chromium/third_party/eigen3/src/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <[email protected]>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H
#define EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H

// IWYU pragma: private
#include "./InternalHeaderCheck.h"

namespace Eigen {
namespace internal {

template <typename Scalar, int Options>
class compute_tensor_flags { … };

traits<Tensor<Scalar_, NumIndices_, Options_, IndexType_>>;

traits<TensorFixedSize<Scalar_, Dimensions, Options_, IndexType_>>;

traits<TensorMap<PlainObjectType, Options_, MakePointer_>>;

traits<TensorRef<PlainObjectType>>;

eval<Tensor<Scalar_, NumIndices_, Options, IndexType_>, Eigen::Dense>;

eval<const Tensor<Scalar_, NumIndices_, Options, IndexType_>, Eigen::Dense>;

eval<TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense>;

eval<const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense>;

eval<TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense>;

eval<const TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense>;

eval<TensorRef<PlainObjectType>, Eigen::Dense>;

eval<const TensorRef<PlainObjectType>, Eigen::Dense>;

// TODO nested<> does not exist anymore in Eigen/Core, and it thus has to be removed in favor of ref_selector.
template <typename T, int n = 1, typename PlainObject = void>
struct nested { … };

nested<Tensor<Scalar_, NumIndices_, Options_, IndexType_>>;

nested<const Tensor<Scalar_, NumIndices_, Options_, IndexType_>>;

nested<TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>>;

nested<const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>>;

nested<TensorRef<PlainObjectType>>;

nested<const TensorRef<PlainObjectType>>;

}  // end namespace internal

// Convolutional layers take in an input tensor of shape (D, R, C, B), or (D, C,
// R, B), and convolve it with a set of filters, which can also be presented as
// a tensor (D, K, K, M), where M is the number of filters, K is the filter
// size, and each 3-dimensional tensor of size (D, K, K) is a filter. For
// simplicity we assume that we always use square filters (which is usually the
// case in images), hence the two Ks in the tensor dimension.  It also takes in
// a few additional parameters:
// Stride (S): The convolution stride is the offset between locations where we
//             apply the filters.  A larger stride means that the output will be
//             spatially smaller.
// Padding (P): The padding we apply to the input tensor along the R and C
//              dimensions.  This is usually used to make sure that the spatial
//              dimensions of the output matches our intention.
//
// Two types of padding are often used:
//   SAME: The pad value is computed so that the output will have size
//         R/S and C/S.
//   VALID: no padding is carried out.
// When we do padding, the padded values at the padded locations are usually
// zero.
//
// The output dimensions for convolution, when given all the parameters above,
// are as follows:
// When Padding = SAME: the output size is (B, R', C', M), where
//   R' = ceil(float(R) / float(S))
//   C' = ceil(float(C) / float(S))
// where ceil is the ceiling function.  The input tensor is padded with 0 as
// needed.  The number of padded rows and columns are computed as:
//   Pr = ((R' - 1) * S + K - R) / 2
//   Pc = ((C' - 1) * S + K - C) / 2
// when the stride is 1, we have the simplified case R'=R, C'=C, Pr=Pc=(K-1)/2.
// This is where SAME comes from - the output has the same size as the input has.
// When Padding = VALID: the output size is computed as
//   R' = ceil(float(R - K + 1) / float(S))
//   C' = ceil(float(C - K + 1) / float(S))
// and the number of padded rows and columns are computed in the same way as in
// the SAME case.
// When the stride is 1, we have the simplified case R'=R-K+1, C'=C-K+1, Pr=0,
// Pc=0.
enum PaddingType { … };

}  // end namespace Eigen

#endif  // EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H