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Diffstat (limited to 'tests/model_objects/AlexNet.h')
| -rw-r--r-- | tests/model_objects/AlexNet.h | 582 |
1 files changed, 582 insertions, 0 deletions
diff --git a/tests/model_objects/AlexNet.h b/tests/model_objects/AlexNet.h new file mode 100644 index 0000000000..d49ef0645a --- /dev/null +++ b/tests/model_objects/AlexNet.h @@ -0,0 +1,582 @@ +/* + * 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 <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) + { + 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)); + 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<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__ |