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Diffstat (limited to 'tests/validation_old/model_objects/AlexNet.h')
| -rw-r--r-- | tests/validation_old/model_objects/AlexNet.h | 585 |
1 files changed, 0 insertions, 585 deletions
diff --git a/tests/validation_old/model_objects/AlexNet.h b/tests/validation_old/model_objects/AlexNet.h deleted file mode 100644 index 45622e2118..0000000000 --- a/tests/validation_old/model_objects/AlexNet.h +++ /dev/null @@ -1,585 +0,0 @@ -/* - * 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 "arm_compute/runtime/Tensor.h" - -#include "tests/AssetsLibrary.h" -#include "tests/Globals.h" -#include "tests/Utils.h" - -#include <memory> - -using namespace arm_compute; -using namespace arm_compute::test; - -namespace arm_compute -{ -namespace test -{ -namespace model_objects -{ -/** AlexNet model object */ -template <typename ITensorType, - typename TensorType, - typename SubTensorType, - typename Accessor, - typename ActivationLayerFunction, - typename ConvolutionLayerFunction, - typename FullyConnectedLayerFunction, - typename NormalizationLayerFunction, - typename PoolingLayerFunction, - typename SoftmaxLayerFunction, - DataType dt = DataType::F32, - int fixed_point_position = 4> -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<TensorType>(new TensorType()); - } - for(auto &bi : b) - { - bi = std::unique_ptr<TensorType>(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<SubTensorType>(new SubTensorType(w[1].get(), TensorShape(5U, 5U, 48U, 128U), Coordinates())); - w22 = std::unique_ptr<SubTensorType>(new SubTensorType(w[1].get(), TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128))); - b21 = std::unique_ptr<SubTensorType>(new SubTensorType(b[1].get(), TensorShape(128U), Coordinates())); - b22 = std::unique_ptr<SubTensorType>(new SubTensorType(b[1].get(), TensorShape(128U), Coordinates(128))); - - w41 = std::unique_ptr<SubTensorType>(new SubTensorType(w[3].get(), TensorShape(3U, 3U, 192U, 192U), Coordinates())); - w42 = std::unique_ptr<SubTensorType>(new SubTensorType(w[3].get(), TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192))); - b41 = std::unique_ptr<SubTensorType>(new SubTensorType(b[3].get(), TensorShape(192U), Coordinates())); - b42 = std::unique_ptr<SubTensorType>(new SubTensorType(b[3].get(), TensorShape(192U), Coordinates(192))); - - w51 = std::unique_ptr<SubTensorType>(new SubTensorType(w[4].get(), TensorShape(3U, 3U, 192U, 128U), Coordinates())); - w52 = std::unique_ptr<SubTensorType>(new SubTensorType(w[4].get(), TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128))); - b51 = std::unique_ptr<SubTensorType>(new SubTensorType(b[4].get(), TensorShape(128U), Coordinates())); - b52 = std::unique_ptr<SubTensorType>(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<TensorType>(new TensorType()); - auto w21_tensor = std::unique_ptr<TensorType>(new TensorType()); - auto w22_tensor = std::unique_ptr<TensorType>(new TensorType()); - w[2] = std::unique_ptr<TensorType>(new TensorType()); - auto w41_tensor = std::unique_ptr<TensorType>(new TensorType()); - auto w42_tensor = std::unique_ptr<TensorType>(new TensorType()); - auto w51_tensor = std::unique_ptr<TensorType>(new TensorType()); - auto w52_tensor = std::unique_ptr<TensorType>(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<TensorType>(new TensorType()); - w[6] = std::unique_ptr<TensorType>(new TensorType()); - w[7] = std::unique_ptr<TensorType>(new TensorType()); - b[5] = std::unique_ptr<TensorType>(new TensorType()); - b[6] = std::unique_ptr<TensorType>(new TensorType()); - b[7] = std::unique_ptr<TensorType>(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 && std::is_same<TensorType, Tensor>::value) - { - 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<SubTensorType>(new SubTensorType(&pool1_out, TensorShape(27U, 27U, 48U, _batches), Coordinates())); - pool12_out = std::unique_ptr<SubTensorType>(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<SubTensorType>(new SubTensorType(&conv2_out, TensorShape(27U, 27U, 128U, _batches), Coordinates())); - conv22_out = std::unique_ptr<SubTensorType>(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<SubTensorType>(new SubTensorType(&act3_out, TensorShape(13U, 13U, 192U, _batches), Coordinates())); - act32_out = std::unique_ptr<SubTensorType>(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<SubTensorType>(new SubTensorType(&conv4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates())); - conv42_out = std::unique_ptr<SubTensorType>(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<SubTensorType>(new SubTensorType(&act4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates())); - act42_out = std::unique_ptr<SubTensorType>(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<SubTensorType>(new SubTensorType(&conv5_out, TensorShape(13U, 13U, 128U, _batches), Coordinates())); - conv52_out = std::unique_ptr<SubTensorType>(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<ConvolutionLayerFunction>(new ConvolutionLayerFunction()); - act1 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction()); - norm1 = std::unique_ptr<NormalizationLayerFunction>(new NormalizationLayerFunction()); - pool1 = std::unique_ptr<PoolingLayerFunction>(new