.. _libdoc_tensor_nnet_conv:

==========================================================
:mod:`conv` -- Ops for convolutional neural nets
==========================================================

.. note::

    Two similar implementation exists for conv2d:

        :func:`signal.conv2d <theano.tensor.signal.conv.conv2d>` and
        :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>`.

    The former implements a traditional
    2D convolution, while the latter implements the convolutional layers
    present in convolutional neural networks (where filters are 3D and pool
    over several input channels).

.. module:: conv
   :platform: Unix, Windows
   :synopsis: ops for signal processing
.. moduleauthor:: LISA


.. note::

    As of October 21st, 2014, the default GPU image convolution
    changed: By default, if :ref:`cuDNN <libdoc_cuda_dnn>`
    is available, we will use it, otherwise we will fall back to using the
    gemm version (slower then cuDNN in most cases and uses more memory).

    Both cuDNN and the gemm version can be disabled using the Theano flags
    ``optimizer_excluding=conv_dnn`` and ``optimizer_excluding=conv_gemm``,
    respectively. In this case, we will fall back to using the legacy
    convolution code, which is slower, but does not require extra memory.
    To verify that cuDNN is used, you can supply the Theano flag
    ``optimizer_including=cudnn``. This will raise an error if cuDNN is
    unavailable.

    It is not advised to ever disable cuDNN, as this is usually the fastest
    option. Disabling the gemm version is only useful if cuDNN is unavailable
    and you run out of GPU memory.

    There are two other implementations: An FFT-based convolution integrated
    into Theano, and an implementation by Alex Krizhevsky available via
    Pylearn2. See the documentation below on how to use them.

    As of November 24th, 2014, you can also use a meta-optimizer to
    automatically choose the fastest implementation for each specific
    convolution in your graph. For each instance, it will compile and benchmark
    each applicable implementation of the ones listed below and choose the
    fastest one. As performance is dependent on input and filter shapes, this
    only works for operations introduced via nnet.conv2d with fully specified
    shape information.
    Enable it via the Theano flag ``optimizer_including=conv_meta``, and
    optionally set it to verbose mode via the flag `metaopt.verbose=1`.


TODO: Give examples on how to use these things! They are pretty complicated.

- Implemented operators for neural network 2D / image convolution:
    - :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>`.
      This is the standard operator for convolutional neural networks working
      with batches of multi-channel 2D images, available for CPU and GPU. It
      computes a convolution, i.e., it flips the kernel.
      Most of the more efficient GPU implementations listed below can be
      inserted automatically as a replacement for nnet.conv2d via graph
      optimizations. Some of these graph optimizations are enabled by default,
      others can be enabled via Theano flags.
    - :func:`conv2d_fft <theano.sandbox.cuda.fftconv.conv2d_fft>` This
      is a GPU-only version of nnet.conv2d that uses an FFT transform
      to perform the work.  It flips the kernel just like ``conv2d``.
      conv2d_fft should not be used directly as
      it does not provide a gradient. Instead, use nnet.conv2d and
      allow Theano's graph optimizer to replace it by the FFT version
      by setting 'THEANO_FLAGS=optimizer_including=conv_fft'
      in your environment. If enabled, it will take precedence over cuDNN
      and the gemm version.  It is not enabled by default because it
      has some restrictions on input and uses a lot more memory.  Also
      note that it requires CUDA >= 5.0, scikits.cuda >= 0.5.0 and
      PyCUDA to run.  To deactivate the FFT optimization on a specific
      nnet.conv2d while the optimization flag is active, you can set
      its ``version`` parameter to ``'no_fft'``. To enable it for just
      one Theano function:

      .. code-block:: python

          mode = theano.compile.get_default_mode()
          mode = mode.including('conv_fft')

          f = theano.function(..., mode=mode)

    - `cuda-convnet wrapper for 2d correlation <http://deeplearning.net/software/pylearn2/library/alex.html>`_

      Wrapper for an open-source GPU-only implementation of conv2d by Alex
      Krizhevsky, very fast, but with several restrictions on input and kernel
      shapes, and with a different memory layout for the input. It does not
      flip the kernel.

      This is in Pylearn2, where it is normally called from the `linear transform
      <http://deeplearning.net/software/pylearn2/library/linear.html>`_
      implementation, but it can also be used `directly from within Theano
      <http://benanne.github.io/2014/04/03/faster-convolutions-in-theano.html>`_
      as a manual replacement for nnet.conv2d.
    - :func:`GpuCorrMM <theano.sandbox.cuda.blas.GpuCorrMM>`
      This is a GPU-only 2d correlation implementation taken from
      `caffe <https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cu>`_
      and also used by Torch. It does not flip the kernel.

