/*
* Copyright (c) 2019-2020 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
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* of this software and associated documentation files (the "Software"), to
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* 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
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#include "arm_compute/graph.h"
#include "tests/NEON/Accessor.h"
#include "tests/validation/Validation.h"
#include "tests/validation/reference/DepthwiseConvolutionLayer.h"
#include "tests/validation/reference/Permute.h"
#include "utils/CommonGraphOptions.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
#include "ValidateExample.h"
#include "graph_validate_utils.h"
#include <utility>
using namespace arm_compute::utils;
using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
using namespace arm_compute::graph;
using namespace arm_compute;
using namespace arm_compute::test;
using namespace arm_compute::test::validation;
namespace
{
/** Depthwise Convolution command line options used to configure the graph examples
*
* (Similar to common options)
* The options in this object get populated when "parse()" is called on the parser used to construct it.
* The expected workflow is:
*
* CommandLineParser parser;
* CommonOptions options( parser );
* parser.parse(argc, argv);
*/
class DepthConvolutionOptions final : public CommonGraphValidateOptions
{
public:
explicit DepthConvolutionOptions(CommandLineParser &parser) noexcept
: CommonGraphValidateOptions(parser),
width(parser.add_option<SimpleOption<int>>("width", 9)),
height(parser.add_option<SimpleOption<int>>("height", 9)),
channels(parser.add_option<SimpleOption<int>>("channels", 1)),
batch(parser.add_option<SimpleOption<int>>("batch", 1)),
weights_width(parser.add_option<SimpleOption<int>>("weights_width", 3)),
weights_height(parser.add_option<SimpleOption<int>>("weights_height", 3)),
padding_top(parser.add_option<SimpleOption<int>>("padding_top", 0)),
padding_left(parser.add_option<SimpleOption<int>>("padding_left", 0)),
padding_bottom(parser.add_option<SimpleOption<int>>("padding_bottom", 0)),
padding_right(parser.add_option<SimpleOption<int>>("padding_right", 0)),
stride_x(parser.add_option<SimpleOption<int>>("stride_x", 1)),
stride_y(parser.add_option<SimpleOption<int>>("stride_y", 1)),
padding_mode(),
conv_mode(),
depth_multiplier(parser.add_option<SimpleOption<int>>("depth_multiplier", 1)),
data_layout(),
scale(parser.add_option<SimpleOption<float>>("scale", 1.0f)),
offset(parser.add_option<SimpleOption<int>>("offset", 0)),
weights_scale(parser.add_option<SimpleOption<float>>("weights_scale", 1.0f)),
weights_offset(parser.add_option<SimpleOption<int>>("weights_offset", 0)),
output_scale(parser.add_option<SimpleOption<float>>("output_scale", 1.0f)),
output_offset(parser.add_option<SimpleOption<int>>("output_offset", 0)),
input_range_low(parser.add_option<SimpleOption<uint64_t>>("input_range_low")),
input_range_high(parser.add_option<SimpleOption<uint64_t>>("input_range_high")),
weights_range_low(parser.add_option<SimpleOption<uint64_t>>("weights_range_low")),
weights_range_high(parser.add_option<SimpleOption<uint64_t>>("weights_range_high")),
input_npy(parser.add_option<SimpleOption<std::string>>("input_image")),
output_npy(parser.add_option<SimpleOption<std::string>>("reference_image")),
weights_npy(parser.add_option<SimpleOption<std::string>>("weights_npy")),
bias_npy(parser.add_option<SimpleOption<std::string>>("bias_image"))
{
const std::set<ConvolutionPaddingMode> available_padding_modes
{
ConvolutionPaddingMode::Valid,
ConvolutionPaddingMode::Same
};
const std::set<arm_compute::graph::DepthwiseConvolutionMethod> supported_convolution_methods
{
arm_compute::graph::DepthwiseConvolutionMethod::Default,
arm_compute::graph::DepthwiseConvolutionMethod::GEMV,
arm_compute::graph::DepthwiseConvolutionMethod::Optimized3x3,
};
const std::set<DataLayout> supported_data_layouts
