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/*
* Copyright (c) 2017 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 "ConvolutionLayer.h"
#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
template <typename T>
SimpleTensor<T> deconvolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &output_shape,
const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &a)
{
// Create reference
TensorShape scaled_shape = src.shape();
scaled_shape.set(0, output_shape.x());
scaled_shape.set(1, output_shape.y());
SimpleTensor<T> scaled{ scaled_shape, src.data_type(), 1, src.fixed_point_position() };
const int width_in = src.shape().x();
const int height_in = src.shape().y();
const int width_scaled = scaled.shape().x();
const int height_scaled = scaled.shape().y();
const int num_2d_slices = src.shape().total_size() / (width_in * height_in);
const float width_ratio = static_cast<float>(width_in) / static_cast<float>(width_scaled);
const float height_ratio = static_cast<float>(height_in) / static_cast<float>(height_scaled);
const int ax = a.first; // The number of zeros added to right edge of the input.
const int ay = a.second; // The number of zeros added to bottom edge of the input.
const unsigned int kernel_size = weights.shape().x();
ARM_COMPUTE_ERROR_ON(info.pad().first > (kernel_size - 1));
const int transposed_convolution_padx = kernel_size - info.pad().first - 1;
const int transposed_convolution_pady = kernel_size - info.pad().second - 1;
const int stridex = info.stride().first;
const int stridey = info.stride().second;
for(int j = 0; j < scaled.num_elements(); ++j)
{
scaled[j] = T(0);
}
for(int slice = 0; slice < num_2d_slices; ++slice)
{
const int offset_slice_in = slice * width_in * height_in;
const int offset_slice_out = slice * width_scaled * height_scaled;
for(int yi = ay; yi < height_scaled; yi += stridey)
{
for(int xi = transposed_convolution_padx; xi < width_scaled; xi += stridex)
{
const float x_src = (xi + 0.5f) * width_ratio - 0.5f;
const float y_src = (yi + 0.5f) * height_ratio - 0.5f;
T *out = scaled.data() + offset_slice_out + xi + yi * width_scaled;
const bool in_bounds = x_src > -1 && y_src > -1 && x_src < width_in && y_src < height_in;
const bool in_axy = xi < transposed_convolution_padx || xi >= (width_scaled - ax) // this is checking if the x coordinate is in the padded left/right area
|| yi < ay || yi >= (height_scaled - transposed_convolution_pady); // like above but top and bottom padding in the upscaled XY plane
if(!in_axy)
{
if(in_bounds)
{
const int in_scaled_x = (x_src < 0.f) ? static_cast<int>(x_src - 0.5f) : static_cast<int>(x_src + 0.5f);
const int in_scaled_y = (y_src < 0.f) ? static_cast<int>(y_src - 0.5f) : static_cast<int>(y_src + 0.5f);
const T *in = src.data() + offset_slice_in + in_scaled_x + in_scaled_y * width_in;
*out = *in;
}
else
{
*out = T(0);
}
}
}
}
}
const PadStrideInfo conv_info(1, 1, 1, 1, DimensionRoundingType::CEIL);
return convolution_layer(scaled, weights, bias, output_shape, conv_info);
}
template SimpleTensor<float> deconvolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape,
const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &a);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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