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Diffstat (limited to 'tests/networks/AlexNetNetwork.h')
| -rw-r--r-- | tests/networks/AlexNetNetwork.h | 646 |
1 files changed, 0 insertions, 646 deletions
diff --git a/tests/networks/AlexNetNetwork.h b/tests/networks/AlexNetNetwork.h deleted file mode 100644 index e15db2a110..0000000000 --- a/tests/networks/AlexNetNetwork.h +++ /dev/null @@ -1,646 +0,0 @@ -/* - * 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 <memory> - -namespace arm_compute -{ -namespace test -{ -namespace networks -{ -/** AlexNet model object */ -template <typename ITensorType, - typename TensorType, - typename SubTensorType, - typename Accessor, - typename ActivationLayerFunction, - typename ConvolutionLayerFunction, - typename DirectConvolutionLayerFunction, - typename FullyConnectedLayerFunction, - typename NormalizationLayerFunction, - typename PoolingLayerFunction, - typename SoftmaxLayerFunction> -class AlexNetNetwork -{ -public: - /** Initialize the network. - * - * @param[in] data_type Data type. - * @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 batches, bool reshaped_weights = false) - { - _data_type = data_type; - _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)); - b[0].allocator()->init(TensorInfo(TensorShape(96U), 1, _data_type)); - w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type)); - b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type)); - w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type)); - b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type)); - w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type)); - b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type)); - w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type)); - b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type)); - w[5].allocator()->init(TensorInfo(TensorShape(9216U, 4096U), 1, _data_type)); - b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type)); - w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type)); - b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type)); - w[7].allocator()->init(TensorInfo(TensorShape(4096U, 1000U), 1, _data_type)); - b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type)); - - w11 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates())); - w12 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128))); - b11 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(), true)); - b12 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128), true)); - - w31 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates())); - w32 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192))); - b31 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(), true)); - b32 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192), true)); - - w41 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates())); - w42 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128))); - b41 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(), true)); - b42 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128), true)); - } - else - { - auto reshape = [&](unsigned int width, unsigned int height, bool convolution_layer) -> TensorShape - { - const bool is_optimised = std::is_same<ITensorType, ITensor>::value && 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<unsigned int>(std::ceil(static_cast<float>(height) / interleave_width)) }; - } - }; - - // Create tensor for the reshaped weights - w[0].allocator()->init(TensorInfo(reshape(366U, 96U, true), 1, _data_type)); - - // Configure the direct convolution's weights. Direct convolution doesn't need reshape weights - if(!_is_direct_conv) - { - auto w11_tensor = std::unique_ptr<TensorType>(new TensorType()); - auto w12_tensor = std::unique_ptr<TensorType>(new TensorType()); - auto w31_tensor = std::unique_ptr<TensorType>(new TensorType()); - auto w32_tensor = std::unique_ptr<TensorType>(new TensorType()); - auto w41_tensor = std::unique_ptr<TensorType>(new TensorType()); - auto w42_tensor = std::unique_ptr<TensorType>(new TensorType()); - w11_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U, true), 1, _data_type)); - w12_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U, true), 1, _data_type)); - w31_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U, true), 1, _data_type)); - w32_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U, true), 1, _data_type)); - w41_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U, true), 1, _data_type)); - w42_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U, true), 1, _data_type)); - w[2].allocator()->init(TensorInfo(reshape(2560U, 384U, true), 1, _data_type)); - 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)); - b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type)); - w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type)); - b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type)); - w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type)); - b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type)); - w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type)); - b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type)); - w11 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates())); - w12 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128))); - b11 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates())); - b12 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128))); - - w31 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates())); - w32 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192))); - b31 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates())); - b32 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192))); - - w41 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates())); - w42 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128))); - b41 