| /* |
| * 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 "Globals.h" |
| #include "TensorLibrary.h" |
| #include "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 FullyConnectedLayerFunction, |
| typename NormalizationLayerFunction, |
| typename PoolingLayerFunction, |
| typename SoftmaxLayerFunction> |
| class AlexNetNetwork |
| { |
| public: |
| 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) |
| { |
| init_weights(); |
| } |
| else |
| { |
| init_reshaped_weights(); |
| } |
| } |
| |
| 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<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, _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, _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<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, _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, _data_type, _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, _data_type, _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, _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)); |
| 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)); |
| conv22.configure(pool12_out.get(), w22.get(), b22.get(), conv22_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U, 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 |
| TensorType *b2 = _reshaped_weights ? nullptr : &b[2]; |
| conv3.configure(&pool2_out, &w[2], b2, &conv3_out, PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 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, 3U)); |
| conv42.configure(act32_out.get(), w42.get(), b42.get(), conv42_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 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, 3U)); |
| conv52.configure(act42_out.get(), w52.get(), b52.get(), conv52_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 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], &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); |
| } |
| |
| 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(); |
| |
| 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() |
| { |
| // 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(); |
| } |
| |
| 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: |
| void init_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)); |
| |
| w21 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates())); |
| w22 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128))); |
| b21 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates())); |
| b22 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128))); |
| |
| w41 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates())); |
| w42 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192))); |
| b41 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates())); |
| b42 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192))); |
| |
| w51 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates())); |
| w52 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128))); |
| b51 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates())); |
| b52 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128))); |
| } |
| |
| void init_reshaped_weights() |
| { |
| const unsigned int data_type_size = 16 / arm_compute::data_size_from_type(_data_type); |
| |
| // Create tensor for the reshaped weights |
| auto w21_tensor = std::unique_ptr<TensorType>(new TensorType()); |
| auto w22_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()); |
| 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 * data_type_size, 96U / data_type_size), 1, _data_type, _fixed_point_position)); |
| w21_tensor->allocator()->init(TensorInfo(TensorShape(1248U * data_type_size, 128U / data_type_size), 1, _data_type, _fixed_point_position)); |
| w22_tensor->allocator()->init(TensorInfo(TensorShape(1248U * data_type_size, 128U / data_type_size), 1, _data_type, _fixed_point_position)); |
| w[2].allocator()->init(TensorInfo(TensorShape(2560U * data_type_size, 384U / data_type_size), 1, _data_type, _fixed_point_position)); |
| w41_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 192U / data_type_size), 1, _data_type, _fixed_point_position)); |
| w42_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 192U / data_type_size), 1, _data_type, _fixed_point_position)); |
| w51_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 128U / data_type_size), 1, _data_type, _fixed_point_position)); |
| w52_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 128U / data_type_size), 1, _data_type, _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); |
| |
| 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) |
| { |
| w[5].allocator()->init(TensorInfo(TensorShape(9216U * data_type_size, 4096U / data_type_size), 1, _data_type, _fixed_point_position)); |
| w[6].allocator()->init(TensorInfo(TensorShape(4096U * data_type_size, 4096U / data_type_size), 1, _data_type, _fixed_point_position)); |
| w[7].allocator()->init(TensorInfo(TensorShape(4096U * data_type_size, 1000U / data_type_size), 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)); |
| } |
| } |
| |
| DataType _data_type{ DataType::UNKNOWN }; |
| int _fixed_point_position{ 0 }; |
| unsigned int _batches{ 0 }; |
| bool _reshaped_weights{ false }; |
| |
| ActivationLayerFunction act1{}, act2{}, act3{}, act4{}, act5{}, act6{}, act7{}; |
| ConvolutionLayerFunction conv1{}, 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> 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{}, 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__ |