.. _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


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

- Convolution operators implemented:
    - :func:`signal.conv2d <theano.tensor.signal.conv.conv2d>`. See note above.
    - :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.
      Most of the more efficient GPU implementations listed below can be used
      as an automatic replacement for nnet.conv2d by enabling specific graph
      optimizations.
    - :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. 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_valid:conv_fft_full'
      in your environement.  This 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 flags are 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_valid', 'conv_fft_full')

          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.

      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.

      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.
      Alternatively, you can use nnet.conv2d and allow Theano's graph optimizer
      to replace it by the GEMM version by setting
      ``THEANO_FLAGS=optimizer_including=conv_gemm`` in your environment.
      This is not enabled by default because it uses some extra memory, but the
      overhead is small compared to conv2d_fft, there are no restrictions on
      input or kernel shapes and it is sometimes still faster than cuda-convnet.
      If using it, please see the warning about a bug in CUDA 5.0 to 6.0 below.
      To enable it for just one Theano function:

      .. code-block:: python

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

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

    - :func:`conv3D <theano.tensor.nnet.Conv3D.conv3D>`
      3D Convolution applying multi-channel 3D filters to batches of
      multi-channel 3D images.
    - :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:`conv3d2d <theano.tensor.nnet.conv3d2d.conv3d>`
      Another conv3d implementation that uses the conv2d with data reshaping.
      It is faster in some cases than conv3d, specifically on the GPU.

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