      For each element in a batch, it first creates a
      `Toeplitz <http://en.wikipedia.org/wiki/Toeplitz_matrix>`_ matrix in a CUDA kernel.
      Then, it performs a ``gemm`` call to multiply this Toeplitz matrix and the filters
      (hence the name: MM is for matrix multiplication).
      It needs extra memory for the Toeplitz matrix, which is a 2D matrix of shape
      ``(no of channels * filter width * filter height, output width * output height)``.

      As it provides a gradient, you can use it as a replacement for nnet.conv2d.
      But usually, you will just use nnet.conv2d and allow Theano's graph
      optimizer to automatically replace it by the GEMM version if cuDNN is not
      available. To explicitly disable the graph optimizer, set
      ``THEANO_FLAGS=optimizer_excluding=conv_gemm`` in your environment.
      If using it, please see the warning about a bug in CUDA 5.0 to 6.0 below.
    - :func:`CorrMM <theano.tensor.nnet.corr.CorrMM>`
      This is a CPU-only 2d correlation implementation taken from
      `caffe <https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cpp>`_
      and also used by Torch. It does not flip the kernel. As it provides a gradient,
      you can use it as a replacement for nnet.conv2d. For convolutions done on
      CPU, nnet.conv2d will be replaced by CorrMM. To explicitly disable it, set
      ``THEANO_FLAGS=optimizer_excluding=conv_gemm`` in your environment.
    - :func:`dnn_conv <theano.sandbox.cuda.dnn.dnn_conv>` GPU-only
      convolution using NVIDIA's cuDNN library. This requires that you have
      cuDNN installed and available, which in turn requires CUDA 6.5 and a GPU
      with compute capability 3.0 or more.

      If cuDNN is available, by default, Theano will replace all nnet.conv2d
      operations with dnn_conv. To explicitly disable it, set
      ``THEANO_FLAGS=optimizer_excluding=conv_dnn`` in your environment.
      As dnn_conv has a gradient defined, you can also use it manually.
- Implemented operators for neural network 3D / video convolution:
    - :func:`conv3D <theano.tensor.nnet.Conv3D.conv3D>`
      3D Convolution applying multi-channel 3D filters to batches of
      multi-channel 3D images. It does not flip the kernel.
    - :func:`conv3d_fft <theano.sandbox.cuda.fftconv.conv3d_fft>`
      GPU-only version of conv3D using FFT transform. conv3d_fft should
      not be called directly as it does not provide a gradient.
      Instead, use conv3D and allow Theano's graph optimizer to replace it by
      the FFT version by setting
      ``THEANO_FLAGS=optimizer_including=conv3d_fft:convgrad3d_fft:convtransp3d_fft``
      in your environment. This is not enabled by default because it does not
      support strides and uses more memory. Also note that it requires
      CUDA >= 5.0, scikits.cuda >= 0.5.0 and PyCUDA to run.
      To enable for just one Theano function:

      .. code-block:: python

          mode = theano.compile.get_default_mode()
          mode = mode.including('conv3d_fft', 'convgrad3d_fft', 'convtransp3d_fft')

          f = theano.function(..., mode=mode)

    - :func:`GpuCorr3dMM <theano.sandbox.cuda.blas.GpuCorr3dMM>`
      This is a GPU-only 3d correlation relying on a Toeplitz matrix
      and gemm implementation (see :func:`GpuCorrMM <theano.sandbox.cuda.blas.GpuCorrMM>`)
      It needs extra memory for the Toeplitz matrix, which is a 2D matrix of shape
      ``(no of channels * filter width * filter height * filter depth, output width * output height * output depth)``.
      As it provides a gradient, you can use it as a replacement for nnet.conv3d.
      Alternatively, you can use nnet.conv3d and allow Theano's graph optimizer
      to replace it by the GEMM version by setting
      ``THEANO_FLAGS=optimizer_including=conv3d_gemm:convgrad3d_gemm:convtransp3d_gemm`` in your environment.
      This is not enabled by default because it uses some extra memory, but the
      overhead is small compared to conv3d_fft, there are no restrictions on
      input or kernel shapes and strides are supported. If using it,
      please see the warning about a bug in CUDA 5.0 to 6.0
      in :func:`GpuCorrMM <theano.sandbox.cuda.blas.GpuCorrMM>`.

    - :func:`conv3d2d <theano.tensor.nnet.conv3d2d.conv3d>`
      Another conv3d implementation that uses the conv2d with data reshaping.
      It is faster in some cases than conv3d, and work on the GPU.
      It flip the kernel.

.. autofunction:: theano.tensor.nnet.conv.conv2d
.. autofunction:: theano.sandbox.cuda.fftconv.conv2d_fft
.. autofunction:: theano.tensor.nnet.Conv3D.conv3D
.. autofunction:: theano.sandbox.cuda.fftconv.conv3d_fft
.. autofunction:: theano.tensor.nnet.conv3d2d.conv3d
