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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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