tutorials/advanced_source/cpp_frontend.rst

Using the PyTorch C++ Frontend
==============================

The PyTorch C++ frontend is a pure C++ interface to the PyTorch machine learning
framework. While the primary interface to PyTorch naturally is Python, this
Python API sits atop a substantial C++ codebase providing foundational data
structures and functionality such as tensors and automatic differentiation. The
C++ frontend exposes a pure C++11 API that extends this underlying C++ codebase
with tools required for machine learning training and inference. This includes a
built-in collection of common components for neural network modeling; an API to
extend this collection with custom modules; a library of popular optimization
algorithms such as stochastic gradient descent; a parallel data loader with an
API to define and load datasets; serialization routines and more.

This tutorial will walk you through an end-to-end example of training a model
with the C++ frontend. Concretely, we will be training a `DCGAN
<https://arxiv.org/abs/1511.06434>`_ -- a kind of generative model -- to
generate images of MNIST digits. While conceptually a simple example, it should
be enough to give you a whirlwind overview of the PyTorch C++ frontend and wet
your appetite for training more complex models. We will begin with some
motivating words for why you would want to use the C++ frontend to begin with,
and then dive straight into defining and training our model.

.. tip::

  Watch `this lightning talk from CppCon 2018
  <https://www.youtube.com/watch?v=auRPXMMHJzc>`_ for a quick (and humorous)
  presentation on the C++ frontend.

.. tip::

  `This note <https://pytorch.org/cppdocs/frontend.html>`_ provides a sweeping
  overview of the C++ frontend's components and design philosophy.

.. tip::

  Documentation for the PyTorch C++ ecosystem is available at
  https://pytorch.org/cppdocs. There you can find high level descriptions as
  well as API-level documentation.

Motivation
----------

Before we embark on our exciting journey of GANs and MNIST digits, let's take a
step back and discuss why you would want to use the C++ frontend instead of the
Python one to begin with. We (the PyTorch team) created the C++ frontend to
enable research in environments in which Python cannot be used, or is simply not
the right tool for the job. Examples for such environments include:

- **Low Latency Systems**: You may want to do reinforcement learning research in
  a pure C++ game engine with high frames-per-second and low latency
  requirements. Using a pure C++ library is a much better fit to such an
  environment than a Python library. Python may not be tractable at all because
  of the slowness of the Python interpreter.
- **Highly Multithreaded Environments**: Due to the Global Interpreter Lock
  (GIL), Python cannot run more than one system thread at a time.
  Multiprocessing is an alternative, but not as scalable and has significant
  shortcomings. C++ has no such constraints and threads are easy to use and
  create. Models requiring heavy parallelization, like those used in `Deep
  Neuroevolution <https://eng.uber.com/deep-neuroevolution/>`_, can benefit from
  this.
- **Existing C++ Codebases**: You may be the owner of an existing C++
  application doing anything from serving web pages in a backend server to
  rendering 3D graphics in photo editing software, and wish to integrate
  machine learning methods into your system. The C++ frontend allows you to
  remain in C++ and spare yourself the hassle of binding back and forth between
  Python and C++, while retaining much of the flexibility and intuitiveness of
  the traditional PyTorch (Python) experience.

The C++ frontend is not intended to compete with the Python frontend. It is
meant to complement it. We know researchers and engineers alike love PyTorch for
its simplicity, flexibility and intuitive API. Our goal is to make sure you can
take advantage of these core design principles in every possible environment,
including the ones described above. If one of these scenarios describes your use
case well, or if you are simply interested or curious, follow along as we
explore the C++ frontend in detail in the following paragraphs.

.. tip::

	The C++ frontend tries to provide an API as close as possible to that of the
	Python frontend. If you are experienced with the Python frontend and ever ask
	yourself "how do I do X with the C++ frontend?", write your code the way you
	would in Python, and more often than not the same functions and methods will
	be available in C++ as in Python (just remember to replace dots with double
	colons).

Writing a Basic Application
---------------------------

Let's begin by writing a minimal C++ application to verify that we're on the
same page regarding our setup and build environment. First, you will need to
grab a copy of the *LibTorch* distribution -- our ready-built zip archive that
packages all relevant headers, libraries and CMake build files required to use
the C++ frontend. The LibTorch distribution is available for download on the
`PyTorch website <https://pytorch.org/get-started/locally/>`_ for Linux, MacOS
and Windows. The rest of this tutorial will assume a basic Ubuntu Linux
environment, however you are free to follow along on MacOS or Windows too.

.. tip::

  The note on `Installing C++ Distributions of PyTorch
  <https://pytorch.org/cppdocs/installing.html>`_ describes the following steps
  in more detail.

.. 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 first step is to download the LibTorch distribution locally, via the link
retrieved from the PyTorch website. For a vanilla Ubuntu Linux environment, this
means running:

.. code-block:: shell

  # If you need e.g. CUDA 9.0 support, please replace "cpu" with "cu90" in the URL below.
  wget https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip
  unzip libtorch-shared-with-deps-latest.zip

Next, let's write a tiny C++ file called ``dcgan.cpp`` that includes
``torch/torch.h`` and for now simply prints out a three by three identity
matrix:

.. code-block:: cpp

  #include <torch/torch.h>
  #include <iostream>

  int main() {
    torch::Tensor tensor = torch::eye(3);
    std::cout << tensor << std::endl;
  }

To build this tiny application as well as our full-fledged training script later
on we'll use this ``CMakeLists.txt`` file:

.. code-block:: cmake

  cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
  project(dcgan)

  find_package(Torch REQUIRED)

  add_executable(dcgan dcgan.cpp)
  target_link_libraries(dcgan "${TORCH_LIBRARIES}")
  set_property(TARGET dcgan PROPERTY CXX_STANDARD 14)

.. note::

  While CMake is the recommended build system for LibTorch, it is not a hard
  requirement. You can also use Visual Studio project files, QMake, plain
  Makefiles or any other build environment you feel comfortable with. However,
  we do not provide out-of-the-box support for this.

