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314 lines
15 KiB
314 lines
15 KiB
/*
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* Copyright (c) 2019-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/graph.h"
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#include "tests/NEON/Accessor.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/reference/FullyConnectedLayer.h"
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#include "tests/validation/reference/Permute.h"
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#include "utils/CommonGraphOptions.h"
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#include "utils/GraphUtils.h"
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#include "utils/Utils.h"
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#include "ValidateExample.h"
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#include "graph_validate_utils.h"
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#include <utility>
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using namespace arm_compute::utils;
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using namespace arm_compute::graph::frontend;
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using namespace arm_compute::graph_utils;
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using namespace arm_compute::graph;
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using namespace arm_compute;
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using namespace arm_compute::test;
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using namespace arm_compute::test::validation;
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namespace
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{
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/** Fully connected command line options used to configure the graph examples
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*
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* (Similar to common options)
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* The options in this object get populated when "parse()" is called on the parser used to construct it.
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* The expected workflow is:
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*
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* CommandLineParser parser;
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* CommonOptions options( parser );
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* parser.parse(argc, argv);
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*/
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class FullyConnectedOptions final : public CommonGraphValidateOptions
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{
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public:
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explicit FullyConnectedOptions(CommandLineParser &parser) noexcept
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: CommonGraphValidateOptions(parser),
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width(parser.add_option<SimpleOption<int>>("width", 3)),
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batch(parser.add_option<SimpleOption<int>>("batch", 1)),
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input_scale(parser.add_option<SimpleOption<float>>("input_scale", 1.0f)),
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input_offset(parser.add_option<SimpleOption<int>>("input_offset", 0)),
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weights_scale(parser.add_option<SimpleOption<float>>("weights_scale", 1.0f)),
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weights_offset(parser.add_option<SimpleOption<int>>("weights_offset", 0)),
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output_scale(parser.add_option<SimpleOption<float>>("output_scale", 1.0f)),
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output_offset(parser.add_option<SimpleOption<int>>("output_offset", 0)),
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num_outputs(parser.add_option<SimpleOption<int>>("num_outputs", 1)),
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input_range_low(parser.add_option<SimpleOption<uint64_t>>("input_range_low")),
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input_range_high(parser.add_option<SimpleOption<uint64_t>>("input_range_high")),
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weights_range_low(parser.add_option<SimpleOption<uint64_t>>("weights_range_low")),
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weights_range_high(parser.add_option<SimpleOption<uint64_t>>("weights_range_high"))
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{
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width->set_help("Set Input dimension width");
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batch->set_help("Set Input dimension batch");
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input_scale->set_help("Quantization scale from QASYMM8");
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input_offset->set_help("Quantization offset from QASYMM8");
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weights_scale->set_help("Quantization scale from QASYMM8");
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weights_offset->set_help("Quantization offset from QASYMM8");
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output_scale->set_help("Quantization scale from QASYMM8");
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output_offset->set_help("Quantization offset from QASYMM8");
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num_outputs->set_help("Number of outputs.");
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input_range_low->set_help("Lower bound for input randomization range");
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input_range_high->set_help("Lower bound for input randomization range");
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weights_range_low->set_help("Lower bound for input randomization range");
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weights_range_high->set_help("Lower bound for input randomization range");
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}
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/** Fill out the supplied parameters with user supplied parameters
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*
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* @param[out] os Output stream.
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* @param[in] common_params Example parameters to output
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*
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* @return None.
