/* * Copyright (c) 2017 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_MODEL_OBJECTS_ALEXNET_H__ #define __ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__ #include "TensorLibrary.h" #include "Utils.h" #include using namespace arm_compute; using namespace arm_compute::test; namespace arm_compute { namespace test { namespace model_objects { /** AlexNet model object */ template class AlexNet { public: AlexNet() : _batches(1), _reshaped_weights(false) { } void init_weights(unsigned int batches, bool reshaped_weights = false) { _batches = batches; _reshaped_weights = reshaped_weights; // Initialize weights and biases if(!_reshaped_weights) { for(auto &wi : w) { wi = std::unique_ptr(new TensorType()); } for(auto &bi : b) { bi = std::unique_ptr(new TensorType()); } w[0]->allocator()->init(TensorInfo(TensorShape(11U, 11U, 3U, 96U), 1, dt, fixed_point_position)); b[0]->allocator()->init(TensorInfo(TensorShape(96U), 1, dt, fixed_point_position)); w[1]->allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, dt, fixed_point_position)); b[1]->allocator()->init(TensorInfo(TensorShape(256U), 1, dt, fixed_point_position)); w[2]->allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, dt, fixed_point_position)); b[2]->allocator()->init(TensorInfo(TensorShape(384U), 1, dt, fixed_point_position)); w[3]->allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, dt, fixed_point_position)); b[3]->allocator()->init(TensorInfo(TensorShape(384U), 1, dt, fixed_point_position)); w[4]->allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, dt, fixed_point_position)); b[4]->allocator()->init(TensorInfo(TensorShape(256U), 1, dt, fixed_point_position)); w[5]->allocator()->init(TensorInfo(TensorShape(9216U, 4096U), 1, dt, fixed_point_position)); b[5]->allocator()->init(TensorInfo(TensorShape(4096U), 1, dt, fixed_point_position)); w[6]->allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, dt, fixed_point_position)); b[6]->allocator()->init(TensorInfo(TensorShape(4096U), 1, dt, fixed_point_position)); w[7]->allocator()->init(TensorInfo(TensorShape(4096U, 1000U), 1, dt, fixed_point_position)); b[7]->allocator()->init(TensorInfo(TensorShape(1000U), 1, dt, fixed_point_position)); w21 = std::unique_ptr(new SubTensorType(w[1].get(), TensorShape(5U, 5U, 48U, 128U), Coordinates())); w22 = std::unique_ptr(new SubTensorType(w[1].get(), TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128))); b21 = std::unique_ptr(new SubTensorType(b[1].get(), TensorShape(128U), Coordinates())); b22 = std::unique_ptr(new SubTensorType(b[1].get(), TensorShape(128U), Coordinates(128))); w41 = std::unique_ptr(new SubTensorType(w[3].get(), TensorShape(3U, 3U, 192U, 192U), Coordinates())); w42 = std::unique_ptr(new SubTensorType(w[3].get(), TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192))); b41 = std::unique_ptr(new SubTensorType(b[3].get(), TensorShape(192U), Coordinates())); b42 = std::unique_ptr(new SubTensorType(b[3].get(), TensorShape(192U), Coordinates(192))); w51 = std::unique_ptr(new SubTensorType(w[4].get(), TensorShape(3U, 3U, 192U, 128U), Coordinates())); w52 = std::unique_ptr(new SubTensorType(w[4].get(), TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128))); b51 = std::unique_ptr(new SubTensorType(b[4].get(), TensorShape(128U), Coordinates())); b52 = std::unique_ptr(new SubTensorType(b[4].get(), TensorShape(128U), Coordinates(128))); } else { const unsigned int dt_size = 16 / arm_compute::data_size_from_type(dt); // Create tensor for the reshaped weights w[0] = std::unique_ptr(new TensorType()); auto w21_tensor = std::unique_ptr(new TensorType()); auto w22_tensor = std::unique_ptr(new TensorType()); w[2] = std::unique_ptr(new TensorType()); auto w41_tensor = std::unique_ptr(new TensorType()); auto w42_tensor = std::unique_ptr(new TensorType()); auto w51_tensor = std::unique_ptr(new TensorType()); auto w52_tensor = std::unique_ptr(new TensorType()); w[0]->allocator()->init(TensorInfo(TensorShape(366U * dt_size, 96U / dt_size), 1, dt, fixed_point_position)); w21_tensor->allocator()->init(TensorInfo(TensorShape(1248U * dt_size, 128U / dt_size), 1, dt, fixed_point_position)); w22_tensor->allocator()->init(TensorInfo(TensorShape(1248U * dt_size, 