Make note of line 4 in the above CMake file: ``find_package(Torch REQUIRED)``.
This instructs CMake to find the build configuration for the LibTorch library.
In order for CMake to know *where* to find these files, we must set the
``CMAKE_PREFIX_PATH`` when invoking ``cmake``. Before we do this, let's agree on
the following directory structure for our ``dcgan`` application:

.. code-block:: shell

  dcgan/
    CMakeLists.txt
    dcgan.cpp

Further, I will refer to the path to the unzipped LibTorch distribution as
``/path/to/libtorch``. Note that this **must be an absolute path**. In
particular, setting ``CMAKE_PREFIX_PATH`` to something like ``../../libtorch``
will break in unexpected ways. Instead, write ``$PWD/../../libtorch`` to get the
corresponding absolute path. Now, we are ready to build our application:

.. code-block:: shell

  root@fa350df05ecf:/home# mkdir build
  root@fa350df05ecf:/home# cd build
  root@fa350df05ecf:/home/build# cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
  -- The C compiler identification is GNU 5.4.0
  -- The CXX compiler identification is GNU 5.4.0
  -- Check for working C compiler: /usr/bin/cc
  -- Check for working C compiler: /usr/bin/cc -- works
  -- Detecting C compiler ABI info
  -- Detecting C compiler ABI info - done
  -- Detecting C compile features
  -- Detecting C compile features - done
  -- Check for working CXX compiler: /usr/bin/c++
  -- Check for working CXX compiler: /usr/bin/c++ -- works
  -- Detecting CXX compiler ABI info
  -- Detecting CXX compiler ABI info - done
  -- Detecting CXX compile features
  -- Detecting CXX compile features - done
  -- Looking for pthread.h
  -- Looking for pthread.h - found
  -- Looking for pthread_create
  -- Looking for pthread_create - not found
  -- Looking for pthread_create in pthreads
  -- Looking for pthread_create in pthreads - not found
  -- Looking for pthread_create in pthread
  -- Looking for pthread_create in pthread - found
  -- Found Threads: TRUE
  -- Found torch: /path/to/libtorch/lib/libtorch.so
  -- Configuring done
  -- Generating done
  -- Build files have been written to: /home/build
  root@fa350df05ecf:/home/build# cmake --build . --config Release
  Scanning dependencies of target dcgan
  [ 50%] Building CXX object CMakeFiles/dcgan.dir/dcgan.cpp.o
  [100%] Linking CXX executable dcgan
  [100%] Built target dcgan

Above, we first created a ``build`` folder inside of our ``dcgan`` directory,
entered this folder, ran the ``cmake`` command to generate the necessary build
(Make) files and finally compiled the project successfully by running ``cmake
--build . --config Release``. We are now all set to execute our minimal binary
and complete this section on basic project configuration:

.. code-block:: shell

  root@fa350df05ecf:/home/build# ./dcgan
  1  0  0
  0  1  0
  0  0  1
  [ Variable[CPUFloatType]{3,3} ]

Looks like an identity matrix to me!

Defining the Neural Network Models
----------------------------------

Now that we have our basic environment configured, we can dive into the much
more interesting parts of this tutorial. First, we will discuss how to define
and interact with modules in the C++ frontend. We'll begin with basic,
small-scale example modules and then implement a full-fledged GAN using the
extensive library of built-in modules provided by the C++ frontend.

Module API Basics
^^^^^^^^^^^^^^^^^

In line with the Python interface, neural networks based on the C++ frontend are
composed of reusable building blocks called *modules*. There is a base module
class from which all other modules are derived. In Python, this class is
``torch.nn.Module`` and in C++ it is ``torch::nn::Module``. Besides a
``forward()`` method that implements the algorithm the module encapsulates, a
module usually contains any of three kinds of sub-objects: parameters, buffers
and submodules.

Parameters and buffers store state in form of tensors. Parameters record
gradients, while buffers do not. Parameters are usually the trainable weights of
your neural network. Examples of buffers include means and variances for batch
normalization. In order to re-use particular blocks of logic and state, the
PyTorch API allows modules to be nested. A nested module is termed a
*submodule*.

Parameters, buffers and submodules must be explicitly registered. Once
registered, methods like ``parameters()`` or ``buffers()`` can be used to
retrieve a container of all parameters in the entire (nested) module hierarchy.
Similarly, methods like ``to(...)``, where e.g. ``to(torch::kCUDA)`` moves all
parameters and buffers from CPU to CUDA memory, work on the entire module
hierarchy.

Defining a Module and Registering Parameters
********************************************

To put these words into code, let's consider this simple module written in the
Python interface:

.. code-block:: python

  import torch

  class Net(torch.nn.Module):
    def __init__(self, N, M):
      super(Net, self).__init__()
      self.W = torch.nn.Parameter(torch.randn(N, M))
      self.b = torch.nn.Parameter(torch.randn(M))

    def forward(self, input):
      return torch.addmm(self.b, input, self.W)


In C++, it would look like this:

.. code-block:: cpp

  #include <torch/torch.h>

  struct Net : torch::nn::Module {
    Net(int64_t N, int64_t M) {
      W = register_parameter("W", torch::randn({N, M}));
      b = register_parameter("b", torch::randn(M));
    }
    torch::Tensor forward(torch::Tensor input) {
      return torch::addmm(b, input, W);
    }
    torch::Tensor W, b;
  };

Just like in Python, we define a class called ``Net`` (for simplicity here a
``struct`` instead of a ``class``) and derive it from the module base class.
Inside the constructor, we create tensors using ``torch::randn`` just like we
use ``torch.randn`` in Python. One interesting difference is how we register the
parameters. In Python, we wrap the tensors with the ``torch.nn.Parameter``
class, while in C++ we have to pass the tensor through the
``register_parameter`` method instead. The reason for this is that the Python
API can detect that an attribute is of type ``torch.nn.Parameter`` and
automatically registers such tensors. In C++, reflection is very limited, so a
more traditional (and less magical) approach is provided.

Registering Submodules and Traversing the Module Hierarchy
**********************************************************

In the same way we can register parameters, we can also register submodules. In
Python, submodules are automatically detected and registered when they are
assigned as an attribute of a module:

.. code-block:: python

  class Net(torch.nn.Module):
    def __init__(self, N, M):
        super(Net, self).__init__()
        # Registered as a submodule behind the scenes
        self.linear = torch.nn.Linear(N, M)
        self.another_bias = torch.nn.Parameter(torch.rand(M))

    def forward(self, input):
      return self.linear(input) + self.another_bias

This allows, for example, to use the ``parameters()`` method to recursively
access all parameters in our module hierarchy:

.. code-block:: python

  >>> net = Net(4, 5)
  >>> print(list(net.parameters()))
  [Parameter containing:
  tensor([0.0808, 0.8613, 0.2017, 0.5206, 0.5353], requires_grad=True), Parameter containing:
  tensor([[-0.3740, -0.0976, -0.4786, -0.4928],
          [-0.1434,  0.4713,  0.1735, -0.3293],
          [-0.3467, -0.3858,  0.1980,  0.1986],
          [-0.1975,  0.4278, -0.1831, -0.2709],
          [ 0.3730,  0.4307,  0.3236, -0.0629]], requires_grad=True), Parameter containing:
  tensor([ 0.2038,  0.4638, -0.2023,  0.1230, -0.0516], requires_grad=True)]

To register submodules in C++, use the aptly named ``register_module()`` method
to register a module like ``torch::nn::Linear``:

.. code-block:: cpp

  struct Net : torch::nn::Module {
    Net(int64_t N, int64_t M)
        : linear(register_module("linear", torch::nn::Linear(N, M))) {
      another_bias = register_parameter("b", torch::randn(M));
    }
    torch::Tensor forward(torch::Tensor input) {
      return linear(input) + another_bias;
    }
    torch::nn::Linear linear;
    torch::Tensor another_bias;
  };

.. tip::

  You can find the full list of available built-in modules like
  ``torch::nn::Linear``, ``torch::nn::Dropout`` or ``torch::nn::Conv2d`` in the
  documentation of the ``torch::nn`` namespace `here
  <https://pytorch.org/cppdocs/api/namespace_torch__nn.html>`_.

