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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
* 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.
*/
#ifndef __NNFW_CKER_MAX_POOL_H__
#define __NNFW_CKER_MAX_POOL_H__
#include "cker/Shape.h"
#include "cker/Types.h"
#include "cker/Utils.h"
#include "cker/neon/neon_check.h"
#include "cker/eigen/Utils.h"
#include <Eigen/Core>
namespace nnfw
{
namespace cker
{
inline void MaxPool(const PoolParams ¶ms, const Shape &input_shape, const float *input_data,
const Shape &output_shape, float *output_data)
{
assert(input_shape.DimensionsCount() == 4);
assert(output_shape.DimensionsCount() == 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape);
auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape);
// 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 + params.padding_values.height;
int wpad = w + params.padding_values.width;
int h_start =
(hpad < params.filter_height) ? 0 : (hpad - params.filter_height) / stride_height + 1;
int h_end = std::min(hpad / stride_height + 1, output_height);
int w_start =
(wpad < params.filter_width) ? 0 : (wpad - params.filter_width) / 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)));
}
}
}
}
}
const int flat_size = output_shape.FlatSize();
for (int i = 0; i < flat_size; ++i)
{
output_data[i] = ActivationFunctionWithMinMax(output_data[i], params.float_activation_min,
params.float_activation_max);
}
}
inline void MaxPool(const PoolParams ¶ms, const Shape &input_shape, const uint8_t *input_data,
const Shape &output_shape, uint8_t *output_data)
{
// Here, and in other pooling ops, in order to maintain locality of reference,
// to minimize some recalculations, and to load into NEON vector registers, we
// use an inner loop down the depth. Since depths can be large and hence we
// would need arbitrarily large temporary storage, we divide the work up into
// depth tranches just within the batch loop.
static constexpr int kPoolingAccTrancheSize = 256;
assert(params.quantized_activation_min <= params.quantized_activation_max);
assert(input_shape.DimensionsCount() == 4);
assert(output_shape.DimensionsCount() == 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
uint8_t acc[kPoolingAccTrancheSize];
for (int batch = 0; batch < batches; ++batch)
{
// We proceed through the depth in tranches (see comment above). The
// depth_base is the depth at the beginning of the tranche. The
// tranche_depth is the depth dimension of the tranche.
for (int depth_base = 0; depth_base < depth; depth_base += kPoolingAccTrancheSize)
{
const int tranche_depth = std::min(depth - depth_base, kPoolingAccTrancheSize);
for (int out_y = 0; out_y < output_height; ++out_y)
{
for (int out_x = 0; out_x < output_width; ++out_x)
{
const int in_x_origin = (out_x * stride_width) - params.padding_values.width;
const int in_y_origin = (out_y * stride_height) - params.padding_values.height;
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end = std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end = std::min(params.filter_height, input_height - in_y_origin);
memset(acc, 0, tranche_depth * sizeof(acc[0]));
const uint8_t *input_ptr =
input_data + depth_base +
depth * (in_x_origin + input_width * (in_y_origin + input_height * batch));
for (int fy = filter_y_start; fy < filter_y_end; fy++)
{
const uint8_t *input_row_ptr = input_ptr + depth * (fy * input_width + filter_x_start);
for (int fx = filter_x_start; fx < filter_x_end; fx++)
{
const uint8_t *input_channel_ptr = input_row_ptr;
int channel = 0;
#ifdef USE_NEON
for (; channel <= tranche_depth - 16; channel += 16)
{
uint8x16_t acc_reg = vld1q_u8(acc + channel);
uint8x16_t input_reg = vld1q_u8(input_channel_ptr);
input_channel_ptr += 16;
acc_reg = vmaxq_u8(acc_reg, input_reg);
vst1q_u8(acc + channel, acc_reg);
}
for (; channel <= tranche_depth - 8; channel += 8)
{
uint8x8_t acc_reg = vld1_u8(acc + channel);
uint8x8_t input_reg = vld1_u8(input_channel_ptr);
input_channel_ptr += 8;
acc_reg = vmax_u8(acc_reg, input_reg);
vst1_u8(acc + channel, acc_reg);
}
#endif
for (; channel < tranche_depth; ++channel)
{
acc[channel] = std::max(acc[channel], *input_channel_ptr++);
}
input_row_ptr += depth;
}
}
uint8_t *output_ptr = output_data + Offset(output_shape, batch, out_y, out_x, depth_base);
int channel = 0;
#ifdef USE_NEON
for (; channel <= tranche_depth - 16; channel += 16)
{
uint8x16_t a = vld1q_u8(acc + channel);
a = vminq_u8(a, vdupq_n_u8(params.quantized_activation_max));
a = vmaxq_u8(a, vdupq_n_u8(params.quantized_activation_min));
vst1q_u8(output_ptr + channel, a);
}
for (; channel <= tranche_depth - 8; channel += 8)
{
uint8x8_t a = vld1_u8(acc + channel);
a = vmin_u8(a, vdup_n_u8(params.quantized_activation_max));
a = vmax_u8(a, vdup_n_u8(params.quantized_activation_min));
vst1_u8(output_ptr + channel, a);
}
#endif
for (; channel < tranche_depth; ++channel)
{
uint8_t a = acc[channel];
a = std::max<uint8_t>(a, params.quantized_activation_min);
a = std::min<uint8_t>(a, params.quantized_activation_max);
output_ptr[channel] = static_cast<uint8_t>(a);
}
}
}
}
}
}
} // namespace cker
} // namespace nnfw
#endif // __NNFW_CKER_MAX_POOL_H__
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