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/*
* Copyright (c) 2019-2020 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 "CropResize.h"
#include "Utils.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
namespace
{
SimpleTensor<float> scale_image(const SimpleTensor<float> &in, const TensorShape &out_shape, InterpolationPolicy policy, float extrapolation_value)
{
ARM_COMPUTE_ERROR_ON(in.data_layout() != DataLayout::NHWC);
SimpleTensor<float> out{ out_shape, DataType::F32, 1, QuantizationInfo(), DataLayout::NHWC };
// Compute the ratio between source width/height and destination width/height
const auto wr = static_cast<float>(in.shape()[1]) / static_cast<float>(out_shape[1]);
const auto hr = static_cast<float>(in.shape()[2]) / static_cast<float>(out_shape[2]);
const auto width = static_cast<int>(in.shape().y());
const auto height = static_cast<int>(in.shape().z());
Window win;
win.use_tensor_dimensions(out_shape);
execute_window_loop(win, [&](const Coordinates & out_id)
{
Coordinates in_id(out_id);
int idw = in_id.y();
int idh = in_id.z();
switch(policy)
{
case InterpolationPolicy::NEAREST_NEIGHBOR:
{
//Calculate the source coords without -0.5f is equivalent to round the x_scr/y_src coords
float x_src = std::floor(idw * wr);
float y_src = std::floor(idh * hr);
in_id.set(1, x_src);
in_id.set(2, y_src);
// If coordinates in range of tensor's width or height
if(is_valid_pixel_index(x_src, y_src, width, height, 0))
{
*reinterpret_cast<float *>(out(out_id)) = tensor_elem_at(in, in_id, BorderMode::CONSTANT, extrapolation_value);
}
else
{
*reinterpret_cast<float *>(out(out_id)) = extrapolation_value;
}
break;
}
case InterpolationPolicy::BILINEAR:
{
float x_src = idw * wr;
float y_src = idh * hr;
in_id.set(1, std::floor(x_src));
in_id.set(2, std::floor(y_src));
if(is_valid_pixel_index(x_src, y_src, width, height, 0))
{
const int id_w = in_id[1];
const int id_h = in_id[2];
const float dx = x_src - id_w;
const float dy = y_src - id_h;
const float dx_1 = 1.0f - dx;
const float dy_1 = 1.0f - dy;
in_id.set(1, id_w);
in_id.set(2, id_h);
const float tl = tensor_elem_at(in, in_id, BorderMode::CONSTANT, extrapolation_value);
in_id.set(1, id_w + 1);
in_id.set(2, id_h);
const float tr = tensor_elem_at(in, in_id, BorderMode::CONSTANT, extrapolation_value);
in_id.set(1, id_w);
in_id.set(2, id_h + 1);
const float bl = tensor_elem_at(in, in_id, BorderMode::CONSTANT, extrapolation_value);
in_id.set(1, id_w + 1);
in_id.set(2, id_h + 1);
const float br = tensor_elem_at(in, in_id, BorderMode::CONSTANT, extrapolation_value);
*reinterpret_cast<float *>(out(out_id)) = tl * (dx_1 * dy_1) + tr * (dx * dy_1) + bl * (dx_1 * dy) + br * (dx * dy);
}
else
{
*reinterpret_cast<float *>(out(out_id)) = extrapolation_value;
}
break;
}
default:
ARM_COMPUTE_ERROR("Unsupported interpolation mode");
}
});
return out;
}
template <typename T>
SimpleTensor<float> crop_image(const SimpleTensor<T> &src, Coordinates start, Coordinates end, int32_t batch_index, float extrapolation_value)
{
TensorShape out_shape(src.shape()[0], static_cast<uint32_t>(abs(end[0] - start[0])) + 1, static_cast<uint32_t>(abs(end[1] - start[1])) + 1);
SimpleTensor<float> out{ out_shape, DataType::F32, 1, QuantizationInfo(), DataLayout::NHWC };
Window win;
win.use_tensor_dimensions(out_shape);
execute_window_loop(win, [&](const Coordinates & id)
{
bool out_of_bounds = false;
Coordinates offset(id[0], 0, 0, batch_index);
