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/*
* Copyright (c) 2022 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "Pooling3dLayer.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
using namespace arm_compute::misc::shape_calculator;
template <typename T>
SimpleTensor<T> pooling_3d_layer_internal(const SimpleTensor<T> &src, const Pooling3dLayerInfo &pool3d_info, SimpleTensor<uint32_t> *indices)
{
TensorShape pooled_shape = compute_pool3d_shape(src.shape(), pool3d_info);
SimpleTensor<T> dst{ pooled_shape, src.data_type(), 1 };
if(indices != nullptr)
{
*indices = SimpleTensor<uint32_t> { pooled_shape, DataType::U32, 1 };
}
const int idx_channel = 0;
const int idx_width = 1;
const int idx_height = 2;
const int idx_depth = 3;
const int idx_batch = 4;
const int pool_size_width = pool3d_info.is_global_pooling ? src.shape()[idx_width] : pool3d_info.pool_size.width;
const int pool_size_height = pool3d_info.is_global_pooling ? src.shape()[idx_height] : pool3d_info.pool_size.height;
const int pool_size_depth = pool3d_info.is_global_pooling ? src.shape()[idx_depth] : pool3d_info.pool_size.depth;
const int pool_stride_width = static_cast<int>(pool3d_info.stride.width);
const int pool_stride_height = static_cast<int>(pool3d_info.stride.height);
const int pool_stride_depth = static_cast<int>(pool3d_info.stride.depth);
const int pad_left = static_cast<int>(pool3d_info.padding.left);
const int pad_top = static_cast<int>(pool3d_info.padding.top);
const int pad_front = static_cast<int>(pool3d_info.padding.front);
const int pad_right = static_cast<int>(pool3d_info.padding.right);
const int pad_bottom = static_cast<int>(pool3d_info.padding.bottom);
const int pad_back = static_cast<int>(pool3d_info.padding.back);
const int num_channels = static_cast<int>(src.shape()[idx_channel]);
const int num_batches = static_cast<int>(src.shape()[idx_batch]);
ARM_COMPUTE_ERROR_ON(num_channels != static_cast<int>(dst.shape()[idx_channel]));
ARM_COMPUTE_ERROR_ON(num_batches != static_cast<int>(dst.shape()[idx_batch]));
const int w_src = static_cast<int>(src.shape()[idx_width]);
const int h_src = static_cast<int>(src.shape()[idx_height]);
const int d_src = static_cast<int>(src.shape()[idx_depth]);
const int w_dst = static_cast<int>(dst.shape()[idx_width]);
const int h_dst = static_cast<int>(dst.shape()[idx_height]);
const int d_dst = static_cast<int>(dst.shape()[idx_depth]);
const bool exclude_padding = pool3d_info.exclude_padding;
const int height_stride_src = num_channels * w_src;
const int depth_stride_src = height_stride_src * h_src;
const int batch_stride_src = depth_stride_src * d_src;
const int height_stride_dst = num_channels * w_dst;
const int depth_stride_dst = height_stride_dst * h_dst;
const int batch_stride_dst = depth_stride_dst * d_dst;
for(int b = 0; b < num_batches; ++b)
{
const int batch_offset_dst = b * batch_stride_dst;
const int batch_offset_src = b * batch_stride_src;
for(int c = 0; c < num_channels; ++c)
{
for(int d = 0; d < d_dst; ++d)
{
const int depth_offset_dst = d * depth_stride_dst;
for(int h = 0; h < h_dst; ++h)
{
const int height_offset_dst = h * height_stride_dst;
for(int w = 0; w < w_dst; ++w)
{
int wstart = w * pool_stride_width - pad_left;
int hstart = h * pool_stride_height - pad_top;
int dstart = d * pool_stride_depth - pad_front;
int wend = std::min(wstart + pool_size_width, w_src + pad_right);
int hend = std::min(hstart + pool_size_height, h_src + pad_bottom);
int dend = std::min(dstart + pool_size_depth, d_src + pad_back);
// this may not be equal to pool_w * pool_h * pool_d because of
// DimensionRoundingType choice (CEIL)
int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
// limit [start, end) to [0, w_src)
wstart = std::max(wstart, 0);
hstart = std::max(hstart, 0);
dstart = std::max(dstart, 0);
wend = std::min(wend, w_src);
hend = std::min(hend, h_src);
dend = std::min(dend, d_src);
auto max_val = -std::numeric_limits<T>::infinity();
int max_index{ 0 };
T avg_val = static_cast<T>(0.f);
T l2_val = static_cast<T>(0.f);
if(exclude_padding)
{
pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
}
for(int z = dstart; z < dend; ++z)
{
const int depth_offset_src = z * depth_stride_src;
for(int y = hstart; y < hend; ++y)
{
const int height_offset_src = y * height_stride_src;
for(int x = wstart; x < wend; ++x)
{
const auto val = static_cast<T>(
src[batch_offset_src + depth_offset_src + height_offset_src + x * num_channels + c]);
if(val > max_val)
{
max_val = val;
max_index = coord2index(src.shape(), Coordinates(c, x, y, z, 0));
}
avg_val += val;
l2_val += val * val;
}
}
}
avg_val /= pool_size;
l2_val = static_cast<T>(std::sqrt(l2_val / pool_size));
int dst_index = batch_offset_dst + depth_offset_dst + height_offset_dst + w * num_channels + c;
switch(pool3d_info.pool_type)
{
case PoolingType::MAX:
dst[dst_index] = static_cast<T>(max_val);
break;
case PoolingType::AVG:
dst[dst_index] = static_cast<T>(avg_val);
break;
case PoolingType::L2:
dst[dst_index] = static_cast<T>(l2_val);
break;
default:
ARM_COMPUTE_ERROR("Pooling Type should be either MAX, AVG or L2");
}
if(indices != nullptr)
{
(*indices)[dst_index] = max_index;
}
}
}
}
}
}
return dst;
}
template SimpleTensor<float> pooling_3d_layer(const SimpleTensor<float> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices);
template SimpleTensor<half> pooling_3d_layer(const SimpleTensor<half> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices);
template <typename T>
SimpleTensor<T> pooling_3d_layer(const SimpleTensor<T> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices)
{
ARM_COMPUTE_UNUSED(output_qinfo);
return pooling_3d_layer_internal<T>(src, pool3d_info, indices);
}
template <>
SimpleTensor<int8_t> pooling_3d_layer<int8_t>(const SimpleTensor<int8_t> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices)
{
SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
SimpleTensor<float> dst_tmp = pooling_3d_layer_internal<float>(src_tmp, pool3d_info, indices);
return convert_to_asymmetric<int8_t>(dst_tmp, output_qinfo);
}
template <>
SimpleTensor<uint8_t> pooling_3d_layer<uint8_t>(const SimpleTensor<uint8_t> &src, const Pooling3dLayerInfo &pool3d_info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices)
{
SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
SimpleTensor<float> dst_tmp = pooling_3d_layer_internal<float>(src_tmp, pool3d_info, indices);
return convert_to_asymmetric<uint8_t>(dst_tmp, output_qinfo);
}
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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