128U / dt_size), 1, dt, fixed_point_position)); w[2]->allocator()->init(TensorInfo(TensorShape(2560U * dt_size, 384U / dt_size), 1, dt, fixed_point_position)); w41_tensor->allocator()->init(TensorInfo(TensorShape(1920U * dt_size, 192U / dt_size), 1, dt, fixed_point_position)); w42_tensor->allocator()->init(TensorInfo(TensorShape(1920U * dt_size, 192U / dt_size), 1, dt, fixed_point_position)); w51_tensor->allocator()->init(TensorInfo(TensorShape(1920U * dt_size, 128U / dt_size), 1, dt, fixed_point_position)); w52_tensor->allocator()->init(TensorInfo(TensorShape(1920U * dt_size, 128U / dt_size), 1, dt, fixed_point_position)); w21 = std::move(w21_tensor); w22 = std::move(w22_tensor); w41 = std::move(w41_tensor); w42 = std::move(w42_tensor); w51 = std::move(w51_tensor); w52 = std::move(w52_tensor); w[5] = std::unique_ptr(new TensorType()); w[6] = std::unique_ptr(new TensorType()); w[7] = std::unique_ptr(new TensorType()); b[5] = std::unique_ptr(new TensorType()); b[6] = std::unique_ptr(new TensorType()); b[7] = std::unique_ptr(new TensorType()); b[5]->allocator()->init(TensorInfo(TensorShape(4096U), 1, dt, fixed_point_position)); b[6]->allocator()->init(TensorInfo(TensorShape(4096U), 1, dt, fixed_point_position)); b[7]->allocator()->init(TensorInfo(TensorShape(1000U), 1, dt, fixed_point_position)); if(_batches > 1) { w[5]->allocator()->init(TensorInfo(TensorShape(9216U * dt_size, 4096U / dt_size), 1, dt, fixed_point_position)); w[6]->allocator()->init(TensorInfo(TensorShape(4096U * dt_size, 4096U / dt_size), 1, dt, fixed_point_position)); w[7]->allocator()->init(TensorInfo(TensorShape(4096U * dt_size, 1000U / dt_size), 1, dt, fixed_point_position)); } else { w[5]->allocator()->init(TensorInfo(TensorShape(4096U, 9216U), 1, dt, fixed_point_position)); w[6]->allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, dt, fixed_point_position)); w[7]->allocator()->init(TensorInfo(TensorShape(1000U, 4096U), 1, dt, fixed_point_position)); } } } void build() { input.allocator()->init(TensorInfo(TensorShape(227U, 227U, 3U, _batches), 1, dt, fixed_point_position)); output.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, dt, fixed_point_position)); // Initialize intermediate tensors // Layer 1 conv1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, dt, fixed_point_position)); act1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, dt, fixed_point_position)); norm1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, dt, fixed_point_position)); pool1_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 96U, _batches), 1, dt, fixed_point_position)); pool11_out = std::unique_ptr(new SubTensorType(&pool1_out, TensorShape(27U, 27U, 48U, _batches), Coordinates())); pool12_out = std::unique_ptr(new SubTensorType(&pool1_out, TensorShape(27U, 27U, 48U, _batches), Coordinates(0, 0, 48))); // Layer 2 conv2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, dt, fixed_point_position)); conv21_out = std::unique_ptr(new SubTensorType(&conv2_out, TensorShape(27U, 27U, 128U, _batches), Coordinates())); conv22_out = std::unique_ptr(new SubTensorType(&conv2_out, TensorShape(27U, 27U, 128U, _batches), Coordinates(0, 0, 128))); act2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, dt, fixed_point_position)); norm2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, dt, fixed_point_position)); pool2_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, dt, fixed_point_position)); // Layer 3 conv3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, dt, fixed_point_position)); act3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, dt, fixed_point_position)); act31_out = std::unique_ptr(new SubTensorType(&act3_out, TensorShape(13U, 13U, 192U, _batches), Coordinates())); act32_out = std::unique_ptr(new SubTensorType(&act3_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192))); // Layer 4 conv4_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, dt, fixed_point_position)); conv41_out = std::unique_ptr(new SubTensorType(&conv4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates())); conv42_out = std::unique_ptr(new SubTensorType(&conv4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192))); act4_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, dt, fixed_point_position)); act41_out = std::unique_ptr(new SubTensorType(&act4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates())); act42_out = std::unique_ptr(new SubTensorType(&act4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192))); // Layer 5 conv5_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, dt, fixed_point_position)); conv51_out = std::unique_ptr(new SubTensorType(&conv5_out, TensorShape(13U, 13U, 128U, _batches), Coordinates())); conv52_out = std::unique_ptr(new SubTensorType(&conv5_out, TensorShape(13U, 13U, 128U, _batches), Coordinates(0, 0, 128))); act5_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, dt, fixed_point_position)); pool5_out.allocator()->init(TensorInfo(TensorShape(6U, 6U, 256U, _batches), 1, dt, fixed_point_position)); // Layer 6 fc6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, dt, fixed_point_position)); act6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, dt, fixed_point_position)); // Layer 7 fc7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, dt, fixed_point_position)); act7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, dt, fixed_point_position)); // Layer 8 fc8_out.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, dt, fixed_point_position)); // Allocate layers { // Layer 1 conv1 = std::unique_ptr(new ConvolutionLayerFunction()); act1 = std::unique_ptr(new ActivationLayerFunction()); norm1 = std::unique_ptr(new NormalizationLayerFunction()); pool1 = std::unique_ptr(new PoolingLayerFunction()); // Layer 2 conv21 = std::unique_ptr(new ConvolutionLayerFunction()); conv22 = std::unique_ptr(new ConvolutionLayerFunction()); act2 = std::unique_ptr(new ActivationLayerFunction()); norm2 = std::unique_ptr(new NormalizationLayerFunction()); pool2 = std::unique_ptr(new PoolingLayerFunction()); // Layer 3 conv3 = std::unique_ptr(new ConvolutionLayerFunction()); act3 = std::unique_ptr(new ActivationLayerFunction()); // Layer 4 conv41 = std::unique_ptr(new ConvolutionLayerFunction()); conv42 = std::unique_ptr(new ConvolutionLayerFunction()); act4 = std::unique_ptr(new ActivationLayerFunction()); // Layer 5 conv51 = std::unique_ptr(new ConvolutionLayerFunction()); conv52 = std::unique_ptr(new ConvolutionLayerFunction()); act5 = std::unique_ptr(new ActivationLayerFunction()); pool5 = std::unique_ptr(new PoolingLayerFunction()); // Layer 6 fc6 = std::unique_ptr(new FullyConnectedLayerFunction()); act6 = std::unique_ptr(new ActivationLayerFunction()); // Layer 7 fc7 = std::unique_ptr(new FullyConnectedLayerFunction()); act7 = std::unique_ptr(new ActivationLayerFunction()); // Layer 8 fc8 = std::unique_ptr(new FullyConnectedLayerFunction()); // Softmax smx = std::unique_ptr(new SoftmaxLayerFunction()); } // Configure Layers { // Layer 1 conv1->configure(&input, w[0].get(), b[0].get(), &conv1_out, PadStrideInfo(4, 4, 0, 0), WeightsInfo(_reshaped_weights, 11U)); act1->configure(&conv1_out, &act1_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); norm1->configure(&act1_out, &norm1_out, NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)); pool1->configure(&norm1_out, &pool1_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))); // Layer 2 conv21->configure(pool11_out.get(), w21.get(), b21.get(), conv21_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U)); conv22->configure(pool12_out.get(), w22.get(), b22.get(), conv22_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U)); act2->configure(&conv2_out, &act2_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); norm2->configure(&act2_out, &norm2_out, NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)); pool2->configure(&norm2_out, &pool2_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))); // Layer 3 conv3->configure(&pool2_out, w[2].get(), b[2].get(), &conv3_out, PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U)); act3->configure(&conv3_out, &act3_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // Layer 4 conv41->configure(act31_out.get(), w41.get(), b41.get(), conv41_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U)); conv42->configure(act32_out.get(), w42.get(), b42.get(), conv42_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U)); act4->configure(&conv4_out, &act4_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // Layer 