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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// 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_NEURAL_NETWORKS_ATTENTION_H
#define EIGEN_CXX11_NEURAL_NETWORKS_ATTENTION_H
namespace Eigen {
/** ExtractGlimpses
* \ingroup CXX11_NeuralNetworks_Module
*
* \brief Extract glimpses from an input tensor.
*
* The input parameter is expected to be a col-major tensor with a rank of 4 (depth, x, y, and batch).
* The width and height parameters specify the extension of the returned glimpses.
* The offsets parameter specifies the x, y locations of the center of the glimpses relative to the center of the input image. The vector is expected to contain one IndexPair for each image in the batch dimension.
* The normalized boolean indicates if incoming coordinates are normalized so that 0.0 and 1.0 correspond to the minimum and maximum of each height and width dimension.
* The centered boolean indicates if incoming coordinates are centered relative to the image, in which case -1.0 and 1.0 correspond to minimum and maximum of each dimension while 0.0 corresponds to the center.
*
* The result can be assigned to a tensor of rank equal to that of the input. The result will be laid out in col-major order (depth, x, y, batch).
* The dimensions of the result will be equal to the dimensions of the input except for width and height which will be equal to the requested glimpse size.
*/
namespace {
template <typename Index>
struct GlimpseExtractionOp {
GlimpseExtractionOp(const Index width, const Index height,
const std::vector<IndexPair<float> >& offsets,
const bool normalized,
const bool centered,
const bool uniform_noise) :
width_(width), height_(height), offsets_(offsets),
normalized_(normalized), centered_(centered), uniform_noise_(uniform_noise) { }
template <typename Input>
DSizes<Index, 4> dimensions(const Input& input) const {
typedef typename internal::traits<Input>::Index IndexType;
typedef TensorRef<Tensor<typename internal::traits<Input>::Scalar, 4,
internal::traits<Input>::Layout, IndexType> > Ref;
Ref in(input);
DSizes<Index, 4> dims = in.dimensions();
dims[0] = in.dimension(0);
dims[1] = width_;
dims[2] = height_;
dims[3] = in.dimension(3);
return dims;
}
template <typename Input, typename Output, typename Device>
EIGEN_DEVICE_FUNC
void eval(const Input& input, Output& output, const Device& device) const
{
typedef typename internal::traits<Input>::Index IndexType;
typedef TensorRef<Tensor<typename internal::traits<Input>::Scalar, 4,
internal::traits<Input>::Layout, IndexType> > Ref;
Ref in(input);
const Index num_channels = in.dimension(0);
const Index input_width = in.dimension(1);
const Index input_height = in.dimension(2);
const Index batch_size = in.dimension(3);
eigen_assert(input_width > 0);
eigen_assert(input_height > 0);
for (Index i = 0; i < batch_size; ++i) {
float x = offsets_[i].first, y = offsets_[i].second;
// Un-normalize coordinates back to pixel space if normalized.
if (normalized_) {
x *= input_width;
y *= input_height;
}
// Un-center if coordinates are centered on the image center.
if (centered_) {
x /= 2.0f;
y /= 2.0f;
x += input_width / 2.0f;
y += input_height / 2.0f;
}
// Remove half of the glimpse window.
x -= width_ / 2.0f;
y -= height_ / 2.0f;
const Index offset_x = (Index) x;
const Index offset_y = (Index) y;
Index glimpse_width = width_;
Index glimpse_height = height_;
bool partial_overlap = false;
DSizes<Index, 3> slice_offset(0, offset_x, offset_y);
DSizes<Index, 3> slice_extent(num_channels, width_, height_);
DSizes<Index, 3> base_offset(0, 0, 0);
if (offset_x < 0) {
slice_offset[1] = 0;
glimpse_width = (std::max<Index>)(0, width_ + offset_x);
slice_extent[1] = glimpse_width;
base_offset[1] = width_ - glimpse_width;
partial_overlap = true;
} else if (offset_x + width_ >= input_width) {
glimpse_width = (std::max<Index>)(0, input_width - offset_x);
slice_extent[1] = glimpse_width;
partial_overlap = true;
}
if (offset_y < 0) {
slice_offset[2] = 0;
glimpse_height = (std::max<Index>)(0, height_ + offset_y);
slice_extent[2] = glimpse_height;
base_offset[2] = height_ - glimpse_height;
partial_overlap = true;
} else if (offset_y + height_ >= input_height) {
glimpse_height = (std::max<Index>)(0, input_height - offset_y);
slice_extent[2] = glimpse_height;
partial_overlap = true;
}
slice_extent[1] = std::min<Index>(input_width, slice_extent[1]);
slice_extent[2] = std::min<Index>(input_height, slice_extent[2]);
if (partial_overlap) {
if (uniform_noise_) {
// Initialize the glimpse with uniform noise.
