COMPMID-1493 Create tests/validate_examples/graph_fully_connected
Add graph example with validation for fully-connected layer
Change-Id: I06fcc670b7097609f04eb040fedf56108c9484d2
Signed-off-by: John Kesapides <john.kesapides@arm.com>
Reviewed-on: https://review.mlplatform.org/c/764
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez <pablo.tello@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validate_examples/graph_fully_connected.cpp b/tests/validate_examples/graph_fully_connected.cpp
new file mode 100644
index 0000000..e4f5117
--- /dev/null
+++ b/tests/validate_examples/graph_fully_connected.cpp
@@ -0,0 +1,596 @@
+/*
+ * 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/FullyConnectedLayer.h"
+#include "tests/validation/reference/Permute.h"
+
+#include "utils/CommonGraphOptions.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+#include "ValidateExample.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
+{
+/** Structure holding all the input tensor graph parameters */
+struct TensorParams
+{
+ int width{ 1 };
+ int height{ 1 };
+ int fm{ 1 };
+ int batch{ 1 };
+ QuantizationInfo quant_info{ 1.0f, 0 };
+ uint64_t range_low{ 0 };
+ uint64_t range_high{ 16 };
+};
+/** Structure holding all the verification graph parameters */
+struct VerificationParams
+{
+ float absolute_tolerance{ -1.f };
+ float relative_tolerance{ -1.f };
+ float tolerance_number{ -1.f };
+};
+
+/** Structure holding all the common graph parameters */
+struct FrameworkParams
+{
+ bool help{ false };
+ int threads{ 0 };
+ arm_compute::graph::Target target{ arm_compute::graph::Target::NEON };
+};
+
+/** Structure holding all the fully_connected layer graph parameters */
+struct FullyConnectedParams
+{
+ arm_compute::DataType data_type{ DataType::F32 };
+ arm_compute::DataLayout data_layout{ DataLayout::NCHW };
+ FullyConnectedLayerInfo info{};
+ int num_outputs{ 1 };
+};
+
+/** Structure holding all the graph Example parameters */
+struct ExampleParams
+{
+ FrameworkParams common_params{};
+ TensorParams input{};
+ TensorParams weights{};
+ TensorParams output{};
+ VerificationParams verification{};
+ FullyConnectedParams fully_connected{};
+};
+
+/** Formatted output of the fully_connectedParams type
+ *
+ * @param[out] os Output stream.
+ * @param[in] common_params fully_connected parameters to output
+ *
+ * @return Modified output stream.
+ */
+::std::ostream &operator<<(::std::ostream &os, const ExampleParams &common_params)
+{
+ std::string false_str = std::string("false");
+ std::string true_str = std::string("true");
+
+ os << "Threads : " << common_params.common_params.threads << std::endl;
+ os << "Target : " << common_params.common_params.target << std::endl;
+ os << "Data type : " << common_params.fully_connected.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 << "Number of outputs : " << common_params.fully_connected.num_outputs << std::endl;
+ return os;
+}
+
+/** fully_connected 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 FullyConnectedOptions final
+{
+public:
+ explicit FullyConnectedOptions(CommandLineParser &parser) noexcept
+ : width(parser.add_option<SimpleOption<int>>("width", 3)),
+ batch(parser.add_option<SimpleOption<int>>("batch", 1)),
+ help(parser.add_option<ToggleOption>("help")),
+ threads(parser.add_option<SimpleOption<int>>("threads")),
+ target(),
+ data_type(),
+ absolute_tolerance(parser.add_option<SimpleOption<float>>("abs_tolerance", -1.0f)),
+ relative_tolerance(parser.add_option<SimpleOption<float>>("rel_tolerance", -1.0f)),
+ tolerance_number(parser.add_option<SimpleOption<float>>("tolerance_num", -1.0f)),
+ input_scale(parser.add_option<SimpleOption<float>>("input_scale", 1.0f)),
+ input_offset(parser.add_option<SimpleOption<int>>("input_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)),
+ num_outputs(parser.add_option<SimpleOption<int>>("num_outputs", 1)),
+ 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"))
+ {
+ const std::set<arm_compute::graph::Target> supported_targets
+ {
+ Target::NEON,
+ Target::CL,
+ Target::GC,
+ };
+
+ const std::set<arm_compute::DataType> supported_data_types