5 conv51->configure(act41_out.get(), w51.get(), b51.get(), conv51_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U)); conv52->configure(act42_out.get(), w52.get(), b52.get(), conv52_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U)); act5->configure(&conv5_out, &act5_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); pool5->configure(&act5_out, &pool5_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))); // Layer 6 fc6->configure(&pool5_out, w[5].get(), b[5].get(), &fc6_out, true, _reshaped_weights); act6->configure(&fc6_out, &act6_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // Layer 7 fc7->configure(&act6_out, w[6].get(), b[6].get(), &fc7_out, true, _reshaped_weights); act7->configure(&fc7_out, &act7_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // Layer 8 fc8->configure(&act7_out, w[7].get(), b[7].get(), &fc8_out, true, _reshaped_weights); // Softmax smx->configure(&fc8_out, &output); } } void allocate() { input.allocator()->allocate(); output.allocator()->allocate(); for(auto &wi : w) { if(wi.get()) { wi->allocator()->allocate(); } } for(auto &bi : b) { if(bi.get()) { bi->allocator()->allocate(); } } if(_reshaped_weights) { dynamic_cast(w21.get())->allocator()->allocate(); dynamic_cast(w22.get())->allocator()->allocate(); dynamic_cast(w41.get())->allocator()->allocate(); dynamic_cast(w42.get())->allocator()->allocate(); dynamic_cast(w51.get())->allocator()->allocate(); dynamic_cast(w52.get())->allocator()->allocate(); } conv1_out.allocator()->allocate(); act1_out.allocator()->allocate(); norm1_out.allocator()->allocate(); pool1_out.allocator()->allocate(); conv2_out.allocator()->allocate(); act2_out.allocator()->allocate(); norm2_out.allocator()->allocate(); pool2_out.allocator()->allocate(); conv3_out.allocator()->allocate(); act3_out.allocator()->allocate(); conv4_out.allocator()->allocate(); act4_out.allocator()->allocate(); conv5_out.allocator()->allocate(); act5_out.allocator()->allocate(); pool5_out.allocator()->allocate(); fc6_out.allocator()->allocate(); act6_out.allocator()->allocate(); fc7_out.allocator()->allocate(); act7_out.allocator()->allocate(); fc8_out.allocator()->allocate(); } /** Fills the trainable parameters and input with random data. */ void fill_random() { library->fill_tensor_uniform(Accessor(input), 0); if(!_reshaped_weights) { for(unsigned int i = 0; i < w.size(); ++i) { library->fill_tensor_uniform(Accessor(*w[i]), i + 1); library->fill_tensor_uniform(Accessor(*b[i]), i + 10); } } else { library->fill_tensor_uniform(Accessor(*w[0]), 1); library->fill_tensor_uniform(Accessor(*w[2]), 2); library->fill_tensor_uniform(Accessor(*w[5]), 3); library->fill_tensor_uniform(Accessor(*b[5]), 4); library->fill_tensor_uniform(Accessor(*w[6]), 5); library->fill_tensor_uniform(Accessor(*b[6]), 6); library->fill_tensor_uniform(Accessor(*w[7]), 7); library->fill_tensor_uniform(Accessor(*b[7]), 8); library->fill_tensor_uniform(Accessor(*dynamic_cast(w21.get())), 9); library->fill_tensor_uniform(Accessor(*dynamic_cast(w22.get())), 10); library->fill_tensor_uniform(Accessor(*dynamic_cast(w41.get())), 11); library->fill_tensor_uniform(Accessor(*dynamic_cast(w42.get())), 12); library->fill_tensor_uniform(Accessor(*dynamic_cast(w51.get())), 13); library->fill_tensor_uniform(Accessor(*dynamic_cast(w52.get())), 14); } } #ifdef INTERNAL_ONLY /** Fills the trainable parameters from binary files * * @param weights Files names containing the weights data * @param biases Files names containing the bias data */ void fill(std::vector weights, std::vector biases) { ARM_COMPUTE_ERROR_ON(weights.size() != w.size()); ARM_COMPUTE_ERROR_ON(biases.size() != b.size()); ARM_COMPUTE_ERROR_ON(_reshaped_weights); for(unsigned int i = 0; i < weights.size(); ++i) { library->fill_layer_data(Accessor(*w[i]), weights[i]); library->fill_layer_data(Accessor(*b[i]), biases[i]); } } /** Feed input to network from file. * * @param name File name of containing the input data. */ void feed(std::string name) { library->fill_layer_data(Accessor(input), name); } #endif /* INTERNAL_ONLY */ /** Get the classification results. * * @return