{
DataLayout::NHWC,
DataLayout::NCHW,
};
padding_mode = parser.add_option<EnumOption<ConvolutionPaddingMode>>("padding_mode", available_padding_modes, ConvolutionPaddingMode::Valid);
conv_mode = parser.add_option<EnumOption<arm_compute::graph::DepthwiseConvolutionMethod>>("convolution_method", supported_convolution_methods,
arm_compute::graph::DepthwiseConvolutionMethod::Default);
data_layout = parser.add_option<EnumOption<DataLayout>>("layout", supported_data_layouts, DataLayout::NHWC);
padding_mode->set_help("Set padding mode");
width->set_help("Set Input dimension width");
height->set_help("Set Input dimension height");
channels->set_help("Set Input dimension channels");
batch->set_help("Set Input dimension batch");
weights_width->set_help("Set weights_dimensions width");
weights_height->set_help("Set weights_dimensions height");
padding_top->set_help("Set padding top");
padding_bottom->set_help("Set padding bottom");
padding_left->set_help("Set padding left");
padding_right->set_help("Set padding right");
stride_x->set_help("Set padding stride x");
stride_y->set_help("Set padding stride y");
conv_mode->set_help("Set convolution method");
data_layout->set_help("Data layout to use");
scale->set_help("Quantization scale from QASYMM8");
offset->set_help("Quantization offset from QASYMM8");
output_scale->set_help("Quantization scale from QASYMM8");
output_offset->set_help("Quantization offset from QASYMM8");
input_npy->set_help("Use input .npy instead");
output_npy->set_help("Use .npy as a reference");
input_range_low->set_help("Lower bound for input randomization range");
input_range_high->set_help("Lower bound for input randomization range");
weights_scale->set_help("Quantization scale from QASYMM8");
weights_offset->set_help("Quantization offset from QASYMM8");
weights_range_low->set_help("Lower bound for input randomization range");
weights_range_high->set_help("Lower bound for input randomization range");
depth_multiplier->set_help("Depth multiplier");
}
/** Fill out the supplied parameters with user supplied parameters
*
* @param[out] os Output stream.
* @param[in] common_params Example parameters to output
*
* @return None.
*/
void consume_parameters(ExampleParams &common_params)
{
common_params.input.width = width->value();
common_params.input.height = height->value();
common_params.input.fm = channels->value();
common_params.input.batch = batch->value();
common_params.input.quant_info = QuantizationInfo(scale->value(), offset->value());
common_params.input.npy = input_npy->value();
common_params.input.range_low = input_range_low->value();
common_params.input.range_high = input_range_high->value();
common_params.weights.width = weights_width->value();
common_params.weights.height = weights_height->value();
common_params.weights.npy = weights_npy->value();
common_params.weights.range_low = weights_range_low->value();
common_params.weights.range_high = weights_range_high->value();
common_params.weights.quant_info = QuantizationInfo(weights_scale->value(), weights_offset->value());
common_params.bias.npy = bias_npy->value();
common_params.output.quant_info = QuantizationInfo(output_scale->value(), output_offset->value());
common_params.output.npy = output_npy->value();
common_params.convolution.padding_mode = padding_mode->value();
common_params.convolution.padding_top = padding_top->value();
common_params.convolution.padding_bottom = padding_bottom->value();
common_params.convolution.padding_left = padding_left->value();
common_params.convolution.padding_right = padding_right->value();
common_params.convolution.padding_stride_x = stride_x->value();
common_params.convolution.padding_stride_y = stride_y->value();
common_params.convolution.depth_multiplier = depth_multiplier->value();
common_params.data_type = data_type->value();
common_params.data_layout = data_layout->value();
common_params.depth_convolution_method = conv_mode->value();
}