+ {
+ DataType::F16,
+ DataType::F32,
+ DataType::QASYMM8,
+ };
+
+ target = parser.add_option<EnumOption<Target>>("target", supported_targets, Target::NEON);
+ data_type = parser.add_option<EnumOption<DataType>>("type", supported_data_types, DataType::F32);
+
+ target->set_help("Target to execute on");
+ data_type->set_help("Data type to use");
+ help->set_help("Show this help message");
+ width->set_help("Set Input dimension width");
+ batch->set_help("Set Input dimension batch");
+ absolute_tolerance->set_help("Absolute tolerance used for verification");
+ relative_tolerance->set_help("Absolute tolerance used for verification");
+ tolerance_number->set_help("Absolute tolerance used for verification");
+ input_scale->set_help("Quantization scale from QASYMM8");
+ input_offset->set_help("Quantization offset from QASYMM8");
+ weights_scale->set_help("Quantization scale from QASYMM8");
+ weights_offset->set_help("Quantization offset from QASYMM8");
+ output_scale->set_help("Quantization scale from QASYMM8");
+ output_offset->set_help("Quantization offset from QASYMM8");
+ num_outputs->set_help("Number of outputs.");
+ input_range_low->set_help("Lower bound for input randomization range");
+ input_range_high->set_help("Lower bound for input randomization range");
+ weights_range_low->set_help("Lower bound for input randomization range");
+ weights_range_high->set_help("Lower bound for input randomization range");
+ }
+
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ FullyConnectedOptions(const FullyConnectedOptions &) = delete;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ FullyConnectedOptions &operator=(const FullyConnectedOptions &) = delete;
+ /** Allow instances of this class to be moved */
+ FullyConnectedOptions(FullyConnectedOptions &&) noexcept(true) = default;
+ /** Allow instances of this class to be moved */
+ FullyConnectedOptions &operator=(FullyConnectedOptions &&) noexcept(true) = default;
+ /** Default destructor */
+ ~FullyConnectedOptions() = default;
+
+ SimpleOption<int> *width; /**< Input width */
+ SimpleOption<int> *batch; /**< Input batch */
+ ToggleOption *help; /**< show help message */
+ SimpleOption<int> *threads; /**< Number of threads option */
+ EnumOption<arm_compute::graph::Target> *target; /**< Graph execution target */
+ EnumOption<arm_compute::DataType> *data_type; /**< Graph data type */
+ SimpleOption<float> *absolute_tolerance; /**< Absolute tolerance used in verification */
+ SimpleOption<float> *relative_tolerance; /**< Relative tolerance used in verification */
+ SimpleOption<float> *tolerance_number; /**< Tolerance number used in verification */
+ SimpleOption<float> *input_scale; /**< Input Quantization scale from QASSYMM8 */
+ SimpleOption<int> *input_offset; /**< Input Quantization offset from QASSYMM8 */
+ SimpleOption<float> *weights_scale; /**< Weights Quantization scale from QASSYMM8 */
+ SimpleOption<int> *weights_offset; /**< Weights Quantization offset from QASSYMM8 */
+ SimpleOption<float> *output_scale; /**< Output Quantization scale from QASSYMM8 */
+ SimpleOption<int> *output_offset; /**< Output Quantization offset from QASSYMM8 */
+ SimpleOption<int> *num_outputs; /**< Number of outputs. */
+ 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 */
+};
+
+/** Consumes the fully_connected graph options and creates a structure containing any information
+ *
+ * @param[in] options Options to consume
+ *
+ * @return fully_connectedparams structure containing the common graph parameters
+ */
+ExampleParams consume_fully_connected_graph_parameters(FullyConnectedOptions &options)
+{
+ ExampleParams common_params;
+
+ common_params.common_params.help = options.help->is_set() ? options.help->value() : false;
+ common_params.common_params.threads = options.threads->value();
+ common_params.common_params.target = options.target->value();
+
+ common_params.input.width = options.width->value();
+ common_params.input.batch = options.batch->value();
+ common_params.input.quant_info.scale = options.input_scale->value();
+ common_params.input.quant_info.offset = options.input_offset->value();
