216 lines
10 KiB
C++
216 lines
10 KiB
C++
/*
|
|
* Copyright (c) 2018-2020 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.
|
|
*/
|
|
#ifndef ARM_COMPUTE_TEST_ROIALIGNLAYER_FIXTURE
|
|
#define ARM_COMPUTE_TEST_ROIALIGNLAYER_FIXTURE
|
|
|
|
#include "arm_compute/core/TensorShape.h"
|
|
#include "arm_compute/core/Types.h"
|
|
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
|
|
#include "tests/AssetsLibrary.h"
|
|
#include "tests/Globals.h"
|
|
#include "tests/IAccessor.h"
|
|
#include "tests/framework/Asserts.h"
|
|
#include "tests/framework/Fixture.h"
|
|
#include "tests/validation/Helpers.h"
|
|
#include "tests/validation/reference/ROIAlignLayer.h"
|
|
|
|
namespace arm_compute
|
|
{
|
|
namespace test
|
|
{
|
|
namespace validation
|
|
{
|
|
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TRois>
|
|
class ROIAlignLayerGenericFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo)
|
|
{
|
|
_rois_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::QASYMM16 : data_type;
|
|
_target = compute_target(input_shape, data_type, data_layout, pool_info, rois_shape, qinfo, output_qinfo);
|
|
_reference = compute_reference(input_shape, data_type, pool_info, rois_shape, qinfo, output_qinfo);
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor)
|
|
{
|
|
library->fill_tensor_uniform(tensor, 0);
|
|
}
|
|
|
|
template <typename U>
|
|
void generate_rois(U &&rois, const TensorShape &shape, const ROIPoolingLayerInfo &pool_info, TensorShape rois_shape, DataLayout data_layout = DataLayout::NCHW)
|
|
{
|
|
const size_t values_per_roi = rois_shape.x();
|
|
const size_t num_rois = rois_shape.y();
|
|
|
|
std::mt19937 gen(library->seed());
|
|
TRois *rois_ptr = static_cast<TRois *>(rois.data());
|
|
|
|
const float pool_width = pool_info.pooled_width();
|
|
const float pool_height = pool_info.pooled_height();
|
|
const float roi_scale = pool_info.spatial_scale();
|
|
|
|
// Calculate distribution bounds
|
|
const auto scaled_width = static_cast<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)] / roi_scale) / pool_width);
|
|
const auto scaled_height = static_cast<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)] / roi_scale) / pool_height);
|
|
const auto min_width = static_cast<float>(pool_width / roi_scale);
|
|
const auto min_height = static_cast<float>(pool_height / roi_scale);
|
|
|
|
// Create distributions
|
|
std::uniform_int_distribution<int> dist_batch(0, shape[3] - 1);
|
|
std::uniform_int_distribution<> dist_x1(0, scaled_width);
|
|
std::uniform_int_distribution<> dist_y1(0, scaled_height);
|
|
std::uniform_int_distribution<> dist_w(min_width, std::max(float(min_width), (pool_width - 2) * scaled_width));
|
|
std::uniform_int_distribution<> dist_h(min_height, std::max(float(min_height), (pool_height - 2) * scaled_height));
|
|
|
|
for(unsigned int pw = 0; pw < num_rois; ++pw)
|
|
{
|
|
const auto batch_idx = dist_batch(gen);
|
|
const auto x1 = dist_x1(gen);
|
|
const auto y1 = dist_y1(gen);
|
|
const auto x2 = x1 + dist_w(gen);
|
|
const auto y2 = y1 + dist_h(gen);
|
|
|
|
rois_ptr[values_per_roi * pw] = batch_idx;
|
|
if(rois.data_type() == DataType::QASYMM16)
|
|
{
|
|
rois_ptr[values_per_roi * pw + 1] = quantize_qasymm16(static_cast<float>(x1), rois.quantization_info());
|
|
rois_ptr[values_per_roi * pw + 2] = quantize_qasymm16(static_cast<float>(y1), rois.quantization_info());
|
|
rois_ptr[values_per_roi * pw + 3] = quantize_qasymm16(static_cast<float>(x2), rois.quantization_info());
|
|
rois_ptr[values_per_roi * pw + 4] = quantize_qasymm16(static_cast<float>(y2), rois.quantization_info());
|
|
}
|
|
else
|
|
{
|
|
rois_ptr[values_per_roi * pw + 1] = static_cast<TRois>(x1);