Vector containing the classified labels */ std::vector get_classifications() { std::vector classified_labels; Accessor output_accessor(output); Window window; window.set(Window::DimX, Window::Dimension(0, 1, 1)); for(unsigned int d = 1; d < output_accessor.shape().num_dimensions(); ++d) { window.set(d, Window::Dimension(0, output_accessor.shape()[d], 1)); } execute_window_loop(window, [&](const Coordinates & id) { int max_idx = 0; float val = 0; const void *const out_ptr = output_accessor(id); for(unsigned int l = 0; l < output_accessor.shape().x(); ++l) { float curr_val = reinterpret_cast(out_ptr)[l]; if(curr_val > val) { max_idx = l; val = curr_val; } } classified_labels.push_back(max_idx); }); return classified_labels; } /** Clear all allocated memory from the tensor objects */ void clear() { conv1.reset(); act1.reset(); norm1.reset(); pool1.reset(); conv21.reset(); conv22.reset(); act2.reset(); norm2.reset(); pool2.reset(); conv3.reset(); act3.reset(); conv41.reset(); conv42.reset(); act4.reset(); conv51.reset(); conv52.reset(); act5.reset(); pool5.reset(); fc6.reset(); act6.reset(); fc7.reset(); act7.reset(); fc8.reset(); smx.reset(); // Free allocations input.allocator()->free(); output.allocator()->free(); for(auto &wi : w) { wi.reset(); } for(auto &bi : b) { bi.reset(); } w21.reset(); w22.reset(); b21.reset(); b21.reset(); w41.reset(); w42.reset(); b41.reset(); b42.reset(); w51.reset(); w52.reset(); b51.reset(); b52.reset(); conv1_out.allocator()->free(); act1_out.allocator()->free(); norm1_out.allocator()->free(); pool1_out.allocator()->free(); conv2_out.allocator()->free(); act2_out.allocator()->free(); norm2_out.allocator()->free(); pool2_out.allocator()->free(); conv3_out.allocator()->free(); act3_out.allocator()->free(); conv4_out.allocator()->free(); act4_out.allocator()->free(); conv5_out.allocator()->free(); act5_out.allocator()->free(); pool5_out.allocator()->free(); fc6_out.allocator()->free(); act6_out.allocator()->free(); fc7_out.allocator()->free(); act7_out.allocator()->free(); fc8_out.allocator()->free(); } /** Runs the model */ void run() { // Layer 1 conv1->run(); act1->run(); norm1->run(); pool1->run(); // Layer 2 conv21->run(); conv22->run(); act2->run(); norm2->run(); pool2->run(); // Layer 3 conv3->run(); act3->run(); // Layer 4 conv41->run(); conv42->run(); act4->run(); // Layer 5 conv51->run(); conv52->run(); act5->run(); pool5->run(); // Layer 6 fc6->run(); act6->run(); // Layer 7 fc7->run(); act7->run(); // Layer 8 fc8->run(); // Softmax smx->run(); } private: unsigned int _batches; bool _reshaped_weights; std::unique_ptr act1{ nullptr }, act2{ nullptr }, act3{ nullptr }, act4{ nullptr }, act5{ nullptr }, act6{ nullptr }, act7{ nullptr }; std::unique_ptr conv1{ nullptr }, conv21{ nullptr }, conv22{ nullptr }, conv3{ nullptr }, conv41{ nullptr }, conv42{ nullptr }, conv51{ nullptr }, conv52{ nullptr }; std::unique_ptr fc6{ nullptr }, fc7{ nullptr }, fc8{}; std::unique_ptr norm1{ nullptr }, norm2{ nullptr }; std::unique_ptr pool1{ nullptr }, pool2{ nullptr }, pool5{ nullptr }; std::unique_ptr smx{ nullptr }; TensorType input{}, output{}; std::array, 8> w{}, b{}; std::unique_ptr w21{ nullptr }, w22{ nullptr }, b21{ nullptr }, b22{ nullptr }; std::unique_ptr w41{ nullptr }, w42{ nullptr }, b41{ nullptr }, b42{ nullptr }; std::unique_ptr w51{ nullptr }, w52{ nullptr }, b51{ nullptr }, b52{ nullptr }; TensorType conv1_out{}, act1_out{}, norm1_out{}, pool1_out{}; TensorType conv2_out{}, act2_out{}, pool2_out{}, norm2_out{}; TensorType conv3_out{}, act3_out{}; TensorType conv4_out{}, act4_out{}; TensorType conv5_out{}, act5_out{}, pool5_out{}; TensorType fc6_out{}, act6_out{}; TensorType fc7_out{}, act7_out{}; TensorType fc8_out{}; std::unique_ptr pool11_out{ nullptr }, pool12_out{ nullptr }; std::unique_ptr conv21_out{ nullptr }, conv22_out{ nullptr }; std::unique_ptr act31_out{ nullptr }, act32_out{ nullptr }; std::unique_ptr conv41_out{ nullptr }, conv42_out{ nullptr }, act41_out{ nullptr }, act42_out{ nullptr }; std::unique_ptr conv51_out{ nullptr }, conv52_out{ nullptr }; }; } // namespace model_objects } // namespace test } // namespace arm_compute #endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__