+ common_params.input.range_low = options.input_range_low->value();
+ common_params.input.range_high = options.input_range_high->value();
+
+ common_params.weights.quant_info.scale = options.weights_scale->value();
+ common_params.weights.quant_info.offset = options.weights_offset->value();
+ common_params.weights.range_low = options.weights_range_low->value();
+ common_params.weights.range_high = options.weights_range_high->value();
+
+ common_params.output.quant_info.scale = options.output_scale->value();
+ common_params.output.quant_info.offset = options.output_offset->value();
+
+ common_params.fully_connected.data_type = options.data_type->value();
+ common_params.fully_connected.num_outputs = options.num_outputs->value();
+
+ common_params.verification.absolute_tolerance = options.absolute_tolerance->value();
+ common_params.verification.relative_tolerance = options.relative_tolerance->value();
+ common_params.verification.tolerance_number = options.tolerance_number->value();
+
+ return common_params;
+}
+
+/** fully_connectedLayer Graph example validation accessor class */
+template <typename D>
+class FullyConnectedVerifyAccessor final : public graph::ITensorAccessor
+{
+public:
+ using TBias = typename std::conditional<std::is_same<typename std::decay<D>::type, uint8_t>::value, int32_t, D>::type;
+
+ /** Constructor
+ *
+ * @param[in] params fully_connected parameters
+ */
+ explicit FullyConnectedVerifyAccessor(ExampleParams ¶ms)
+ : _params(params)
+ {
+ }
+
+ // Inherited methods overridden:
+ bool access_tensor(ITensor &tensor) override
+ {
+ const RelativeTolerance<float> rel_tolerance(relative_tolenace(_params.verification.relative_tolerance)); /**< Relative tolerance */
+ const AbsoluteTolerance<float> abs_tolerance(absolute_tolerance(_params.verification.absolute_tolerance)); /**< Absolute tolerance */
+ const float tolerance_num(tolerance_number(_params.verification.tolerance_number)); /**< Tolerance number */
+
+ // Calculate Tensor shapes for verification
+ const TensorShape input_shape = TensorShape(_params.input.width, _params.input.height, _params.input.fm, _params.input.batch);
+ const TensorDescriptor input_descriptor = TensorDescriptor(input_shape, _params.fully_connected.data_type, _params.input.quant_info);
+ const TensorDescriptor weights_descriptor = FullyConnectedLayerNode::compute_weights_descriptor(input_descriptor,
+ _params.fully_connected.num_outputs,
+ _params.fully_connected.info,
+ _params.weights.quant_info);
+ const TensorDescriptor output_desciptor = FullyConnectedLayerNode::compute_output_descriptor(input_descriptor, _params.fully_connected.num_outputs, _params.output.quant_info);
+
+ //Create Input tensors
+ SimpleTensor<D> src{ input_descriptor.shape, _params.fully_connected.data_type, 1, input_descriptor.quant_info };
+ SimpleTensor<D> weights{ weights_descriptor.shape, _params.fully_connected.data_type, 1, weights_descriptor.quant_info };
+ SimpleTensor<TBias> bias{ TensorShape(tensor.info()->tensor_shape().x()), _params.fully_connected.data_type, 1, _params.input.quant_info };
+
+ //Fill the tensors with random values
+ fill_tensor<D>(src, 0, static_cast<D>(_params.input.range_low), static_cast<D>(_params.input.range_high));
+ fill_tensor<D>(weights, 1, static_cast<D>(_params.weights.range_low), static_cast<D>(_params.weights.range_high));
+ fill_tensor<TBias>(bias, 2, static_cast<TBias>(_params.input.range_low), static_cast<TBias>(_params.input.range_high));
+
+ //Calculate reference
+ SimpleTensor<D> output = reference::fully_connected_layer<D>(src, weights, bias, output_desciptor.shape, _params.output.quant_info);
+
+ arm_compute::test::validation::validate(Accessor(tensor), output, rel_tolerance, tolerance_num, abs_tolerance);
+
+ return false;
+ }
+
+private:
+ /** Fill tensor with Random values.
+ *
+ * Validate the given tensor against the reference result.
+ *
+ * @param[out] tensor The tensor we want to file
+ * @param[in] seed seed for the randomization function
+ * @param[in] low lower bound for random values
+ * @param[in] high upper bound for random values
+ *
+ * @return None.
