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/*
* Copyright (c) 2017-2018 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_DEPTHWISE_CONVOLUTION_FIXTURE
#define ARM_COMPUTE_TEST_DEPTHWISE_CONVOLUTION_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/DepthwiseConvolutionLayer.h"
#include "utils/Utils.h"
#include <random>
namespace arm_compute
{
namespace test
{
namespace validation
{
using namespace arm_compute::misc::shape_calculator;
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DepthwiseConvolutionLayerValidationGenericFixture : public framework::Fixture
{
public:
using TBias = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value, int32_t, T>::type;
public:
template <typename...>
void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, unsigned int depth_multiplier, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout)
{
_quantization_info = quantization_info;
_data_type = data_type;
const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
TensorShape weights_shape(kernel_size.width, kernel_size.height);
const TensorInfo in_info(in_shape, 1, data_type);
const TensorInfo we_info(weights_shape, 1, data_type);
TensorShape out_shape = compute_depthwise_convolution_shape(in_info, we_info, pad_stride_info, depth_multiplier);
weights_shape.set(2, out_shape.z());
const TensorShape biases_shape(weights_shape[2]);
_target = compute_target(in_shape, weights_shape, biases_shape, out_shape, pad_stride_info, depth_multiplier, data_type, bias_data_type, quantization_info, data_layout);
_reference = compute_reference(in_shape, weights_shape, biases_shape, out_shape, pad_stride_info, depth_multiplier, data_type, bias_data_type, quantization_info);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
switch(tensor.data_type())
{
case DataType::QASYMM8:
{
std::uniform_int_distribution<uint8_t> distribution(0, 10);
library->fill(tensor, distribution, i);
break;
}
case DataType::F32:
case DataType::F16:
{
std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
library->fill(tensor, distribution, i);
break;
}
case DataType::S32:
{
std::uniform_int_distribution<int32_t> distribution(-100, 100);
library->fill(tensor, distribution, i);
break;
}
default:
library->fill_tensor_uniform(tensor, i);
}
}
TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, TensorShape biases_shape, TensorShape output_shape, PadStrideInfo &pad_stride_info, unsigned int depth_multiplier,
const DataType data_type, const DataType bias_data_type, const QuantizationInfo quantization_info, const DataLayout data_layout)
{
if(data_layout == DataLayout::NHWC)
{
permute(input_shape, PermutationVector(2U, 0U, 1U));
permute(weights_shape, PermutationVector(2U, 0U, 1U));
permute(output_shape, PermutationVector(2U, 0U, 1U));
}
// Create tensors
TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, quantization_info, data_layout);
TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, quantization_info, data_layout);
TensorType biases = create_tensor<TensorType>(biases_shape, bias_data_type, 1, quantization_info, data_layout);
TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, quantization_info, data_layout);
// Create Depthwise Convolution configure function
FunctionType dwc;
dwc.configure(&src, &weights, &biases, &dst, pad_stride_info, depth_multiplier);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(biases.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src.allocator()->allocate();
weights.allocator()->allocate();
biases.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!biases.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(src), 0);
fill(AccessorType(weights), 1);
fill(AccessorType(biases), 2);
// Compute function
dwc.run();
return dst;
}
SimpleTensor<T> compute_reference(const TensorShape &in_shape, const TensorShape &weights_shape, const TensorShape &biases_shape, const TensorShape &out_shape, const PadStrideInfo &pad_stride_info,
unsigned int depth_multiplier,
const DataType data_type, const DataType bias_data_type, const QuantizationInfo quantization_info)
{
SimpleTensor<T> src{ in_shape, data_type, 1, quantization_info };
SimpleTensor<T> weights{ weights_shape, data_type, 1, quantization_info };
SimpleTensor<TBias> biases{ biases_shape, bias_data_type, 1, quantization_info };
fill(src, 0);
fill(weights, 1);
fill(biases, 2);
return reference::depthwise_convolution(src, weights, biases, out_shape, pad_stride_info, depth_multiplier);
}
TensorType _target{};
SimpleTensor<T> _reference{};
DataType _data_type{};
QuantizationInfo _quantization_info{};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DepthwiseConvolutionLayerValidationFixture : public DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, unsigned int depth_multiplier, DataType data_type, DataLayout data_layout)
{
DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(in_shape, kernel_size, pad_stride_info, depth_multiplier,
data_type, QuantizationInfo(), data_layout);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DepthwiseConvolutionLayerValidationQuantizedFixture : public DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, unsigned int depth_multiplier, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout)
{
DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(in_shape, kernel_size, pad_stride_info, depth_multiplier,
data_type, quantization_info, data_layout);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_DEPTHWISE_CONVOLUTION_FIXTURE */
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