typedef typename internal::remove_const<
typename internal::traits<Input>::Scalar>::type Scalar;
TensorFixedSize<Scalar, Sizes<> > mini;
mini.device(device) = input.template chip<3>(i).minimum();
TensorFixedSize<float, Sizes<> > range;
range.device(device) =
(input.template chip<3>(i).maximum() - mini).template cast<float>();
DSizes<Index, 3> glimpse_size(num_channels, width_, height_);
TensorMap<Tensor<float, 3> > tmp(NULL, glimpse_size);
output.template chip<3>(i).device(device) =
mini.reshape(Sizes<1,1,1>()).broadcast(glimpse_size) +
(tmp.random() * range.reshape(Sizes<1,1,1>()).broadcast(glimpse_size)).template cast<Scalar>();
} else {
// Initialize the glimpse with white noise: compute the mean and sigma
// of each channel, and use them to shape the gaussian.
DSizes<Index, 2> glimpse_size(width_, height_);
DSizes<Index, 2> input_size(input_width, input_height);
typedef typename internal::remove_const<
typename internal::traits<Input>::Scalar>::type Scalar;
for (int j = 0; j < num_channels; ++j) {
TensorFixedSize<Scalar, Sizes<> > mean;
mean.device(device) = input.template chip<3>(i).template chip<0>(j).template cast<float>().mean();
TensorFixedSize<float, Sizes<> > sigma;
sigma.device(device) =
(input.template chip<3>(i).template chip<0>(j).template cast<float>() - mean.reshape(Sizes<1,1>()).broadcast(input_size)).square().mean().sqrt();
TensorFixedSize<Scalar, Sizes<> > mini;
mini.device(device) = input.template chip<3>(i).template chip<0>(j).minimum();
TensorFixedSize<float, Sizes<> > maxi;
maxi.device(device) = input.template chip<3>(i).template chip<0>(j).maximum();
TensorMap<Tensor<float, 2> > tmp(NULL, glimpse_size);
output.template chip<3>(i).template chip<0>(j).device(device) =
(mean.reshape(Sizes<1,1>()).broadcast(glimpse_size) +
(tmp.random(internal::NormalRandomGenerator<float>()) * sigma.reshape(Sizes<1,1>()).broadcast(glimpse_size)).template cast<Scalar>()).cwiseMin(maxi.reshape(Sizes<1,1>()).broadcast(glimpse_size)).cwiseMax(mini.reshape(Sizes<1,1>()).broadcast(glimpse_size));
}
}
// Copy the part of the glimpse that cover the input image if any.
if (glimpse_width == 0 || glimpse_height == 0) {
continue;
}
output.template chip<3>(i).slice(base_offset, slice_extent).device(device) = input.template chip<3>(i).slice(slice_offset, slice_extent);
} else {
output.template chip<3>(i).device(device) = input.template chip<3>(i).slice(slice_offset, slice_extent);
}
}
}
private:
const Index width_;
const Index height_;
const std::vector<IndexPair<float> > offsets_;
const bool normalized_;
const bool centered_;
const bool uniform_noise_;
};
}
template <typename Input>
EIGEN_ALWAYS_INLINE
static const TensorCustomUnaryOp<const GlimpseExtractionOp<typename internal::traits<Input>::Index>, const Input>
ExtractGlimpses(const Input& input,
const typename internal::traits<Input>::Index width,
const typename internal::traits<Input>::Index height,
const std::vector<IndexPair<float> >& offsets,
const bool normalized = true, const bool centered = true,
const bool uniform_noise = true)
{
EIGEN_STATIC_ASSERT(internal::traits<Input>::Layout == ColMajor, YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT(internal::traits<Input>::NumDimensions == 4, YOU_MADE_A_PROGRAMMING_MISTAKE);
typedef typename internal::traits<Input>::Index Index;
const GlimpseExtractionOp<Index> op(width, height, offsets, normalized,
centered, uniform_noise);
return input.customOp(op);
}
} // end namespace Eigen
#endif // EIGEN_CXX11_NEURAL_NETWORKS_ATTENTION_H
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