/* * Copyright (c) 2017-2018 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 "arm_compute/runtime/NEON/NEScheduler.h" #include "arm_compute/runtime/Tensor.h" #include "tests/AssetsLibrary.h" #include "tests/Globals.h" #include "tests/Utils.h" #include namespace arm_compute { namespace test { namespace networks { /** AlexNet model object */ template class AlexNetNetwork { public: /** Initialize the network. * * @param[in] data_type Data type. * @param[in] fixed_point_position Fixed point position (for quantized data types). * @param[in] batches Number of batches. * @param[in] reshaped_weights Whether the weights need reshaping or not. Default: false. */ void init(DataType data_type, int fixed_point_position, int batches, bool reshaped_weights = false) { _data_type = data_type; _fixed_point_position = fixed_point_position; _batches = batches; _reshaped_weights = reshaped_weights; // Initialize weights and biases if(!_reshaped_weights) { w[0].allocator()->init(TensorInfo(TensorShape(11U, 11U, 3U, 96U), 1, _data_type, _fixed_point_position)); b[0].allocator()->init(TensorInfo(TensorShape(96U), 1, _data_type, _fixed_point_position)); w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type, _fixed_point_position)); b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position)); w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type, _fixed_point_position)); b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position)); w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type, _fixed_point_position)); b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position)); w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type, _fixed_point_position)); b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position)); w[5].allocator()->init(TensorInfo(TensorShape(9216U, 4096U), 1, _data_type, _fixed_point_position)); b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position)); w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type, _fixed_point_position)); b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position)); w[7].allocator()->init(TensorInfo(TensorShape(4096U, 1000U), 1, _data_type, _fixed_point_position)); b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type, _fixed_point_position)); w11 = std::unique_ptr(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates())); w12 = std::unique_ptr(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128))); b11 = std::unique_ptr(new SubTensorType(&b[1], TensorShape(128U), Coordinates())); b12 = std::unique_ptr(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128))); w31 = std::unique_ptr(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates())); w32 = std::unique_ptr(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192))); b31 = std::unique_ptr(new SubTensorType(&b[3], TensorShape(192U), Coordinates())); b32 = std::unique_ptr(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192))); w41 = std::unique_ptr(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates())); w42 = std::unique_ptr(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128))); b41 = std::unique_ptr(new SubTensorType(&b[4], TensorShape(128U), Coordinates())); b42 = std::unique_ptr(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128))); } else { auto reshape = [&](unsigned int width, unsigned int height, bool convolution_layer) -> TensorShape { const bool is_optimised = std::is_same::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV7 && data_type == DataType::F32; if(convolution_layer && is_optimised) { return TensorShape{ height, width }; } else { const int interleave_width = 16 / arm_compute::data_size_from_type(_data_type); return TensorShape{ width * interleave_width, static_cast(std::ceil(static_cast(height) / interleave_width)) }; } }; // Create tensor for the reshaped weights w[0].allocator()->init(TensorInfo(reshape(366U, 96U, true), 1, _data_type, _fixed_point_position)); // Configure the direct convolution's weights. Direct convolution doesn't need reshape weights if(!_is_direct_conv) { auto w11_tensor = std::unique_ptr(new TensorType()); auto w12_tensor = std::unique_ptr(new TensorType()); auto w31_tensor = std::unique_ptr(new TensorType()); auto w32_tensor = std::unique_ptr(new TensorType()); auto w41_tensor = std::unique_ptr(new TensorType()); auto w42_tensor = std::unique_ptr(new TensorType()); w11_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U, true), 1, _data_type, _fixed_point_position)); w12_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U, true), 1, _data_type, _fixed_point_position)); w31_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U, true), 1, _data_type, _fixed_point_position)); w32_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U, true), 1, _data_type, _fixed_point_position)); w41_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U, true), 1, _data_type, _fixed_point_position)); w42_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U, true), 1, _data_type, _fixed_point_position)); w[2].allocator()->init(TensorInfo(reshape(2560U, 384U, true), 1, _data_type, _fixed_point_position)); w11 = std::move(w11_tensor); w12 = std::move(w12_tensor); w31 = std::move(w31_tensor); w32 = std::move(w32_tensor); w41 = std::move(w41_tensor); w42 = std::move(w42_tensor); } else { w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type, _fixed_point_position)); b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position)); w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type, _fixed_point_position)); b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position)); w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type, _fixed_point_position)); b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position)); w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type, _fixed_point_position)); b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position)); w11 = std::unique_ptr(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates())); w12 = std::unique_ptr(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128))); b11 = std::unique_ptr(new SubTensorType(&b[1], TensorShape(128U), Coordinates())); b12 = std::unique_ptr(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128))); w31 = std::unique_ptr(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates())); w32 = std::unique_ptr(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192))); b31 = std::unique_ptr(new SubTensorType(&b[3], TensorShape(192U), Coordinates())); b32 = std::unique_ptr(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192))); w41 = std::unique_ptr(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates())); w42 = std::unique_ptr(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128))); b41 = std::unique_ptr(new SubTensorType(&b[4], TensorShape(128U), Coordinates())); b42 = std::unique_ptr(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128))); } b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position)); b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position)); b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type, _fixed_point_position)); if(_batches > 1 && std::is_same::value) { w[5].allocator()->init(TensorInfo(reshape(9216U, 4096U, false), 1, _data_type, _fixed_point_position)); w[6].allocator()->init(TensorInfo(reshape(4096U, 4096U, false), 1, _data_type, _fixed_point_position)); w[7].allocator()->init(TensorInfo(reshape(4096U, 1000U, false), 1, _data_type, _fixed_point_position)); } else { w[5].allocator()->init(TensorInfo(TensorShape(4096U, 9216U), 1, _data_type, _fixed_point_position)); w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type, _fixed_point_position)); w[7].allocator()->init(TensorInfo(TensorShape(1000U, 4096U), 1, _data_type, _fixed_point_position)); } } } /** Build the network */ void build() { input.allocator()->init(TensorInfo(TensorShape(227U, 227U, 3U, _batches), 1, _data_type, _fixed_point_position)); output.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type, _fixed_point_position)); // Initialize intermediate tensors // Layer 1 conv1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position)); act1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position)); norm1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position)); pool1_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 96U, _batches), 1, _data_type, _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, _data_type, _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, _data_type, _fixed_point_position)); norm2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type, _fixed_point_position)); pool2_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type, _fixed_point_position)); // Layer 3 conv3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position)); act3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _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, _data_type, _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, _data_type, _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, _data_type, _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, _data_type, _fixed_point_position)); pool5_out.allocator()->init(TensorInfo(TensorShape(6U, 6U, 256U, _batches), 1, _data_type, _fixed_point_position)); // Layer 6 fc6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position)); act6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position)); // Layer 7 fc7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position)); act7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position)); // Layer 8 fc8_out.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type, _fixed_point_position)); // Configure Layers // Layer 1 TensorType *b0 = _reshaped_weights ? nullptr : &b[0]; conv1.configure(&input, &w[0], b0, &conv1_out, PadStrideInfo(4, 4, 0, 0), WeightsInfo(_reshaped_weights, 11U, 11U, 96U)); 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(), w11.get(), b11.get(), conv21_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U, 5U, 128U)); conv22.configure(pool12_out.get(), w12.get(), b12.get(), conv22_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U, 5U, 128U)); 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 TensorType *b2 = (_reshaped_weights && !