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*/
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void consume_parameters(ExampleParams &common_params)
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{
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common_params.input.width = width->value();
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common_params.input.batch = batch->value();
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common_params.input.quant_info = QuantizationInfo(input_scale->value(), input_offset->value());
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common_params.input.range_low = input_range_low->value();
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common_params.input.range_high = input_range_high->value();
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common_params.weights.quant_info = QuantizationInfo(weights_scale->value(), weights_offset->value());
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common_params.weights.range_low = weights_range_low->value();
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common_params.weights.range_high = weights_range_high->value();
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common_params.output.quant_info = QuantizationInfo(output_scale->value(), output_offset->value());
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common_params.data_type = data_type->value();
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common_params.fully_connected.num_outputs = num_outputs->value();
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}
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void print_parameters(::std::ostream &os, const ExampleParams &common_params) override
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{
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os << "Threads : " << common_params.common_params.threads << std::endl;
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os << "Target : " << common_params.common_params.target << std::endl;
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os << "Data type : " << common_params.data_type << std::endl;
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os << "Input dimensions(X,Y, Channels, Batch) : (" << common_params.input.width << "," << common_params.input.height << "," << common_params.input.fm << "," << common_params.input.batch << ")"
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<< std::endl;
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os << "Number of outputs : " << common_params.fully_connected.num_outputs << std::endl;
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}
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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FullyConnectedOptions(const FullyConnectedOptions &) = delete;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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FullyConnectedOptions &operator=(const FullyConnectedOptions &) = delete;
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/** Allow instances of this class to be moved */
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FullyConnectedOptions(FullyConnectedOptions &&) noexcept(true) = default;
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/** Allow instances of this class to be moved */
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FullyConnectedOptions &operator=(FullyConnectedOptions &&) noexcept(true) = default;
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/** Default destructor */
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~FullyConnectedOptions() override = default;
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private:
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SimpleOption<int> *width; /**< Input width */
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SimpleOption<int> *batch; /**< Input batch */
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SimpleOption<float> *input_scale; /**< Input Quantization scale from QASSYMM8 */
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SimpleOption<int> *input_offset; /**< Input Quantization offset from QASSYMM8 */
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SimpleOption<float> *weights_scale; /**< Weights Quantization scale from QASSYMM8 */
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SimpleOption<int> *weights_offset; /**< Weights Quantization offset from QASSYMM8 */
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SimpleOption<float> *output_scale; /**< Output Quantization scale from QASSYMM8 */
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SimpleOption<int> *output_offset; /**< Output Quantization offset from QASSYMM8 */
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SimpleOption<int> *num_outputs; /**< Number of outputs. */
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SimpleOption<uint64_t> *input_range_low; /**< Lower bound for input randomization range */
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SimpleOption<uint64_t> *input_range_high; /**< Upper bound for input randomization range */
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SimpleOption<uint64_t> *weights_range_low; /**< Lower bound for weights randomization range */
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SimpleOption<uint64_t> *weights_range_high; /**< Upper bound for weights randomization range */
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};
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/** Fully Connected Layer Graph example validation accessor class */
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template <typename D>
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class FullyConnectedVerifyAccessor final : public VerifyAccessor<D>
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{
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using BaseClassType = VerifyAccessor<D>;
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using BaseClassType::BaseClassType;
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using BaseClassType::_params;
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using TBias = typename std::conditional<std::is_same<typename std::decay<D>::type, uint8_t>::value, int32_t, D>::type;
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// Inherited methods overriden:
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void create_tensors(arm_compute::test::SimpleTensor<D> &src,
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arm_compute::test::SimpleTensor<D> &weights,
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arm_compute::test::SimpleTensor<TBias> &bias,
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ITensor &tensor) override
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{
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// Calculate Tensor shapes for verification
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const TensorShape input_shape = TensorShape(_params.input.width, _params.input.height, _params.input.fm, _params.input.batch);
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const TensorDescriptor input_descriptor = TensorDescriptor(input_shape, _params.data_type, _params.input.quant_info);
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const TensorDescriptor weights_descriptor = FullyConnectedLayerNode::compute_weights_descriptor(input_descriptor,
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_params.fully_connected.num_outputs,
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_params.fully_connected.info,
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_params.weights.quant_info);
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const TensorDescriptor output_desciptor = FullyConnectedLayerNode::compute_output_descriptor(input_descriptor, _params.fully_connected.num_outputs, _params.output.quant_info);
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//Create Input tensors
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src = SimpleTensor<D> { input_descriptor.shape, _params.data_type, 1, input_descriptor.quant_info };
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weights = SimpleTensor<D> { weights_descriptor.shape, _params.data_type, 1, weights_descriptor.quant_info };
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bias = SimpleTensor<TBias> { TensorShape(tensor.info()->tensor_shape().x()), _params.data_type, 1, _params.input.quant_info };
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}
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TensorShape output_shape(ITensor &tensor) override
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{
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ARM_COMPUTE_UNUSED(tensor);
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const TensorShape input_shape = TensorShape(_params.input.width, _params.input.height, _params.input.fm, _params.input.batch);