One subtlety about the above code is why the submodule was created in the
constructor's initializer list, while the parameter was created inside the
constructor body. There is a good reason for this, which we'll touch upon this
in the section on the C++ frontend's *ownership model* further below. The end
result, however, is that we can recursively access our module tree's parameters
just like in Python. Calling ``parameters()`` returns a
``std::vector<torch::Tensor>``, which we can iterate over:

.. code-block:: cpp

  int main() {
    Net net(4, 5);
    for (const auto& p : net.parameters()) {
      std::cout << p << std::endl;
    }
  }

which prints:

.. code-block:: shell

  root@fa350df05ecf:/home/build# ./dcgan
  0.0345
  1.4456
  -0.6313
  -0.3585
  -0.4008
  [ Variable[CPUFloatType]{5} ]
  -0.1647  0.2891  0.0527 -0.0354
  0.3084  0.2025  0.0343  0.1824
  -0.4630 -0.2862  0.2500 -0.0420
  0.3679 -0.1482 -0.0460  0.1967
  0.2132 -0.1992  0.4257  0.0739
  [ Variable[CPUFloatType]{5,4} ]
  0.01 *
  3.6861
  -10.1166
  -45.0333
  7.9983
  -20.0705
  [ Variable[CPUFloatType]{5} ]

with three parameters just like in Python. To also see the names of these
parameters, the C++ API provides a ``named_parameters()`` method which returns
an ``OrderedDict`` just like in Python:

.. code-block:: cpp

  Net net(4, 5);
  for (const auto& pair : net.named_parameters()) {
    std::cout << pair.key() << ": " << pair.value() << std::endl;
  }

which we can execute again to see the output:

.. code-block:: shell

  root@fa350df05ecf:/home/build# make && ./dcgan                                                                                                                                            11:13:48
  Scanning dependencies of target dcgan
  [ 50%] Building CXX object CMakeFiles/dcgan.dir/dcgan.cpp.o
  [100%] Linking CXX executable dcgan
  [100%] Built target dcgan
  b: -0.1863
  -0.8611
  -0.1228
  1.3269
  0.9858
  [ Variable[CPUFloatType]{5} ]
  linear.weight:  0.0339  0.2484  0.2035 -0.2103
  -0.0715 -0.2975 -0.4350 -0.1878
  -0.3616  0.1050 -0.4982  0.0335
  -0.1605  0.4963  0.4099 -0.2883
  0.1818 -0.3447 -0.1501 -0.0215
  [ Variable[CPUFloatType]{5,4} ]
  linear.bias: -0.0250
  0.0408
  0.3756
  -0.2149
  -0.3636
  [ Variable[CPUFloatType]{5} ]

.. note::

  `The documentation
  <https://pytorch.org/cppdocs/api/classtorch_1_1nn_1_1_module.html#exhale-class-classtorch-1-1nn-1-1-module>`_
  for ``torch::nn::Module`` contains the full list of methods that operate on
  the module hierarchy.

Running the Network in Forward Mode
***********************************

To execute the network in C++, we simply call the ``forward()`` method we
defined ourselves:

.. code-block:: cpp

  int main() {
    Net net(4, 5);
    std::cout << net.forward(torch::ones({2, 4})) << std::endl;
  }

which prints something like:

.. code-block:: shell

  root@fa350df05ecf:/home/build# ./dcgan
  0.8559  1.1572  2.1069 -0.1247  0.8060
  0.8559  1.1572  2.1069 -0.1247  0.8060
  [ Variable[CPUFloatType]{2,5} ]

Module Ownership
****************

At this point, we know how to define a module in C++, register parameters,
register submodules, traverse the module hierarchy via methods like
``parameters()`` and finally run the module's ``forward()`` method. While there
are many more methods, classes and topics to devour in the C++ API, I will refer
you to `docs <https://pytorch.org/cppdocs/api/namespace_torch__nn.html>`_ for
the full menu. We'll also touch upon some more concepts as we implement the
DCGAN model and end-to-end training pipeline in just a second. Before we do so,
let me briefly touch upon the *ownership model* the C++ frontend provides for
subclasses of ``torch::nn::Module``.

For this discussion, the ownership model refers to the way modules are stored
and passed around -- which determines who or what *owns* a particular module
instance. In Python, objects are always allocated dynamically (on the heap) and
have reference semantics. This is very easy to work with and straightforward to
understand. In fact, in Python, you can largely forget about where objects live
and how they get referenced, and focus on getting things done.

C++, being a lower level language, provides more options in this realm. This
increases complexity and heavily influences the design and ergonomics of the C++
frontend. In particular, for modules in the C++ frontend, we have the option of
using *either* value semantics *or* reference semantics. The first case is the
simplest and was shown in the examples thus far: module objects are allocated on
the stack and when passed to a function, can be either copied, moved (with
``std::move``) or taken by reference or by pointer:

.. code-block:: cpp

  struct Net : torch::nn::Module { };

  void a(Net net) { }
  void b(Net& net) { }
  void c(Net* net) { }

  int main() {
    Net net;
    a(net);
    a(std::move(net));
    b(net);
    c(&net);
  }

For the second case -- reference semantics -- we can use ``std::shared_ptr``.
The advantage of reference semantics is that, like in Python, it reduces the
cognitive overhead of thinking about how modules must be passed to functions and
how arguments must be declared (assuming you use ``shared_ptr`` everywhere).

.. code-block:: cpp

  struct Net : torch::nn::Module {};

  void a(std::shared_ptr<Net> net) { }

  int main() {
    auto net = std::make_shared<Net>();
    a(net);
  }

In our experience, researchers coming from dynamic languages greatly prefer
reference semantics over value semantics, even though the latter is more
"native" to C++. It is also important to note that ``torch::nn::Module``'s
design, in order to stay close to the ergonomics of the Python API, relies on
shared ownership. For example, take our earlier (here shortened) definition of
``Net``:

.. code-block:: cpp

  struct Net : torch::nn::Module {
    Net(int64_t N, int64_t M)
      : linear(register_module("linear", torch::nn::Linear(N, M)))
    { }
    torch::nn::Linear linear;
  };

In order to use the ``linear`` submodule, we want to store it directly in our
class. However, we also want the module base class to know about and have access
to this submodule. For this, it must store a reference to this submodule. At
this point, we have already arrived at the need for shared ownership. Both the
``torch::nn::Module`` class and concrete ``Net`` class require a reference to
the submodule. For this reason, the base class stores modules as
``shared_ptr``\s, and therefore the concrete class must too.

