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/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
inline int NodeOffset(int b, int h, int w, int height, int width) {
return (b * height + h) * width + w;
}
inline void AveragePool(const float* input_data, const Dims<4>& input_dims,
int stride_width, int stride_height, int pad_width,
int pad_height, int kwidth, int kheight,
float* output_data,
const Dims<4>& output_dims,
bool include_pad) {
const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
const int input_height = ArraySize(input_dims, 2);
const int input_width = ArraySize(input_dims, 1);
const int output_height = ArraySize(output_dims, 2);
const int output_width = ArraySize(output_dims, 1);
MatchingArraySize(input_dims, 0, output_dims, 0);
// TODO(benoitjacob) make this a proper reference impl without Eigen!
const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims);
auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims);
// TODO(benoitjacob) get rid of the dynamic memory allocation here!
Eigen::VectorXf out_count(out_mat.cols());
out_count.setZero();
// Prefill the output to 0.
out_mat.setZero();
for (int b = 0; b < batches; ++b) {
for (int h = 0; h < input_height; ++h) {
for (int w = 0; w < input_width; ++w) {
// (h_start, h_end) * (w_start, w_end) is the range that the input
// vector projects to.
int hpad = h + pad_height;
int wpad = w + pad_width;
int h_start =
(hpad < kheight) ? 0 : (hpad - kheight) / stride_height + 1;
int h_end = std::min(hpad / stride_height + 1, output_height);
int w_start = (wpad < kwidth) ? 0 : (wpad - kwidth) / stride_width + 1;
int w_end = std::min(wpad / stride_width + 1, output_width);
// compute elementwise sum
for (int ph = h_start; ph < h_end; ++ph) {
for (int pw = w_start; pw < w_end; ++pw) {
int out_offset = NodeOffset(b, ph, pw, output_height, output_width);
out_mat.col(out_offset) +=
in_mat.col(NodeOffset(b, h, w, input_height, input_width));
out_count(out_offset)++;
}
}
}
}
}
// Divide the output by the actual number of elements being averaged over
TFLITE_DCHECK_GT(out_count.minCoeff(), 0);
if (include_pad) {
out_mat.array() /= kheight * kwidth;
} else {
out_mat.array().rowwise() /= out_count.transpose().array();
}
}
inline void MaxPool(const float* input_data, const Dims<4>& input_dims,
int stride_width, int stride_height, int pad_width,
int pad_height, int kwidth, int kheight,
float* output_data, const Dims<4>& output_dims) {
const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
const int input_height = ArraySize(input_dims, 2);
const int input_width = ArraySize(input_dims, 1);
const int output_height = ArraySize(output_dims, 2);
const int output_width = ArraySize(output_dims, 1);
MatchingArraySize(input_dims, 0, output_dims, 0);
const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims);
auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims);
// Prefill the output to minimum representable float value
out_mat.setConstant(std::numeric_limits<float>::lowest());
for (int b = 0; b < batches; ++b) {
for (int h = 0; h < input_height; ++h) {
for (int w = 0; w < input_width; ++w) {
// (h_start, h_end) * (w_start, w_end) is the range that the input
// vector projects to.
int hpad = h + pad_height;
int wpad = w + pad_width;
int h_start =
(hpad < kheight) ? 0 : (hpad - kheight) / stride_height + 1;
int h_end = std::min(hpad / stride_height + 1, output_height);
int w_start = (wpad < kwidth) ? 0 : (wpad - kwidth) / stride_width + 1;
int w_end = std::min(wpad / stride_width + 1, output_width);
// compute elementwise sum
for (int ph = h_start; ph < h_end; ++ph) {
for (int pw = w_start; pw < w_end; ++pw) {
int out_offset = NodeOffset(b, ph, pw, output_height, output_width);
out_mat.col(out_offset) =
out_mat.col(out_offset)
.cwiseMax(in_mat.col(
NodeOffset(b, h, w, input_height, input_width)));
}
}
}
}
}
}
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