+ */
+ template <typename T>
+ void fill_tensor(arm_compute::test::SimpleTensor<T> &tensor, std::random_device::result_type seed, T low, T high)
+ {
+ std::mt19937 gen(seed);
+ switch(tensor.data_type())
+ {
+ case arm_compute::DataType::QASYMM8:
+ {
+ const uint8_t qasymm8_low = tensor.quantization_info().quantize(low, RoundingPolicy::TO_NEAREST_UP);
+ const uint8_t qasymm8_high = tensor.quantization_info().quantize(high, RoundingPolicy::TO_NEAREST_UP);
+
+ std::uniform_int_distribution<uint8_t> distribution(qasymm8_low, qasymm8_high);
+
+ for(int i = 0; i < tensor.num_elements(); ++i)
+ {
+ tensor[i] = tensor.quantization_info().quantize(distribution(gen), RoundingPolicy::TO_NEAREST_UP);
+ }
+
+ break;
+ }
+ case arm_compute::DataType::S32:
+ {
+ std::uniform_int_distribution<int32_t> distribution(static_cast<int32_t>(low), static_cast<uint32_t>(high));
+
+ for(int i = 0; i < tensor.num_elements(); ++i)
+ {
+ tensor[i] = distribution(gen);
+ }
+
+ break;
+ }
+
+ case arm_compute::DataType::F16:
+ {
+ std::uniform_real_distribution<float> distribution(static_cast<half>(low), static_cast<half>(high));
+
+ for(int i = 0; i < tensor.num_elements(); ++i)
+ {
+ tensor[i] = static_cast<half>(distribution(gen));
+ }
+ break;
+ }
+ case arm_compute::DataType::F32:
+ {
+ std::uniform_real_distribution<float> distribution(static_cast<float>(low), static_cast<float>(high));
+
+ for(int i = 0; i < tensor.num_elements(); ++i)
+ {
+ tensor[i] = distribution(gen);
+ }
+
+ break;
+ }
+ default:
+ ARM_COMPUTE_ERROR("NOT SUPPORTED!");
+ }
+ }
+ /** Select relative tolerance.
+ *
+ * Select relative tolerance if not supplied by user.
+ *
+ * @param[in] user_value supplied relative tolerance. -1 designates no user input
+ *
+ * @return Appropriate relative tolerance.
+ */
+ float relative_tolenace(float user_value)
+ {
+ const std::map<arm_compute::graph::Target, const std::map<DataType, float>> relative_tolerance
+ {
+ {
+ arm_compute::graph::Target::CL,
+ { { DataType::F16, 0.2f },
+ { DataType::F32, 0.05f },
+ { DataType::QASYMM8, 1.0f }
+ }
+ },
+ {
+ arm_compute::graph::Target::NEON,
+ { { DataType::F16, 0.2f },
+ { DataType::F32, 0.01f },
+ { DataType::QASYMM8, 1.0f }
+ }
+ }
+ };
+ if(user_value == -1)
+ {
+ return relative_tolerance.at(_params.common_params.target).at(_params.fully_connected.data_type);
+ }
+
+ return user_value;
+ }
+
+ /** Select absolute tolerance.
+ *
+ * Select absolute tolerance if not supplied by user.
+ *
+ * @param[in] user_value supplied absolute tolerance. -1 designates no user input
+ *
+ * @return Appropriate absolute tolerance.
+ */
+ float absolute_tolerance(float user_value)
+ {
+ const std::map<Target, const std::map<DataType, float>> absolute_tolerance
+ {
+ {
+ Target::CL,
+ { { DataType::F16, 0.0f },
+ { DataType::F32, 0.0001f },
+ { DataType::QASYMM8, 1.0f }
+ }
+ },
+ {
+ Target::NEON,
+ { { DataType::F16, 0.3f },
+ { DataType::F32, 0.1f },
+ { DataType::QASYMM8, 1.0f }
+ }
+ }
+ };
+
+ if(user_value == -1)
+ {
+ return absolute_tolerance.at(_params.common_params.target).at(_params.fully_connected.data_type);
+ }
+ return user_value;
+ }
+ /** Select tolerance number.
+ *
+ * Select tolerance number if not supplied by user.
+ *
+ * @param[in] user_value supplied tolerance number. -1 designates no user input
+ *
+ * @return Appropriate tolerance number.