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const TensorDescriptor input_descriptor = TensorDescriptor(input_shape, _params.data_type, _params.input.quant_info);
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const TensorDescriptor output_desciptor = FullyConnectedLayerNode::compute_output_descriptor(input_descriptor, _params.fully_connected.num_outputs, _params.output.quant_info);
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return output_desciptor.shape;
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}
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arm_compute::test::SimpleTensor<D> reference(arm_compute::test::SimpleTensor<D> &src,
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arm_compute::test::SimpleTensor<D> &weights,
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arm_compute::test::SimpleTensor<TBias> &bias,
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const arm_compute::TensorShape &output_shape) override
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{
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return reference::fully_connected_layer<D>(src, weights, bias, output_shape, _params.output.quant_info);
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}
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float relative_tolerance() override
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{
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const std::map<arm_compute::graph::Target, const std::map<DataType, float>> relative_tolerance
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{
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{
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arm_compute::graph::Target::CL,
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{ { DataType::F16, 0.2f },
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{ DataType::F32, 0.05f },
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{ DataType::QASYMM8, 1.0f }
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}
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},
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{
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arm_compute::graph::Target::NEON,
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{ { DataType::F16, 0.2f },
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{ DataType::F32, 0.01f },
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{ DataType::QASYMM8, 1.0f }
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}
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}
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};
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return relative_tolerance.at(_params.common_params.target).at(_params.data_type);
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}
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float absolute_tolerance() override
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{
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const std::map<Target, const std::map<DataType, float>> absolute_tolerance
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{
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{
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Target::CL,
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{ { DataType::F16, 0.0f },
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{ DataType::F32, 0.0001f },
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{ DataType::QASYMM8, 1.0f }
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}
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},
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{
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Target::NEON,
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{ { DataType::F16, 0.3f },
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{ DataType::F32, 0.1f },
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{ DataType::QASYMM8, 1.0f }
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}
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}
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};
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return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
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}
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float tolerance_number() override
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{
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const std::map<Target, const std::map<DataType, float>> absolute_tolerance
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{
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{
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Target::CL,
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{ { DataType::F16, 0.07f },
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{ DataType::F32, 0.07f },
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{ DataType::QASYMM8, 0.0f }
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}
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},
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{
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Target::NEON,
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{ { DataType::F16, 0.07f },
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{ DataType::F32, 0.0f },
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{ DataType::QASYMM8, 0.0f }
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}
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}
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};
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return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
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}
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};
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} // namespace
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class GraphFullyConnectedValidateExample final : public GraphValidateExample<FullyConnectedLayer, FullyConnectedOptions, FullyConnectedVerifyAccessor>
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{
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using GraphValidateExample::graph;
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public:
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GraphFullyConnectedValidateExample()
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: GraphValidateExample("Fully_connected Graph example")
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{
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}
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FullyConnectedLayer GraphFunctionLayer(ExampleParams ¶ms) override
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{
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const PixelValue lower = PixelValue(params.input.range_low, params.data_type, params.input.quant_info);
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const PixelValue upper = PixelValue(params.input.range_high, params.data_type, params.input.quant_info);
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const PixelValue weights_lower = PixelValue(params.weights.range_low, params.data_type, params.weights.quant_info);
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const PixelValue weights_upper = PixelValue(params.weights.range_high, params.data_type, params.weights.quant_info);
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return FullyConnectedLayer(params.fully_connected.num_outputs,
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get_random_accessor(weights_lower, weights_upper, 1),
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get_random_accessor(lower, upper, 2),
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params.fully_connected.info, params.weights.quant_info, params.output.quant_info);
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}
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};
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/** Main program for Graph fully_connected test
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*
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* @param[in] argc Number of arguments
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* @param[in] argv Arguments ( Input dimensions [width, batch]
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* Fully connected [num_outputs,type]
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* Verification[tolerance_number,absolute_tolerance,relative_tolerance] )
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*
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*/
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int main(int argc, char **argv)
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{
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return arm_compute::utils::run_example<GraphFullyConnectedValidateExample>(argc, argv);
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}
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