But wait! I don't see any mention of ``shared_ptr`` in the above code! Why is
that? Well, because ``std::shared_ptr<MyModule>`` is a hell of a lot to type. To
keep our researchers productive, we came up with an elaborate scheme to hide the
mention of ``shared_ptr`` -- a benefit usually reserved for value semantics --
while retaining reference semantics. To understand how this works, we can take a
look at a simplified definition of the ``torch::nn::Linear`` module in the core
library (the full definition is `here
<https://github.com/pytorch/pytorch/blob/master/torch/csrc/api/include/torch/nn/modules/linear.h>`_):

.. code-block:: cpp

  struct LinearImpl : torch::nn::Module {
    LinearImpl(int64_t in, int64_t out);

    Tensor forward(const Tensor& input);

    Tensor weight, bias;
  };

  TORCH_MODULE(Linear);

In brief: the module is not called ``Linear``, but ``LinearImpl``. A macro,
``TORCH_MODULE`` then defines the actual ``Linear`` class. This "generated"
class is effectively a wrapper over a ``std::shared_ptr<LinearImpl>``. It is a
wrapper instead of a simple typedef so that, among other things, constructors
still work as expected, i.e. you can still write ``torch::nn::Linear(3, 4)``
instead of ``std::make_shared<LinearImpl>(3, 4)``. We call the class created by
the macro the module *holder*. Like with (shared) pointers, you access the
underlying object using the arrow operator (like ``model->forward(...)``). The
end result is an ownership model that resembles that of the Python API quite
closely. Reference semantics become the default, but without the extra typing of
``std::shared_ptr`` or ``std::make_shared``. For our ``Net``, using the module
holder API looks like this:

.. code-block:: cpp

  struct NetImpl : torch::nn::Module {};
  TORCH_MODULE(Net);

  void a(Net net) { }

  int main() {
    Net net;
    a(net);
  }

There is one subtle issue that deserves mention here. A default constructed
``std::shared_ptr`` is "empty", i.e. contains a null pointer. What is a default
constructed ``Linear`` or ``Net``? Well, it's a tricky choice. We could say it
should be an empty (null) ``std::shared_ptr<LinearImpl>``. However, recall that
``Linear(3, 4)`` is the same as ``std::make_shared<LinearImpl>(3, 4)``. This
means that if we had decided that ``Linear linear;`` should be a null pointer,
then there would be no way to construct a module that does not take any
constructor arguments, or defaults all of them. For this reason, in the current
API, a default constructed module holder (like ``Linear()``) invokes the
default constructor of the underlying module (``LinearImpl()``). If the
underlying module does not have a default constructor, you get a compiler error.
To instead construct the empty holder, you can pass ``nullptr`` to the
constructor of the holder.

In practice, this means you can use submodules either like shown earlier, where
the module is registered and constructed in the *initializer list*:

.. code-block:: cpp

  struct Net : torch::nn::Module {
    Net(int64_t N, int64_t M)
      : linear(register_module("linear", torch::nn::Linear(N, M)))
    { }
    torch::nn::Linear linear;
  };

or you can first construct the holder with a null pointer and then assign to it
in the constructor (more familiar for Pythonistas):

.. code-block:: cpp

  struct Net : torch::nn::Module {
    Net(int64_t N, int64_t M) {
      linear = register_module("linear", torch::nn::Linear(N, M));
    }
    torch::nn::Linear linear{nullptr}; // construct an empty holder
  };

In conclusion: Which ownership model -- which semantics -- should you use? The
C++ frontend's API best supports the ownership model provided by module holders.
The only disadvantage of this mechanism is one extra line of boilerplate below
the module declaration. That said, the simplest model is still the value
semantics model shown in the introduction to C++ modules. For small, simple
scripts, you may get away with it too. But you'll find sooner or later that, for
technical reasons, it is not always supported. For example, the serialization
API (``torch::save`` and ``torch::load``) only supports module holders (or plain
``shared_ptr``). As such, the module holder API is the recommended way of
defining modules with the C++ frontend, and we will use this API in this
tutorial henceforth.

Defining the DCGAN Modules
^^^^^^^^^^^^^^^^^^^^^^^^^^

We now have the necessary background and introduction to define the modules for
the machine learning task we want to solve in this post. To recap: our task is
to generate images of digits from the `MNIST dataset
<http://yann.lecun.com/exdb/mnist/>`_. We want to use a `generative adversarial
network (GAN)
<https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf>`_ to solve
this task. In particular, we'll use a `DCGAN architecture
<https://arxiv.org/abs/1511.06434>`_ -- one of the first and simplest of its
kind, but entirely sufficient for this task.

.. tip::

  You can find the full source code presented in this tutorial `in this
  repository <https://github.com/pytorch/examples/tree/master/cpp/dcgan>`_.

What was a GAN aGAN?
********************

A GAN consists of two distinct neural network models: a *generator* and a
*discriminator*. The generator receives samples from a noise distribution, and
its aim is to transform each noise sample into an image that resembles those of
a target distribution -- in our case the MNIST dataset. The discriminator in
turn receives either *real* images from the MNIST dataset, or *fake* images from
the generator. It is asked to emit a probability judging how real (closer to
``1``) or fake (closer to ``0``) a particular image is. Feedback from the
discriminator on how real the images produced by the generator are is used to
train the generator. Feedback on how good of an eye for authenticity the
discriminator has is used to optimize the discriminator. In theory, a delicate
balance between the generator and discriminator makes them improve in tandem,
leading to the generator producing images indistinguishable from the target
distribution, fooling the discriminator's (by then) excellent eye into emitting
a probability of ``0.5`` for both real and fake images. For us, the end result
is a machine that receives noise as input and generates realistic images of
digits as its output.