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates())); - b42 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128))); - } - - b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type)); - b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type)); - b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type)); - - if(_batches > 1 && std::is_same<TensorType, Tensor>::value) - { - w[5].allocator()->init(TensorInfo(reshape(9216U, 4096U, false), 1, _data_type)); - w[6].allocator()->init(TensorInfo(reshape(4096U, 4096U, false), 1, _data_type)); - w[7].allocator()->init(TensorInfo(reshape(4096U, 1000U, false), 1, _data_type)); - } - else - { - w[5].allocator()->init(TensorInfo(TensorShape(4096U, 9216U), 1, _data_type)); - w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type)); - w[7].allocator()->init(TensorInfo(TensorShape(1000U, 4096U), 1, _data_type)); - } - } - } - - /** Build the network */ - void build() - { - FullyConnectedLayerInfo fc_info; - fc_info.are_weights_reshaped = _reshaped_weights; - - input.allocator()->init(TensorInfo(TensorShape(227U, 227U, 3U, _batches), 1, _data_type)); - output.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type)); - - // Initialize intermediate tensors - // Layer 1 - conv1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type)); - act1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type)); - norm1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type)); - pool1_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 96U, _batches), 1, _data_type)); - 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, _data_type)); - 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, _data_type)); - norm2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type)); - pool2_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type)); - // Layer 3 - conv3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type)); - act3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type)); - 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, _data_type)); - 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, _data_type)); - 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, _data_type)); - 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, _data_type)); - pool5_out.allocator()->init(TensorInfo(TensorShape(6U, 6U, 256U, _batches), 1, _data_type)); - // Layer 6 - fc6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type)); - act6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type)); - // Layer 7 - fc7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type)); - act7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type)); - // Layer 8 - fc8_out.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type)); - - // 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, fc_info); - act6.configure(&fc6_out, &act6_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - // Layer 7 - fc7.configure(&act6_out, &w[6], &b[6], &fc7_out, fc_info); - act7.configure(&fc7_out, &act7_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - // Layer 8 - fc8.configure(&act7_out, &w[7], &b[7], &fc8_out, fc_info); - // 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<TensorType *>(w11.get())->allocator()->allocate(); - dynamic_cast<TensorType *>(w12.get())->allocator()->allocate(); - dynamic_cast<TensorType *>(w31.get())->allocator()->allocate(); - dynamic_cast<TensorType *>(w32.get())->allocator()->allocate(); - dynamic_cast<TensorType *>(w41.get())->allocator()->allocate(); - dynamic_cast<TensorType *>(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<TensorType *>(w11.get())), 9); - library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w12.get())), 10); - library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w31.get())), 11); - library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w32.get())), 12); - library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w41.get())), 13); - library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(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<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); - } - - /** 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() - { - // 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<TensorType>(); - sync_tensor_if_necessary<TensorType>(output); - } - -private: - struct DirectConv - { - template <typename ConvolutionLayerFunction1 = ConvolutionLayerFunction, typename DirectConvolutionLayerFunction1 = DirectConvolutionLayerFunction> - typename std::enable_if < !std::is_same<ConvolutionLayerFunction1, DirectConvolutionLayerFunction1>::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 ConvolutionLayerFunction1 = ConvolutionLayerFunction, typename DirectConvolutionLayerFunction1 = DirectConvolutionLayerFunction> - typename std::enable_if<std::is_same<ConvolutionLayerFunction1, DirectConvolutionLayerFunction1>::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 }; - unsigned int _batches{ 0 }; - bool _reshaped_weights{ false }; - bool _is_direct_conv{ !std::is_same<ConvolutionLayerFunction, DirectConvolutionLayerFunction>::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<TensorType, 8> w{ {} }, b{ {} }; - std::unique_ptr<ITensorType> w11{ nullptr }, w12{ nullptr }, b11{ nullptr }, b12{ nullptr }; - std::unique_ptr<ITensorType> w31{ nullptr }, w32{ nullptr }, b31{ nullptr }, b32{ nullptr }; - std::unique_ptr<ITensorType> 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<SubTensorType> pool11_out{}, pool12_out{}; - std::unique_ptr<SubTensorType> conv21_out{}, conv22_out{}; - std::unique_ptr<SubTensorType> act31_out{}, act32_out{}; - std::unique_ptr<SubTensorType> conv41_out{}, conv42_out{}, act41_out{}, act42_out{}; - std::unique_ptr<SubTensorType> conv51_out{}, conv52_out{}; -}; -} // namespace networks -} // namespace test -} // namespace arm_compute -#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__ |