_is_direct_conv) ? nullptr : &b[2]; conv3.configure(&pool2_out, &w[2], b2, &conv3_out, PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 384U)); act3.configure(&conv3_out, &act3_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // Layer 4 conv41.configure(act31_out.get(), w31.get(), b31.get(), conv41_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 192U)); conv42.configure(act32_out.get(), w32.get(), b32.get(), conv42_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 192U)); act4.configure(&conv4_out, &act4_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // Layer 5 conv51.configure(act41_out.get(), w41.get(), b41.get(), conv51_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 128U)); conv52.configure(act42_out.get(), w42.get(), b42.get(), conv52_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 128U)); 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], &b[5], &fc6_out, true, _reshaped_weights); act6.configure(&fc6_out, &act6_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // Layer 7 fc7.configure(&act6_out, &w[6], &b[6], &fc7_out, true, _reshaped_weights); act7.configure(&fc7_out, &act7_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // Layer 8 fc8.configure(&act7_out, &w[7], &b[7], &fc8_out, true, _reshaped_weights); // Softmax smx.configure(&fc8_out, &output); } /** Allocate the network */ void allocate() { input.allocator()->allocate(); output.allocator()->allocate(); if(!_reshaped_weights) { for(auto &wi : w) { wi.allocator()->allocate(); } for(auto &bi : b) { bi.allocator()->allocate(); } } else { w[0].allocator()->allocate(); w[2].allocator()->allocate(); w[5].allocator()->allocate(); w[6].allocator()->allocate(); w[7].allocator()->allocate(); b[5].allocator()->allocate(); b[6].allocator()->allocate(); b[7].allocator()->allocate(); if(!_is_direct_conv) { dynamic_cast(w11.get())->allocator()->allocate(); dynamic_cast(w12.get())->allocator()->allocate(); dynamic_cast(w31.get())->allocator()->allocate(); dynamic_cast(w32.get())->allocator()->allocate(); dynamic_cast(w41.get())->allocator()->allocate(); dynamic_cast(w42.get())->allocator()->allocate(); } else { b[1].allocator()->allocate(); b[2].allocator()->allocate(); b[3].allocator()->allocate(); b[4].allocator()->allocate(); w[1].allocator()->allocate(); w[3].allocator()->allocate(); w[4].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); if(!_is_direct_conv) { library->fill_tensor_uniform(Accessor(*dynamic_cast(w11.get())), 9); library->fill_tensor_uniform(Accessor(*dynamic_cast(w12.get())), 10); library->fill_tensor_uniform(Accessor(*dynamic_cast(w31.get())), 11); library->fill_tensor_uniform(Accessor(*dynamic_cast(w32.get())), 12); library->fill_tensor_uniform(Accessor(*dynamic_cast(w41.get())), 13); library->fill_tensor_uniform(Accessor(*dynamic_cast(w42.get())), 14); } else { library->fill_tensor_uniform(Accessor(w[1]), 9); library->fill_tensor_uniform(Accessor(b[1]), 10); library->fill_tensor_uniform(Accessor(w[3]), 11); library->fill_tensor_uniform(Accessor(b[3]), 12); library->fill_tensor_uniform(Accessor(w[4]), 13); library->fill_tensor_uniform(Accessor(b[4]), 14); } } } /** 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); } /** 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() { // Free allocations input.allocator()->free(); output.allocator()->free(); if(!_reshaped_weights) { for(auto &wi : w) { wi.allocator()->free(); } for(auto &bi : b) { bi.allocator()->free(); } } else { w[0].allocator()->free(); w[2].allocator()->free(); w[5].allocator()->free(); w[6].allocator()->free(); w[7].allocator()->free(); b[5].allocator()->free(); b[6].allocator()->free(); b[7].allocator()->free(); if(_is_direct_conv) { w[3].allocator()->free(); w[4].allocator()->free(); b[2].allocator()->free(); b[3].allocator()->free(); b[4].allocator()->free(); } } w11.reset(); w12.reset(); b11.reset(); b11.reset(); w31.reset(); w32.reset(); b31.reset(); b32.reset(); w41.reset(); w42.reset(); b41.reset(); b42.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(); } /** Sync the results */ void sync() { sync_if_necessary(); sync_tensor_if_necessary(output); } private: struct DirectConv { template typename std::enable_if < !std::is_same::value, void >::type configure(ITensorType *input, const ITensorType *weights, const ITensorType *biases, ITensorType *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo()) { _func.configure(input, weights, biases, output, conv_info); } template typename std::enable_if::value, void>::type configure(ITensorType *input, const ITensorType *weights, const ITensorType *biases, ITensorType *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo()) { _func.configure(input, weights, biases, output, conv_info, weights_info); } void run() { _func.run(); } DirectConvolutionLayerFunction _func{}; }; DataType _data_type{ DataType::UNKNOWN }; int _fixed_point_position{ 0 }; unsigned int _batches{ 0 }; bool _reshaped_weights{ false }; bool _is_direct_conv{ !std::is_same::value }; ActivationLayerFunction act1{}, act2{}, act3{}, act4{}, act5{}, act6{}, act7{}; ConvolutionLayerFunction conv1{}; DirectConv conv21{}, conv22{}, conv3{}, conv41{}, conv42{}, conv51{}, conv52{}; FullyConnectedLayerFunction fc6{}, fc7{}, fc8{}; NormalizationLayerFunction norm1{}, norm2{}; PoolingLayerFunction pool1{}, pool2{}, pool5{}; SoftmaxLayerFunction smx{}; TensorType input{}, output{}; std::array w{ {} }, b{ {} }; std::unique_ptr w11{ nullptr }, w12{ nullptr }, b11{ nullptr }, b12{ nullptr }; std::unique_ptr w31{ nullptr }, w32{ nullptr }, b31{ nullptr }, b32{ nullptr }; std::unique_ptr w41{ nullptr }, w42{ nullptr }, b41{ nullptr }, b42{ 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{}, pool12_out{}; std::unique_ptr conv21_out{}, conv22_out{}; std::unique_ptr act31_out{}, act32_out{}; std::unique_ptr conv41_out{}, conv42_out{}, act41_out{}, act42_out{}; std::unique_ptr conv51_out{}, conv52_out{}; }; } // namespace networks } // namespace test } // namespace arm_compute #endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__