The Generator Module
********************

We begin by defining the generator module, which consists of a series of
transposed 2D convolutions, batch normalizations and ReLU activation units.
We explicitly pass inputs (in a functional way) between modules in the
``forward()`` method of a module we define ourselves:

.. code-block:: cpp

  struct DCGANGeneratorImpl : nn::Module {
    DCGANGeneratorImpl(int kNoiseSize)
        : conv1(nn::ConvTranspose2dOptions(kNoiseSize, 256, 4)
                    .bias(false)),
          batch_norm1(256),
          conv2(nn::ConvTranspose2dOptions(256, 128, 3)
                    .stride(2)
                    .padding(1)
                    .bias(false)),
          batch_norm2(128),
          conv3(nn::ConvTranspose2dOptions(128, 64, 4)
                    .stride(2)
                    .padding(1)
                    .bias(false)),
          batch_norm3(64),
          conv4(nn::ConvTranspose2dOptions(64, 1, 4)
                    .stride(2)
                    .padding(1)
                    .bias(false))
   {
     // register_module() is needed if we want to use the parameters() method later on
     register_module("conv1", conv1);
     register_module("conv2", conv2);
     register_module("conv3", conv3);
     register_module("conv4", conv4);
     register_module("batch_norm1", batch_norm1);
     register_module("batch_norm2", batch_norm2);
     register_module("batch_norm3", batch_norm3);
   }

   torch::Tensor forward(torch::Tensor x) {
     x = torch::relu(batch_norm1(conv1(x)));
     x = torch::relu(batch_norm2(conv2(x)));
     x = torch::relu(batch_norm3(conv3(x)));
     x = torch::tanh(conv4(x));
     return x;
   }

   nn::ConvTranspose2d conv1, conv2, conv3, conv4;
   nn::BatchNorm2d batch_norm1, batch_norm2, batch_norm3;
  };
  TORCH_MODULE(DCGANGenerator);

  DCGANGenerator generator(kNoiseSize);

We can now invoke ``forward()`` on the ``DCGANGenerator`` to map a noise sample to an image.

The particular modules chosen, like ``nn::ConvTranspose2d`` and ``nn::BatchNorm2d``,
follows the structure outlined earlier. The ``kNoiseSize`` constant determines
the size of the input noise vector and is set to ``100``. Hyperparameters were,
of course, found via grad student descent.

.. attention::

	No grad students were harmed in the discovery of hyperparameters. They were
	fed Soylent regularly.

.. note::

	A brief word on the way options are passed to built-in modules like ``Conv2d``
	in the C++ frontend: Every module has some required options, like the number
	of features for ``BatchNorm2d``. If you only need to configure the required
	options, you can pass them directly to the module's constructor, like
	``BatchNorm2d(128)`` or ``Dropout(0.5)`` or ``Conv2d(8, 4, 2)`` (for input
	channel count, output channel count, and kernel size). If, however, you need
	to modify other options, which are normally defaulted, such as ``bias``
	for ``Conv2d``, you need to construct and pass an *options* object. Every
	module in the C++ frontend has an associated options struct, called
	``ModuleOptions`` where ``Module`` is the name of the module, like
	``LinearOptions`` for ``Linear``. This is what we do for the ``Conv2d``
	modules above.

The Discriminator Module
************************

The discriminator is similarly a sequence of convolutions, batch normalizations
and activations. However, the convolutions are now regular ones instead of
transposed, and we use a leaky ReLU with an alpha value of 0.2 instead of a
vanilla ReLU. Also, the final activation becomes a Sigmoid, which squashes
values into a range between 0 and 1. We can then interpret these squashed values
as the probabilities the discriminator assigns to images being real.

To build the discriminator, we will try something different: a `Sequential` module.
Like in Python, PyTorch here provides two APIs for model definition: a functional one
where inputs are passed through successive functions (e.g. the generator module example),
and a more object-oriented one where we build a `Sequential` module containing the
entire model as submodules. Using `Sequential`, the discriminator would look like:

.. code-block:: cpp

  nn::Sequential discriminator(
    // Layer 1
    nn::Conv2d(
        nn::Conv2dOptions(1, 64, 4).stride(2).padding(1).bias(false)),
    nn::LeakyReLU(nn::LeakyReLUOptions().negative_slope(0.2)),
    // Layer 2
    nn::Conv2d(
        nn::Conv2dOptions(64, 128, 4).stride(2).padding(1).bias(false)),
    nn::BatchNorm2d(128),
    nn::LeakyReLU(nn::LeakyReLUOptions().negative_slope(0.2)),
    // Layer 3
    nn::Conv2d(
        nn::Conv2dOptions(128, 256, 4).stride(2).padding(1).bias(false)),
    nn::BatchNorm2d(256),
    nn::LeakyReLU(nn::LeakyReLUOptions().negative_slope(0.2)),
    // Layer 4
    nn::Conv2d(
        nn::Conv2dOptions(256, 1, 3).stride(1).padding(0).bias(false)),
    nn::Sigmoid());

.. tip::

  A ``Sequential`` module simply performs function composition. The output of
  the first submodule becomes the input of the second, the output of the third
  becomes the input of the fourth and so on.


Loading Data
------------

Now that we have defined the generator and discriminator model, we need some
data we can train these models with. The C++ frontend, like the Python one,
comes with a powerful parallel data loader. This data loader can read batches of
data from a dataset (which you can define yourself) and provides many
configuration knobs.

.. note::

	While the Python data loader uses multi-processing, the C++ data loader is truly
	multi-threaded and does not launch any new processes.

The data loader is part of the C++ frontend's ``data`` api, contained in the
``torch::data::`` namespace. This API consists of a few different components:

- The data loader class,
- An API for defining datasets,
- An API for defining *transforms*, which can be applied to datasets,
- An API for defining *samplers*, which produce the indices with which datasets are indexed,
- A library of existing datasets, transforms and samplers.

For this tutorial, we can use the ``MNIST`` dataset that comes with the C++
frontend. Let's instantiate a ``torch::data::datasets::MNIST`` for this, and
apply two transformations: First, we normalize the images so that they are in
the range of ``-1`` to ``+1`` (from an original range of ``0`` to ``1``).
Second, we apply the ``Stack`` *collation*, which takes a batch of tensors and
stacks them into a single tensor along the first dimension:

.. code-block:: cpp

  auto dataset = torch::data::datasets::MNIST("./mnist")
      .map(torch::data::transforms::Normalize<>(0.5, 0.5))
      .map(torch::data::transforms::Stack<>());

Note that the MNIST dataset should be located in the ``./mnist`` directory
relative to wherever you execute the training binary from. You can use `this
script <https://gist.github.com/goldsborough/6dd52a5e01ed73a642c1e772084bcd03>`_
to download the MNIST dataset.

