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/*
* Copyright (c) 2017-2019 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 "DepthwiseConvolutionLayer.h"
#include "ConvolutionLayer.h"
#include "Utils.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/Utils.h"
#include "tests/validation/reference/UtilsQuantizedAsymm.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
/** Perform a depthwise convolution
*
* - Three dimensions tensors
* - Third dimention is number of channels
* - Depths of input tensor and filter are equals
* - Padding, stride and output shape "match"
*
*/
template <typename T, typename TB>
SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &biases, const TensorShape &dst_shape, const PadStrideInfo &conv_info,
unsigned int depth_multiplier, const Size2D &dilation, const QuantizationInfo &out_quant_info)
{
ARM_COMPUTE_UNUSED(out_quant_info);
SimpleTensor<T> dst{ dst_shape, src.data_type(), 1 };
// Compute reference
const int filter_width = weights.shape().x();
const int filter_height = weights.shape().y();
const int filter_plane = filter_width * filter_height;
const int input_width = src.shape().x();
const int input_height = src.shape().y();
const int input_depth = src.shape().z();
const int num_batches = src.shape().total_size() / (input_width * input_height * input_depth);
const int pad_left = conv_info.pad_left();
const int pad_top = conv_info.pad_top();
const int pad_right = conv_info.pad_right();
const int pad_bottom = conv_info.pad_bottom();
const float patch_width = (filter_width + (dilation.x() - 1) * (filter_width - 1));
const float patch_height = (filter_height + (dilation.y() - 1) * (filter_height - 1));
const int patch_half_width_floor = patch_width / 2;
const int patch_half_height_floor = patch_height / 2;
const auto patch_half_width_ceil = static_cast<int>(std::ceil(patch_width / 2));
const auto patch_half_height_ceil = static_cast<int>(std::ceil(patch_height / 2));
const int minimum_x = -pad_left + patch_half_width_floor;
const int minimum_y = -pad_top + patch_half_height_floor;
const int maximum_x = input_width + pad_left + pad_right - static_cast<int>(patch_width);
const int maximum_y = input_height + pad_top + pad_bottom - static_cast<int>(patch_height);
const T border_value(0);
int out_pos = 0;
for(int r = 0; r < num_batches; ++r)
{
for(int z = 0; z < input_depth; ++z)
{
for(unsigned int m = 0; m < depth_multiplier; ++m)
{
const int out_z = z * depth_multiplier + m;
for(int y = minimum_y; y <= minimum_y + maximum_y; y += conv_info.stride().second)
{
for(int x = minimum_x; x <= minimum_x + maximum_x; x += conv_info.stride().first)
{
Coordinates coords(static_cast<int>(x), static_cast<int>(y), static_cast<int>(z), static_cast<int>(r));
size_t filter_offset = filter_plane * out_z;
T val(0);
for(int j = y - patch_half_height_floor; j < y + patch_half_height_ceil; j += dilation.y())
{
for(int i = x - patch_half_width_floor; i < x + patch_half_width_ceil; i += dilation.x())
{
coords.set(0, i);
coords.set(1, j);
val += *(weights.data() + filter_offset) * tensor_elem_at(src, coords, BorderMode::CONSTANT, border_value);
++filter_offset;
}
}
dst[out_pos++] = saturate_cast<T>(val + *static_cast<const TB *>(biases(Coordinates(out_z))));
}
}
}
}
}
return dst;
}
template <>
SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &biases, const TensorShape &dst_shape,
const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, const QuantizationInfo &out_quant_info)
{
// if no explicit quantization has been set you the same as src
const QuantizationInfo &dst_qinfo = out_quant_info.uniform().empty() ? src.quantization_info() : out_quant_info;
SimpleTensor<uint8_t> dst{ dst_shape, src.data_type(), 1, dst_qinfo };
// Create reference
const int input_offset = -src.quantization_info().uniform().offset;
const float input_scale = src.quantization_info().uniform().scale;
const int weights_offset = -weights.quantization_info().uniform().offset;
const float weights_scale = weights.quantization_info().uniform().scale;
const int output_offset = dst_qinfo.uniform().offset;
const float output_scale = dst_qinfo.uniform().scale;
int output_multiplier = 0;
int output_shift = 0;
const float multiplier = input_scale * weights_scale / output_scale;
arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
// Compute reference
const int filter_width = weights.shape().x();
const int filter_height = weights.shape().y();
const int filter_plane = filter_width * filter_height;
const int input_width = src.shape().x();
const int input_height = src.shape().y();
const int input_depth = src.shape().z();
const int num_batches = src.shape().total_size() / (input_width * input_height * input_depth);
const int pad_left = conv_info.pad_left();
const int pad_top = conv_info.pad_top();
const int pad_right = conv_info.pad_right();
const int pad_bottom = conv_info.pad_bottom();
const float patch_width = (filter_width + (dilation.x() - 1) * (filter_width - 1));
const float patch_height = (filter_height + (dilation.y() - 1) * (filter_height - 1));
const int patch_half_width_floor = patch_width / 2;
const int patch_half_height_floor = patch_height / 2;
const auto patch_half_width_ceil = static_cast<int>(std::ceil(patch_width / 2));
const auto patch_half_height_ceil = static_cast<int>(std::ceil(patch_height / 2));
const int minimum_x = -pad_left + patch_half_width_floor;
const int minimum_y = -pad_top + patch_half_height_floor;
const int maximum_x = input_width + pad_left + pad_right - static_cast<int>(patch_width);
const int maximum_y = input_height + pad_top + pad_bottom - static_cast<int>(patch_height);
int out_pos = 0;
for(int r = 0; r < num_batches; ++r)
{
for(int z = 0; z < input_depth; ++z)
{
for(unsigned int m = 0; m < depth_multiplier; ++m)
{
const int out_z = z * depth_multiplier + m;
const int32_t bias_val = *static_cast<const int32_t *>(biases(Coordinates(out_z)));
for(int y = minimum_y; y <= minimum_y + maximum_y; y += conv_info.stride().second)
{
for(int x = minimum_x; x <= minimum_x + maximum_x; x += conv_info.stride().first)
{
Coordinates coords(x, y, z, r);
int filter_offset = filter_plane * out_z;
int32_t val = 0;
for(int j = y - patch_half_height_floor; j < y + patch_half_height_ceil; j += dilation.y())
{
for(int i = x - patch_half_width_floor; i < x + patch_half_width_ceil; i += dilation.x())
{
coords.set(0, i);
coords.set(1, j);
const auto in_val = tensor_elem_at<uint8_t>(src, coords, BorderMode::CONSTANT, -input_offset);
const uint8_t w_val = *(weights.data() + filter_offset);
val += (in_val + input_offset) * (w_val + weights_offset);
++filter_offset;
}
}
val += bias_val;
val = asymm_rounding_divide_by_pow2(asymm_int_mult(val, output_multiplier), output_shift);
val += output_offset;
val = std::max<int32_t>(val, 0);
val = std::min<int32_t>(val, 255);
// Store the result
dst[out_pos++] = val;
}
}
}
}
}
return dst;
}
template SimpleTensor<float> depthwise_convolution(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &biases, const TensorShape &dst_shape,
const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, const QuantizationInfo &out_quant_info);
template SimpleTensor<half> depthwise_convolution(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &biases, const TensorShape &dst_shape,
const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, const QuantizationInfo &out_quant_info);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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