|
|
rois_ptr[values_per_roi * pw + 2] = static_cast<TRois>(y1);
|
|
rois_ptr[values_per_roi * pw + 3] = static_cast<TRois>(x2);
|
|
rois_ptr[values_per_roi * pw + 4] = static_cast<TRois>(y2);
|
|
}
|
|
}
|
|
}
|
|
|
|
TensorType compute_target(TensorShape input_shape,
|
|
DataType data_type,
|
|
DataLayout data_layout,
|
|
const ROIPoolingLayerInfo &pool_info,
|
|
const TensorShape rois_shape,
|
|
const QuantizationInfo &qinfo,
|
|
const QuantizationInfo &output_qinfo)
|
|
{
|
|
if(data_layout == DataLayout::NHWC)
|
|
{
|
|
permute(input_shape, PermutationVector(2U, 0U, 1U));
|
|
}
|
|
|
|
const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo();
|
|
|
|
// Create tensors
|
|
TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, qinfo, data_layout);
|
|
TensorType rois_tensor = create_tensor<TensorType>(rois_shape, _rois_data_type, 1, rois_qinfo);
|
|
|
|
const TensorShape dst_shape = misc::shape_calculator::compute_roi_align_shape(*(src.info()), *(rois_tensor.info()), pool_info);
|
|
TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, output_qinfo, data_layout);
|
|
|
|
// Create and configure function
|
|
FunctionType roi_align_layer;
|
|
roi_align_layer.configure(&src, &rois_tensor, &dst, pool_info);
|
|
|
|
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(rois_tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
|
|
// Allocate tensors
|
|
src.allocator()->allocate();
|
|
rois_tensor.allocator()->allocate();
|
|
dst.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(!rois_tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
|
|
// Fill tensors
|
|
fill(AccessorType(src));
|
|
generate_rois(AccessorType(rois_tensor), input_shape, pool_info, rois_shape, data_layout);
|
|
|
|
// Compute function
|
|
roi_align_layer.run();
|
|
|
|
return dst;
|
|
}
|
|
|
|
SimpleTensor<T> compute_reference(const TensorShape &input_shape,
|
|
DataType data_type,
|
|
const ROIPoolingLayerInfo &pool_info,
|
|
const TensorShape rois_shape,
|
|
const QuantizationInfo &qinfo,
|
|
const QuantizationInfo &output_qinfo)
|
|
{
|
|
// Create reference tensor
|
|
SimpleTensor<T> src{ input_shape, data_type, 1, qinfo };
|
|
const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo();
|
|
SimpleTensor<TRois> rois_tensor{ rois_shape, _rois_data_type, 1, rois_qinfo };
|
|
|
|
// Fill reference tensor
|
|
fill(src);
|
|
generate_rois(rois_tensor, input_shape, pool_info, rois_shape);
|
|
|
|
return reference::roi_align_layer(src, rois_tensor, pool_info, output_qinfo);
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<T> _reference{};
|
|
DataType _rois_data_type{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TRois>
|
|
class ROIAlignLayerFixture : public ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T, TRois>
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout)
|
|
{
|
|
ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T, TRois>::setup(input_shape, pool_info, rois_shape, data_type, data_layout,
|
|
QuantizationInfo(), QuantizationInfo());
|
|
}
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TRois>
|
|
class ROIAlignLayerQuantizedFixture : public ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T, TRois>
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type,
|
|
DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo)
|
|
{
|
|
ROIAlignLayerGenericFixture<TensorType, AccessorType, FunctionType, T, TRois>::setup(input_shape, pool_info, rois_shape,
|
|
data_type, data_layout, qinfo, output_qinfo);
|
|
}
|
|
};
|
|
} // namespace validation
|
|
} // namespace test
|
|
} // namespace arm_compute
|
|
#endif /* ARM_COMPUTE_TEST_ROIALIGNLAYER_FIXTURE */
|