PoolingLayerFunction()); - // Layer 2 - conv21 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction()); - conv22 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction()); - act2 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction()); - norm2 = std::unique_ptr<NormalizationLayerFunction>(new NormalizationLayerFunction()); - pool2 = std::unique_ptr<PoolingLayerFunction>(new PoolingLayerFunction()); - // Layer 3 - conv3 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction()); - act3 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction()); - // Layer 4 - conv41 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction()); - conv42 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction()); - act4 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction()); - // Layer 5 - conv51 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction()); - conv52 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction()); - act5 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction()); - pool5 = std::unique_ptr<PoolingLayerFunction>(new PoolingLayerFunction()); - // Layer 6 - fc6 = std::unique_ptr<FullyConnectedLayerFunction>(new FullyConnectedLayerFunction()); - act6 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction()); - // Layer 7 - fc7 = std::unique_ptr<FullyConnectedLayerFunction>(new FullyConnectedLayerFunction()); - act7 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction()); - // Layer 8 - fc8 = std::unique_ptr<FullyConnectedLayerFunction>(new FullyConnectedLayerFunction()); - // Softmax - smx = std::unique_ptr<SoftmaxLayerFunction>(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, 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(), w21.get(), b21.get(), conv21_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U, 5U, 128U)); - conv22->configure(pool12_out.get(), w22.get(), b22.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 - conv3->configure(&pool2_out, w[2].get(), b[2].get(), &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(), w41.get(), b41.get(), conv41_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 192U)); - conv42->configure(act32_out.get(), w42.get(), b42.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(), w51.get(), b51.get(), conv51_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 128U)); - conv52->configure(act42_out.get(), w52.get(), b52.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].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<TensorType *>(w21.get())->allocator()->allocate(); - dynamic_cast<TensorType *>(w22.get())->allocator()->allocate(); - dynamic_cast<TensorType *>(w41.get())->allocator()->allocate(); - dynamic_cast<TensorType *>(w42.get())->allocator()->allocate(); - dynamic_cast<TensorType *>(w51.get())->allocator()->allocate(); - dynamic_cast<TensorType *>(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<TensorType *>(w21.get())), 9); - library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w22.get())), 10); - library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w41.get())), 11); - library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w42.get())), 12); - library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w51.get())), 13); - library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(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<std::string> weights, std::vector<std::string> 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<unsigned int> get_classifications() - { - std::vector<unsigned int> 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<const float *>(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<ActivationLayerFunction> act1{ nullptr }, act2{ nullptr }, act3{ nullptr }, act4{ nullptr }, act5{ nullptr }, act6{ nullptr }, act7{ nullptr }; - std::unique_ptr<ConvolutionLayerFunction> conv1{ nullptr }, conv21{ nullptr }, conv22{ nullptr }, conv3{ nullptr }, conv41{ nullptr }, conv42{ nullptr }, conv51{ nullptr }, conv52{ nullptr }; - std::unique_ptr<FullyConnectedLayerFunction> fc6{ nullptr }, fc7{ nullptr }, fc8{}; - std::unique_ptr<NormalizationLayerFunction> norm1{ nullptr }, norm2{ nullptr }; - std::unique_ptr<PoolingLayerFunction> pool1{ nullptr }, pool2{ nullptr }, pool5{ nullptr }; - std::unique_ptr<SoftmaxLayerFunction> smx{ nullptr }; - - TensorType input{}, output{}; - std::array<std::unique_ptr<TensorType>, 8> w{}, b{}; - std::unique_ptr<ITensorType> w21{ nullptr }, w22{ nullptr }, b21{ nullptr }, b22{ nullptr }; - std::unique_ptr<ITensorType> w41{ nullptr }, w42{ nullptr }, b41{ nullptr }, b42{ nullptr }; - std::unique_ptr<ITensorType> 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<SubTensorType> pool11_out{ nullptr }, pool12_out{ nullptr }; - std::unique_ptr<SubTensorType> conv21_out{ nullptr }, conv22_out{ nullptr }; - std::unique_ptr<SubTensorType> act31_out{ nullptr }, act32_out{ nullptr }; - std::unique_ptr<SubTensorType> conv41_out{ nullptr }, conv42_out{ nullptr }, act41_out{ nullptr }, act42_out{ nullptr }; - std::unique_ptr<SubTensorType> conv51_out{ nullptr }, conv52_out{ nullptr }; -}; -} // namespace model_objects -} // namespace test -} // namespace arm_compute -#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__ |