Next, we create a data loader and pass it this dataset. To make a new data
loader, we use ``torch::data::make_data_loader``, which returns a
``std::unique_ptr`` of the correct type (which depends on the type of the
dataset, the type of the sampler and some other implementation details):

.. code-block:: cpp

  auto data_loader = torch::data::make_data_loader(std::move(dataset));

The data loader does come with a lot of options. You can inspect the full set
`here
<https://github.com/pytorch/pytorch/blob/master/torch/csrc/api/include/torch/data/dataloader_options.h>`_.
For example, to speed up the data loading, we can increase the number of
workers. The default number is zero, which means the main thread will be used.
If we set ``workers`` to ``2``, two threads will be spawned that load data
concurrently. We should also increase the batch size from its default of ``1``
to something more reasonable, like ``64`` (the value of ``kBatchSize``). So
let's create a ``DataLoaderOptions`` object and set the appropriate properties:

.. code-block:: cpp

  auto data_loader = torch::data::make_data_loader(
      std::move(dataset),
      torch::data::DataLoaderOptions().batch_size(kBatchSize).workers(2));


We can now write a loop to load batches of data, which we'll only print to the
console for now:

.. code-block:: cpp

  for (torch::data::Example<>& batch : *data_loader) {
    std::cout << "Batch size: " << batch.data.size(0) << " | Labels: ";
    for (int64_t i = 0; i < batch.data.size(0); ++i) {
      std::cout << batch.target[i].item<int64_t>() << " ";
    }
    std::cout << std::endl;
  }

The type returned by the data loader in this case is a ``torch::data::Example``.
This type is a simple struct with a ``data`` field for the data and a ``target``
field for the label. Because we applied the ``Stack`` collation earlier, the
data loader returns only a single such example. If we had not applied the
collation, the data loader would yield ``std::vector<torch::data::Example<>>``
instead, with one element per example in the batch.

If you rebuild and run this code, you should see something like this:

.. code-block:: shell

  root@fa350df05ecf:/home/build# make
  Scanning dependencies of target dcgan
  [ 50%] Building CXX object CMakeFiles/dcgan.dir/dcgan.cpp.o
  [100%] Linking CXX executable dcgan
  [100%] Built target dcgan
  root@fa350df05ecf:/home/build# make
  [100%] Built target dcgan
  root@fa350df05ecf:/home/build# ./dcgan
  Batch size: 64 | Labels: 5 2 6 7 2 1 6 7 0 1 6 2 3 6 9 1 8 4 0 6 5 3 3 0 4 6 6 6 4 0 8 6 0 6 9 2 4 0 2 8 6 3 3 2 9 2 0 1 4 2 3 4 8 2 9 9 3 5 8 0 0 7 9 9
  Batch size: 64 | Labels: 2 2 4 7 1 2 8 8 6 9 0 2 2 9 3 6 1 3 8 0 4 4 8 8 8 9 2 6 4 7 1 5 0 9 7 5 4 3 5 4 1 2 8 0 7 1 9 6 1 6 5 3 4 4 1 2 3 2 3 5 0 1 6 2
  Batch size: 64 | Labels: 4 5 4 2 1 4 8 3 8 3 6 1 5 4 3 6 2 2 5 1 3 1 5 0 8 2 1 5 3 2 4 4 5 9 7 2 8 9 2 0 6 7 4 3 8 3 5 8 8 3 0 5 8 0 8 7 8 5 5 6 1 7 8 0
  Batch size: 64 | Labels: 3 3 7 1 4 1 6 1 0 3 6 4 0 2 5 4 0 4 2 8 1 9 6 5 1 6 3 2 8 9 2 3 8 7 4 5 9 6 0 8 3 0 0 6 4 8 2 5 4 1 8 3 7 8 0 0 8 9 6 7 2 1 4 7
  Batch size: 64 | Labels: 3 0 5 5 9 8 3 9 8 9 5 9 5 0 4 1 2 7 7 2 0 0 5 4 8 7 7 6 1 0 7 9 3 0 6 3 2 6 2 7 6 3 3 4 0 5 8 8 9 1 9 2 1 9 4 4 9 2 4 6 2 9 4 0
  Batch size: 64 | Labels: 9 6 7 5 3 5 9 0 8 6 6 7 8 2 1 9 8 8 1 1 8 2 0 7 1 4 1 6 7 5 1 7 7 4 0 3 2 9 0 6 6 3 4 4 8 1 2 8 6 9 2 0 3 1 2 8 5 6 4 8 5 8 6 2
  Batch size: 64 | Labels: 9 3 0 3 6 5 1 8 6 0 1 9 9 1 6 1 7 7 4 4 4 7 8 8 6 7 8 2 6 0 4 6 8 2 5 3 9 8 4 0 9 9 3 7 0 5 8 2 4 5 6 2 8 2 5 3 7 1 9 1 8 2 2 7
  Batch size: 64 | Labels: 9 1 9 2 7 2 6 0 8 6 8 7 7 4 8 6 1 1 6 8 5 7 9 1 3 2 0 5 1 7 3 1 6 1 0 8 6 0 8 1 0 5 4 9 3 8 5 8 4 8 0 1 2 6 2 4 2 7 7 3 7 4 5 3
  Batch size: 64 | Labels: 8 8 3 1 8 6 4 2 9 5 8 0 2 8 6 6 7 0 9 8 3 8 7 1 6 6 2 7 7 4 5 5 2 1 7 9 5 4 9 1 0 3 1 9 3 9 8 8 5 3 7 5 3 6 8 9 4 2 0 1 2 5 4 7
  Batch size: 64 | Labels: 9 2 7 0 8 4 4 2 7 5 0 0 6 2 0 5 9 5 9 8 8 9 3 5 7 5 4 7 3 0 5 7 6 5 7 1 6 2 8 7 6 3 2 6 5 6 1 2 7 7 0 0 5 9 0 0 9 1 7 8 3 2 9 4
  Batch size: 64 | Labels: 7 6 5 7 7 5 2 2 4 9 9 4 8 7 4 8 9 4 5 7 1 2 6 9 8 5 1 2 3 6 7 8 1 1 3 9 8 7 9 5 0 8 5 1 8 7 2 6 5 1 2 0 9 7 4 0 9 0 4 6 0 0 8 6
  ...

Which means we are successfully able to load data from the MNIST dataset.

Writing the Training Loop
-------------------------

Let's now finish the algorithmic part of our example and implement the delicate
dance between the generator and discriminator. First, we'll create two
optimizers, one for the generator and one for the discriminator. The optimizers
we use implement the `Adam <https://arxiv.org/pdf/1412.6980.pdf>`_ algorithm:

.. code-block:: cpp

  torch::optim::Adam generator_optimizer(
      generator->parameters(), torch::optim::AdamOptions(2e-4).beta1(0.5));
  torch::optim::Adam discriminator_optimizer(
      discriminator->parameters(), torch::optim::AdamOptions(5e-4).beta1(0.5));

.. note::

	As of this writing, the C++ frontend provides optimizers implementing Adagrad,
	Adam, LBFGS, RMSprop and SGD. The `docs
	<https://pytorch.org/cppdocs/api/namespace_torch__optim.html>`_ have the
	up-to-date list.