for(uint32_t i = 1; i < 3; ++i)
{
offset.set(i, end[i - 1] < start[i - 1] ? start[i - 1] - id[i] : start[i - 1] + id[i]);
if(offset[i] < 0 || static_cast<uint32_t>(offset[i]) > src.shape()[i] - 1)
{
out_of_bounds = true;
break;
}
}
if(!out_of_bounds)
{
*reinterpret_cast<float *>(out(id)) = static_cast<float>(*reinterpret_cast<const T *>(src(offset)));
}
else
{
*reinterpret_cast<float *>(out(id)) = extrapolation_value;
}
});
return out;
}
} // namespace
template <typename T>
SimpleTensor<float> crop_and_resize(const SimpleTensor<T> &src, const SimpleTensor<float> &boxes, SimpleTensor<int32_t> box_ind,
Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value)
{
ARM_COMPUTE_ERROR_ON(src.shape().num_dimensions() > 4);
ARM_COMPUTE_ERROR_ON(src.data_layout() != DataLayout::NHWC);
const TensorShape out_shape(src.shape()[0], crop_size.x, crop_size.y, boxes.shape()[1]);
SimpleTensor<float> out{ out_shape, DataType::F32, 1, QuantizationInfo(), DataLayout::NHWC };
const TensorShape scaled_image_shape(src.shape()[0], crop_size.x, crop_size.y);
for(uint32_t i = 0; i < boxes.shape()[1]; ++i)
{
Coordinates start = Coordinates(std::floor((*reinterpret_cast<const float *>(boxes(Coordinates(1, i)))) * (src.shape()[1] - 1) + 0.5f),
std::floor((*reinterpret_cast<const float *>(boxes(Coordinates(0, i)))) * (src.shape()[2] - 1) + 0.5f));
Coordinates end = Coordinates(std::floor((*reinterpret_cast<const float *>(boxes(Coordinates(3, i)))) * (src.shape()[1] - 1) + 0.5f),
std::floor((*reinterpret_cast<const float *>(boxes(Coordinates(2, i)))) * (src.shape()[2] - 1) + 0.5f));
SimpleTensor<float> cropped = crop_image(src, start, end, box_ind[i], extrapolation_value);
SimpleTensor<float> scaled = scale_image(cropped, scaled_image_shape, method, extrapolation_value);
std::copy_n(reinterpret_cast<float *>(scaled.data()), scaled.num_elements(), reinterpret_cast<float *>(out(Coordinates(0, 0, 0, i))));
}
return out;
}
template SimpleTensor<float> crop_and_resize(const SimpleTensor<float> &src, const SimpleTensor<float> &boxes, SimpleTensor<int32_t> box_ind,
Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value);
template SimpleTensor<float> crop_and_resize(const SimpleTensor<uint16_t> &src, const SimpleTensor<float> &boxes, SimpleTensor<int32_t> box_ind,
Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value);
template SimpleTensor<float> crop_and_resize(const SimpleTensor<uint32_t> &src, const SimpleTensor<float> &boxes, SimpleTensor<int32_t> box_ind,
Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value);
template SimpleTensor<float> crop_and_resize(const SimpleTensor<int16_t> &src, const SimpleTensor<float> &boxes, SimpleTensor<int32_t> box_ind,
Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value);
template SimpleTensor<float> crop_and_resize(const SimpleTensor<int32_t> &src, const SimpleTensor<float> &boxes, SimpleTensor<int32_t> box_ind,
Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value);
template SimpleTensor<float> crop_and_resize(const SimpleTensor<half> &src, const SimpleTensor<float> &boxes, SimpleTensor<int32_t> box_ind,
Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value);
template SimpleTensor<float> crop_and_resize(const SimpleTensor<uint8_t> &src, const SimpleTensor<float> &boxes, SimpleTensor<int32_t> box_ind,
Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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