+ */
+ float tolerance_number(float user_value)
+ {
+ const std::map<Target, const std::map<DataType, float>> absolute_tolerance
+ {
+ {
+ Target::CL,
+ { { DataType::F16, 0.07f },
+ { DataType::F32, 0.07f },
+ { DataType::QASYMM8, 0.0f }
+ }
+ },
+ {
+ Target::NEON,
+ { { DataType::F16, 0.07f },
+ { DataType::F32, 0.0f },
+ { DataType::QASYMM8, 0.0f }
+ }
+ }
+ };
+
+ if(user_value == -1)
+ {
+ return absolute_tolerance.at(_params.common_params.target).at(_params.fully_connected.data_type);
+ }
+ return user_value;
+ }
+
+ ExampleParams _params;
+};
+
+/** Generates appropriate fully_connected verify accessor
+ *
+ * @param[in] params User supplied parameters for fully_connected.
+ *
+ * @return A fully_connected verify accessor for the requested datatype.
+ */
+inline std::unique_ptr<graph::ITensorAccessor> get_fully_connected_verify_accessor(ExampleParams params)
+{
+ switch(params.fully_connected.data_type)
+ {
+ case DataType::QASYMM8:
+ {
+ return arm_compute::support::cpp14::make_unique<FullyConnectedVerifyAccessor<uint8_t>>(
+ params);
+ }
+ case DataType::F16:
+ {
+ return arm_compute::support::cpp14::make_unique<FullyConnectedVerifyAccessor<half>>(
+ params);
+ }
+ case DataType::F32:
+ {
+ return arm_compute::support::cpp14::make_unique<FullyConnectedVerifyAccessor<float>>(
+ params);
+ }
+ default:
+ ARM_COMPUTE_ERROR("NOT SUPPORTED!");
+ }
+}
+
+} // namespace
+
+class Graphfully_connectedValidateExample final : public ValidateExample
+{
+public:
+ Graphfully_connectedValidateExample()
+ : graph(0, "fully_connected Graph example")
+ {
+ }
+ bool do_setup(int argc, char **argv) override
+ {
+ CommandLineParser parser;
+
+ FullyConnectedOptions Options(parser);
+
+ parser.parse(argc, argv);
+
+ ExampleParams params = consume_fully_connected_graph_parameters(Options);
+
+ if(params.common_params.help)
+ {
+ parser.print_help(argv[0]);
+ return false;
+ }
+
+ std::cout << params << std::endl;
+
+ // Create input descriptor
+ const TensorShape input_shape = TensorShape(params.input.width, params.input.height, params.input.fm, params.input.batch);
+ const TensorDescriptor input_descriptor = TensorDescriptor(input_shape, params.fully_connected.data_type, params.input.quant_info, params.fully_connected.data_layout);
+
+ const PixelValue lower = PixelValue(params.input.range_low, params.fully_connected.data_type, params.input.quant_info);
+ const PixelValue upper = PixelValue(params.input.range_high, params.fully_connected.data_type, params.input.quant_info);
+
+ const PixelValue weights_lower = PixelValue(params.weights.range_low, params.fully_connected.data_type, params.weights.quant_info);
+ const PixelValue weights_upper = PixelValue(params.weights.range_high, params.fully_connected.data_type, params.weights.quant_info);
+
+ graph << params.common_params.target
+ << InputLayer(input_descriptor, get_random_accessor(lower, upper, 0))
+ << FullyConnectedLayer(params.fully_connected.num_outputs,
+ get_random_accessor(weights_lower, weights_upper, 1),
+ get_random_accessor(lower, upper, 2),
+ params.fully_connected.info, params.weights.quant_info, params.output.quant_info)
+ << OutputLayer(get_fully_connected_verify_accessor(params));
+
+ GraphConfig config;
+ config.num_threads = params.common_params.threads;
+
+ graph.finalize(params.common_params.target, config);
+
+ return true;
+ }
+
+ void do_run() override
+ {
+ graph.run();
+ }
+
+ void do_teardown() override
+ {
+ }
+
+private:
+ Stream graph;
+};
+
+/** Main program for Graph fully_connected test
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( Input dimensions [width, batch]
+ * Fully connected [num_outputs,type]
+ * Verification[tolerance_number,absolute_tolerance,relative_tolerance] )
+ *
+ */
+int main(int argc, char **argv)
+{
+ return arm_compute::utils::run_example<Graphfully_connectedValidateExample>(argc, argv);
+}