Next, we need to update our training loop. We'll add an outer loop to exhaust
the data loader every epoch and then write the GAN training code:

.. code-block:: cpp

  for (int64_t epoch = 1; epoch <= kNumberOfEpochs; ++epoch) {
    int64_t batch_index = 0;
    for (torch::data::Example<>& batch : *data_loader) {
      // Train discriminator with real images.
      discriminator->zero_grad();
      torch::Tensor real_images = batch.data;
      torch::Tensor real_labels = torch::empty(batch.data.size(0)).uniform_(0.8, 1.0);
      torch::Tensor real_output = discriminator->forward(real_images);
      torch::Tensor d_loss_real = torch::binary_cross_entropy(real_output, real_labels);
      d_loss_real.backward();

      // Train discriminator with fake images.
      torch::Tensor noise = torch::randn({batch.data.size(0), kNoiseSize, 1, 1});
      torch::Tensor fake_images = generator->forward(noise);
      torch::Tensor fake_labels = torch::zeros(batch.data.size(0));
      torch::Tensor fake_output = discriminator->forward(fake_images.detach());
      torch::Tensor d_loss_fake = torch::binary_cross_entropy(fake_output, fake_labels);
      d_loss_fake.backward();

      torch::Tensor d_loss = d_loss_real + d_loss_fake;
      discriminator_optimizer.step();

      // Train generator.
      generator->zero_grad();
      fake_labels.fill_(1);
      fake_output = discriminator->forward(fake_images);
      torch::Tensor g_loss = torch::binary_cross_entropy(fake_output, fake_labels);
      g_loss.backward();
      generator_optimizer.step();

      std::printf(
          "\r[%2ld/%2ld][%3ld/%3ld] D_loss: %.4f | G_loss: %.4f",
          epoch,
          kNumberOfEpochs,
          ++batch_index,
          batches_per_epoch,
          d_loss.item<float>(),
          g_loss.item<float>());
    }
  }

Above, we first evaluate the discriminator on real images, for which it should
assign a high probability. For this, we use
``torch::empty(batch.data.size(0)).uniform_(0.8, 1.0)`` as the target
probabilities.

.. note::

	We pick random values uniformly distributed between 0.8 and 1.0 instead of 1.0
	everywhere in order to make the discriminator training more robust. This trick
	is called *label smoothing*.

Before evaluating the discriminator, we zero out the gradients of its
parameters. After computing the loss, we back-propagate through the network by
calling ``d_loss.backward()`` to compute new gradients. We repeat this spiel for
the fake images. Instead of using images from the dataset, we let the generator
create fake images for this by feeding it a batch of random noise. We then
forward those fake images to the discriminator. This time, we want the
discriminator to emit low probabilities, ideally all zeros. Once we have
computed the discriminator loss for both the batch of real and the batch of fake
images, we can progress the discriminator's optimizer by one step in order to
update its parameters.

To train the generator, we again first zero its gradients, and then re-evaluate
the discriminator on the fake images. However, this time we want the
discriminator to assign probabilities very close to one, which would indicate
that the generator can produce images that fool the discriminator into thinking
they are actually real (from the dataset). For this, we fill the ``fake_labels``
tensor with all ones. We finally step the generator's optimizer to also update
its parameters.

We should now be ready to train our model on the CPU. We don't have any code yet
to capture state or sample outputs, but we'll add this in just a moment. For
now, let's just observe that our model is doing *something* -- we'll later
verify based on the generated images whether this something is meaningful.
Re-building and running should print something like:

.. code-block:: shell

  root@3c0711f20896:/home/build# make && ./dcgan
  Scanning dependencies of target dcgan
  [ 50%] Building CXX object CMakeFiles/dcgan.dir/dcgan.cpp.o
  [100%] Linking CXX executable dcgan
  [100%] Built target dcga
  [ 1/10][100/938] D_loss: 0.6876 | G_loss: 4.1304
  [ 1/10][200/938] D_loss: 0.3776 | G_loss: 4.3101
  [ 1/10][300/938] D_loss: 0.3652 | G_loss: 4.6626
  [ 1/10][400/938] D_loss: 0.8057 | G_loss: 2.2795
  [ 1/10][500/938] D_loss: 0.3531 | G_loss: 4.4452
  [ 1/10][600/938] D_loss: 0.3501 | G_loss: 5.0811
  [ 1/10][700/938] D_loss: 0.3581 | G_loss: 4.5623
  [ 1/10][800/938] D_loss: 0.6423 | G_loss: 1.7385
  [ 1/10][900/938] D_loss: 0.3592 | G_loss: 4.7333
  [ 2/10][100/938] D_loss: 0.4660 | G_loss: 2.5242
  [ 2/10][200/938] D_loss: 0.6364 | G_loss: 2.0886
  [ 2/10][300/938] D_loss: 0.3717 | G_loss: 3.8103
  [ 2/10][400/938] D_loss: 1.0201 | G_loss: 1.3544
  [ 2/10][500/938] D_loss: 0.4522 | G_loss: 2.6545
  ...

Moving to the GPU
-----------------

While our current script can run just fine on the CPU, we all know convolutions
are a lot faster on GPU. Let's quickly discuss how we can move our training onto
the GPU. We'll need to do two things for this: pass a GPU device specification
to tensors we allocate ourselves, and explicitly copy any other tensors onto the
GPU via the ``to()`` method all tensors and modules in the C++ frontend have.
The simplest way to achieve both is to create an instance of ``torch::Device``
at the top level of our training script, and then pass that device to tensor
factory functions like ``torch::zeros`` as well as the ``to()`` method. We can
start by doing this with a CPU device:

.. code-block:: cpp

  // Place this somewhere at the top of your training script.
  torch::Device device(torch::kCPU);

New tensor allocations like

.. code-block:: cpp

  torch::Tensor fake_labels = torch::zeros(batch.data.size(0));

should be updated to take the ``device`` as the last argument:

.. code-block:: cpp

  torch::Tensor fake_labels = torch::zeros(batch.data.size(0), device);

For tensors whose creation is not in our hands, like those coming from the MNIST
dataset, we must insert explicit ``to()`` calls. This means

.. code-block:: cpp

  torch::Tensor real_images = batch.data;

becomes

.. code-block:: cpp

  torch::Tensor real_images = batch.data.to(device);

and also our model parameters should be moved to the correct device:

.. code-block:: cpp

  generator->to(device);
  discriminator->to(device);

.. note::

	If a tensor already lives on the device supplied to ``to()``, the call is a
	no-op. No extra copy is made.

