COMPMID-1492 Create tests/validate_examples/graph_depthwise_convolution
Add new validate graph example and unify common example code
Change-Id: Ibfd7ae2067ad805d6c82d953fe3febfbea961149
Signed-off-by: John Kesapides <john.kesapides@arm.com>
Reviewed-on: https://review.mlplatform.org/c/825
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/tests/validate_examples/graph_depthwiseconvolution.cpp b/tests/validate_examples/graph_depthwiseconvolution.cpp
new file mode 100644
index 0000000..cdad404
--- /dev/null
+++ b/tests/validate_examples/graph_depthwiseconvolution.cpp
@@ -0,0 +1,396 @@
+/*
+ * Copyright (c) 2019 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 "arm_compute/graph.h"
+
+#include "support/ToolchainSupport.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.scale = scale->value();
+ common_params.input.quant_info.offset = 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.scale = weights_scale->value();
+ common_params.weights.quant_info.offset = weights_offset->value();
+
+ common_params.bias.npy = bias_npy->value();
+
+ common_params.output.quant_info.scale = output_scale->value();
+ common_params.output.quant_info.offset = 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;
+
+ 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);
+}