void print_parameters(::std::ostream &os, const ExampleParams &common_params) override
{
os << "Threads : " << common_params.common_params.threads << std::endl;
os << "Target : " << common_params.common_params.target << std::endl;
os << "Data type : " << common_params.data_type << std::endl;
os << "Input dimensions(X,Y, Channels, Batch) : (" << common_params.input.width << "," << common_params.input.height << "," << common_params.input.fm << "," << common_params.input.batch << ")"
<< std::endl;
os << "Weight dimensions(X,Y, Channels(same as input)) : (" << common_params.weights.width << "," << common_params.weights.height << "," << common_params.input.fm << ","
<< ")" << std::endl;
os << "Padding(top, bottom, left, right) (stride x, stride y) : (" << common_params.convolution.padding_top << "," << common_params.convolution.padding_bottom << "," <<
common_params.convolution.padding_left << "," << common_params.convolution.padding_right << ") (" << common_params.convolution.padding_stride_x << "," << common_params.convolution.padding_stride_y <<
")" << std::endl;
os << "Padding Mode: " << common_params.convolution.padding_mode << std::endl;
os << "Convolution Method: " << common_params.depth_convolution_method << std::endl;
os << "Depth multiplier: " << common_params.convolution.depth_multiplier;
}
/** Prevent instances of this class from being copied (As this class contains pointers) */
DepthConvolutionOptions(const DepthConvolutionOptions &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
DepthConvolutionOptions &operator=(const DepthConvolutionOptions &) = delete;
/** Allow instances of this class to be moved */
DepthConvolutionOptions(DepthConvolutionOptions &&) noexcept(true) = default;
/** Allow instances of this class to be moved */
DepthConvolutionOptions &operator=(DepthConvolutionOptions &&) noexcept(true) = default;
/** Default destructor */
~DepthConvolutionOptions() override = default;
private:
SimpleOption<int> *width; /**< Input width */
SimpleOption<int> *height; /**< Input height */
SimpleOption<int> *channels; /**< Input channels */
SimpleOption<int> *batch; /**< Input batch */
SimpleOption<int> *weights_width; /**< weights width */
SimpleOption<int> *weights_height; /**< weights height */
SimpleOption<int> *padding_top; /**< Padding top */
SimpleOption<int> *padding_left; /**< Padding left */
SimpleOption<int> *padding_bottom; /**< Padding bottom */
SimpleOption<int> *padding_right; /**< Padding right */
SimpleOption<int> *stride_x; /**< Padding stride x */
SimpleOption<int> *stride_y; /**< Padding stride y */
EnumOption<ConvolutionPaddingMode> *padding_mode; /**< Padding mode */
EnumOption<arm_compute::graph::DepthwiseConvolutionMethod> *conv_mode; /**< Convolution method */
SimpleOption<int> *depth_multiplier; /**< Depth multiplier */
EnumOption<arm_compute::DataLayout> *data_layout; /**< Graph data layout */
SimpleOption<float> *scale; /**< Input Quantization scale from QASYMM8 */
SimpleOption<int> *offset; /**< Input Quantization offset from QASYMM8 */
SimpleOption<float> *weights_scale; /**< Weights Quantization scale from QASYMM8 */
SimpleOption<int> *weights_offset; /**< Weights Quantization offset from QASYMM8 */
SimpleOption<float> *output_scale; /**< Output Quantization scale from QASYMM8 */
SimpleOption<int> *output_offset; /**< Output Quantization offset from QASYMM8 */
SimpleOption<uint64_t> *input_range_low; /**< Lower bound for input randomization range */
SimpleOption<uint64_t> *input_range_high; /**< Upper bound for input randomization range */
SimpleOption<uint64_t> *weights_range_low; /**< Lower bound for weights randomization range */
SimpleOption<uint64_t> *weights_range_high; /**< Upper bound for weights randomization range */
SimpleOption<std::string> *input_npy; /**< Use input .npy image */
SimpleOption<std::string> *output_npy; /**< Use output .npy image to verify*/
SimpleOption<std::string> *weights_npy; /**< Use weights .npy image */