At this point, we've just made our previous CPU-residing code more explicit.
However, it is now also very easy to change the device to a CUDA device:

.. code-block:: cpp

  torch::Device device(torch::kCUDA)

And now all tensors will live on the GPU, calling into fast CUDA kernels for all
operations, without us having to change any downstream code. If we wanted to
specify a particular device index, it could be passed as the second argument to
the ``Device`` constructor. If we wanted different tensors to live on different
devices, we could pass separate device instances (for example one on CUDA device
0 and the other on CUDA device 1). We can even do this configuration
dynamically, which is often useful to make our training scripts more portable:

.. code-block:: cpp

  torch::Device device = torch::kCPU;
  if (torch::cuda::is_available()) {
    std::cout << "CUDA is available! Training on GPU." << std::endl;
    device = torch::kCUDA;
  }

or even

.. code-block:: cpp

  torch::Device device(torch::cuda::is_available() ? torch::kCUDA : torch::kCPU);

Checkpointing and Recovering the Training State
-----------------------------------------------

The last augmentation we should make to our training script is to periodically
save the state of our model parameters, the state of our optimizers as well as a
few generated image samples. If our computer were to crash in the middle of the
training procedure, the first two will allow us to restore the training state.
For long-lasting training sessions, this is absolutely essential. Fortunately,
the C++ frontend provides an API to serialize and deserialize both model and
optimizer state, as well as individual tensors.

The core API for this is ``torch::save(thing,filename)`` and
``torch::load(thing,filename)``, where ``thing`` could be a
``torch::nn::Module`` subclass or an optimizer instance like the ``Adam`` object
we have in our training script. Let's update our training loop to checkpoint the
model and optimizer state at a certain interval:

.. code-block:: cpp

  if (batch_index % kCheckpointEvery == 0) {
    // Checkpoint the model and optimizer state.
    torch::save(generator, "generator-checkpoint.pt");
    torch::save(generator_optimizer, "generator-optimizer-checkpoint.pt");
    torch::save(discriminator, "discriminator-checkpoint.pt");
    torch::save(discriminator_optimizer, "discriminator-optimizer-checkpoint.pt");
    // Sample the generator and save the images.
    torch::Tensor samples = generator->forward(torch::randn({8, kNoiseSize, 1, 1}, device));
    torch::save((samples + 1.0) / 2.0, torch::str("dcgan-sample-", checkpoint_counter, ".pt"));
    std::cout << "\n-> checkpoint " << ++checkpoint_counter << '\n';
  }

where ``kCheckpointEvery`` is an integer set to something like ``100`` to
checkpoint every ``100`` batches, and ``checkpoint_counter`` is a counter bumped
every time we make a checkpoint.

To restore the training state, you can add lines like these after all models and
optimizers are created, but before the training loop:

.. code-block:: cpp

  torch::optim::Adam generator_optimizer(
      generator->parameters(), torch::optim::AdamOptions(2e-4).beta1(0.5));
  torch::optim::Adam discriminator_optimizer(
      discriminator->parameters(), torch::optim::AdamOptions(2e-4).beta1(0.5));

  if (kRestoreFromCheckpoint) {
    torch::load(generator, "generator-checkpoint.pt");
    torch::load(generator_optimizer, "generator-optimizer-checkpoint.pt");
    torch::load(discriminator, "discriminator-checkpoint.pt");
    torch::load(
        discriminator_optimizer, "discriminator-optimizer-checkpoint.pt");
  }

  int64_t checkpoint_counter = 0;
  for (int64_t epoch = 1; epoch <= kNumberOfEpochs; ++epoch) {
    int64_t batch_index = 0;
    for (torch::data::Example<>& batch : *data_loader) {


Inspecting Generated Images
---------------------------

Our training script is now complete. We are ready to train our GAN, whether on
CPU or GPU. To inspect the intermediary output of our training procedure, for
which we added code to periodically save image samples to the
``"dcgan-sample-xxx.pt"`` file, we can write a tiny Python script to load the
tensors and display them with matplotlib:

.. code-block:: python

  from __future__ import print_function
  from __future__ import unicode_literals

  import argparse

  import matplotlib.pyplot as plt
  import torch


  parser = argparse.ArgumentParser()
  parser.add_argument("-i", "--sample-file", required=True)
  parser.add_argument("-o", "--out-file", default="out.png")
  parser.add_argument("-d", "--dimension", type=int, default=3)
  options = parser.parse_args()

  module = torch.jit.load(options.sample_file)
  images = list(module.parameters())[0]

  for index in range(options.dimension * options.dimension):
    image = images[index].detach().cpu().reshape(28, 28).mul(255).to(torch.uint8)
    array = image.numpy()
    axis = plt.subplot(options.dimension, options.dimension, 1 + index)
    plt.imshow(array, cmap="gray")
    axis.get_xaxis().set_visible(False)
    axis.get_yaxis().set_visible(False)

  plt.savefig(options.out_file)
  print("Saved ", options.out_file)

Let's now train our model for around 30 epochs:

.. code-block:: shell

  root@3c0711f20896:/home/build# make && ./dcgan                                                                                                                                10:17:57
  Scanning dependencies of target dcgan
  [ 50%] Building CXX object CMakeFiles/dcgan.dir/dcgan.cpp.o
  [100%] Linking CXX executable dcgan
  [100%] Built target dcgan
  CUDA is available! Training on GPU.
  [ 1/30][200/938] D_loss: 0.4953 | G_loss: 4.0195
  -> checkpoint 1
  [ 1/30][400/938] D_loss: 0.3610 | G_loss: 4.8148
  -> checkpoint 2
  [ 1/30][600/938] D_loss: 0.4072 | G_loss: 4.36760
  -> checkpoint 3
  [ 1/30][800/938] D_loss: 0.4444 | G_loss: 4.0250
  -> checkpoint 4
  [ 2/30][200/938] D_loss: 0.3761 | G_loss: 3.8790
  -> checkpoint 5
  [ 2/30][400/938] D_loss: 0.3977 | G_loss: 3.3315
  ...
  -> checkpoint 120
  [30/30][938/938] D_loss: 0.3610 | G_loss: 3.8084

And display the imags in a plot:

.. code-block:: shell

  root@3c0711f20896:/home/build# python display.py -i dcgan-sample-100.pt
  Saved out.png

Which should look something like this:

.. figure:: /_static/img/cpp-frontend/digits.png
   :alt: digits

Digits! Hooray! Now the ball is in your court: can you improve the model to make
the digits look even better?

Conclusion
----------

This tutorial has hopefully given you a digestible digest of the PyTorch C++
frontend. A machine learning library like PyTorch by necessity has a very broad
and extensive API. As such, there are many concepts we did not have time or
space to discuss here. However, I encourage you to try out the API, and consult
`our documentation <https://pytorch.org/cppdocs/>`_ and in particular the
`Library API <https://pytorch.org/cppdocs/api/library_root.html>`_ section when
you get stuck. Also, remember that you can expect the C++ frontend to follow the
design and semantics of the Python frontend whenever we could make this
possible, so you can leverage this fact to increase your learning rate.

.. tip::

  You can find the full source code presented in this tutorial `in this
  repository <https://github.com/pytorch/examples/tree/master/cpp/dcgan>`_.

As always, if you run into any problems or have questions, you can use our
`forum <https://discuss.pytorch.org/>`_ or `GitHub issues
<https://github.com/pytorch/pytorch/issues>`_ to get in touch.