARIES}")
-  set_property(TARGET example-app PROPERTY CXX_STANDARD 11)
+  set_property(TARGET example-app PROPERTY CXX_STANDARD 14)
 
 The last thing we need to build the example application is the LibTorch
 distribution. You can always grab the latest stable release from the `download
@@ -228,6 +226,8 @@ structure:
 .. tip::
   On Windows, debug and release builds are not ABI-compatible. If you plan to
   build your project in debug mode, please try the debug version of LibTorch.
+  Also, make sure you specify the correct configuration in the ``cmake --build .``
+  line below.
 
 The last step is building the application. For this, assume our example
 directory is laid out like this:
@@ -246,7 +246,7 @@ We can now run the following commands to build the application from within the
   mkdir build
   cd build
   cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
-  make
+  cmake --build . --config Release
 
 where ``/path/to/libtorch`` should be the full path to the unzipped LibTorch
 distribution. If all goes well, it will look something like this:

_static/img/bert_mrpc.png

@@ -667,8 +667,8 @@ We'll start with the C++ file, which we'll call ``lltm_cuda.cpp``, for example:
 
   // C++ interface
 
-  #define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor")
-  #define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous")
+  #define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
+  #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
   #define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
 
   std::vector<torch::Tensor> lltm_forward(