SimpleOption<std::string> *bias_npy; /**< Use bias .npy image */
};
/** DepthwiseConvolutionLayer Graph example validation accessor class */
template <typename D>
class DepthConvolutionVerifyAccessor final : public VerifyAccessor<D>
{
public:
using BaseClassType = VerifyAccessor<D>;
using BaseClassType::BaseClassType;
using BaseClassType::_params;
using TBias = typename std::conditional<std::is_same<typename std::decay<D>::type, uint8_t>::value, int32_t, D>::type;
public:
SimpleTensor<D> reference(SimpleTensor<D> &src, SimpleTensor<D> &weights, SimpleTensor<TBias> &bias, const TensorShape &output_shape) override
{
// Calculate padding information
const PadStrideInfo padding_info = calculate_convolution_padding(_params);
//Calculate reference
return reference::depthwise_convolution<D>(src, weights, bias, output_shape, padding_info,
_params.convolution.depth_multiplier,
Size2D(1U, 1U),
_params.output.quant_info);
}
float relative_tolerance() override
{
const std::map<arm_compute::graph::Target, const std::map<DataType, float>> relative_tolerance
{
{
arm_compute::graph::Target::CL,
{ { DataType::F16, 0.01f },
{ DataType::F32, 0.01f },
{ DataType::QASYMM8, 0.0f }
}
},
{
arm_compute::graph::Target::NEON,
{ { DataType::F16, 0.01f },
{ DataType::F32, 0.01f },
{ DataType::QASYMM8, 1.0f }
}
}
};
return relative_tolerance.at(_params.common_params.target).at(_params.data_type);
}
float absolute_tolerance() override
{
const std::map<Target, const std::map<DataType, float>> absolute_tolerance
{
{
Target::CL,
{ { DataType::F16, 0.0f },
{ DataType::F32, 0.0000f },
{ DataType::QASYMM8, 0.0f }
}
},
{
Target::NEON,
{ { DataType::F16, 0.2f },
{ DataType::F32, 0.002f },
{ DataType::QASYMM8, 0.0f }
}
}
};
return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
}
float tolerance_number() override
{
const std::map<Target, const std::map<DataType, float>> absolute_tolerance
{
{
Target::CL,
{ { DataType::F16, 0.05f },
{ DataType::F32, 0.00f },
{ DataType::QASYMM8, 0.0f }
}
},
{
Target::NEON,
{ { DataType::F16, 0.05f },
{ DataType::F32, 0.0f },
{ DataType::QASYMM8, 0.0f }
}
}
};
return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
}
};
} // namespace
class GraphDepthwiseConvolutionValidateExample final : public GraphValidateExample<DepthwiseConvolutionLayer, DepthConvolutionOptions, DepthConvolutionVerifyAccessor>
{
using GraphValidateExample::graph;
public:
GraphDepthwiseConvolutionValidateExample()
: GraphValidateExample("DepthWiseConvolution Graph example")
{
}
DepthwiseConvolutionLayer GraphFunctionLayer(ExampleParams ¶ms) override
{
const PixelValue lower = PixelValue(params.input.range_low, params.data_type, params.input.quant_info);
const PixelValue upper = PixelValue(params.input.range_high, params.data_type, params.input.quant_info);
const PixelValue weights_lower = PixelValue(params.weights.range_low, params.data_type, params.weights.quant_info);
const PixelValue weights_upper = PixelValue(params.weights.range_high, params.data_type, params.weights.quant_info);
// Calculate padding information
const PadStrideInfo padding_info = calculate_convolution_padding(params);
return DepthwiseConvolutionLayer(params.weights.width, params.weights.height,
get_accessor(params.weights, weights_lower, weights_upper, 1),
get_accessor(params.bias, lower, upper, 2),
padding_info, params.convolution.depth_multiplier, params.weights.quant_info, params.output.quant_info);
}
};
/** Main program for Graph Depthwise Convolution test
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments ( Input dimensions [width, height, channels, batch]
* Weights dimensions [width, height, channels]
* Padding [top,bottom,left,right, Stride x, Stride y, mode [Valid / Same / Manual] )
* Convolution Method[ Default/GEMV/Optimized3x3]
* Verification[tolerance_number,absolute_tolerance,relative_tolerance] )
*
*/
int main(int argc, char **argv)
{
return arm_compute::utils::run_example<GraphDepthwiseConvolutionValidateExample>(argc, argv);
}