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Diffstat (limited to 'tests/validation_old/model_objects/LeNet5.h')
| -rw-r--r-- | tests/validation_old/model_objects/LeNet5.h | 278 |
1 files changed, 0 insertions, 278 deletions
diff --git a/tests/validation_old/model_objects/LeNet5.h b/tests/validation_old/model_objects/LeNet5.h deleted file mode 100644 index d3e72b0010..0000000000 --- a/tests/validation_old/model_objects/LeNet5.h +++ /dev/null @@ -1,278 +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_LENET5_H__ -#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_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 -{ -/** Lenet5 model object */ -template <typename TensorType, - typename Accessor, - typename ActivationLayerFunction, - typename ConvolutionLayerFunction, - typename FullyConnectedLayerFunction, - typename PoolingLayerFunction, - typename SoftmaxLayerFunction> -class LeNet5 -{ -public: - /** Initialize and build the model. - * - * @param batches Number of batches should handle - */ - void build(unsigned int batches) - { - // Initialize input, output, weights and biases - input.allocator()->init(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32)); - output.allocator()->init(TensorInfo(TensorShape(10U, batches), 1, DataType::F32)); - w[0].allocator()->init(TensorInfo(TensorShape(5U, 5U, 1U, 20U), 1, DataType::F32)); - b[0].allocator()->init(TensorInfo(TensorShape(20U), 1, DataType::F32)); - w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 20U, 50U), 1, DataType::F32)); - b[1].allocator()->init(TensorInfo(TensorShape(50U), 1, DataType::F32)); - w[2].allocator()->init(TensorInfo(TensorShape(800U, 500U), 1, DataType::F32)); - b[2].allocator()->init(TensorInfo(TensorShape(500U), 1, DataType::F32)); - w[3].allocator()->init(TensorInfo(TensorShape(500U, 10U), 1, DataType::F32)); - b[3].allocator()->init(TensorInfo(TensorShape(10U), 1, DataType::F32)); - - // Initialize intermediate tensors - // Layer 1 - conv1_out.allocator()->init(TensorInfo(TensorShape(24U, 24U, 20U, batches), 1, DataType::F32)); - pool1_out.allocator()->init(TensorInfo(TensorShape(12U, 12U, 20U, batches), 1, DataType::F32)); - // Layer 2 - conv2_out.allocator()->init(TensorInfo(TensorShape(8U, 8U, 50U, batches), 1, DataType::F32)); - pool2_out.allocator()->init(TensorInfo(TensorShape(4U, 4U, 50U, batches), 1, DataType::F32)); - // Layer 3 - fc1_out.allocator()->init(TensorInfo(TensorShape(500U, batches), 1, DataType::F32)); - act1_out.allocator()->init(TensorInfo(TensorShape(500U, batches), 1, DataType::F32)); - // Layer 6 - fc2_out.allocator()->init(TensorInfo(TensorShape(10U, batches), 1, DataType::F32)); - - // Allocate layers - { - // Layer 1 - conv1 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction()); - pool1 = std::unique_ptr<PoolingLayerFunction>(new PoolingLayerFunction()); - // Layer 2 - conv2 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction()); - pool2 = std::unique_ptr<PoolingLayerFunction>(new PoolingLayerFunction()); - // Layer 3 - fc1 = std::unique_ptr<FullyConnectedLayerFunction>(new FullyConnectedLayerFunction()); - act1 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction()); - // Layer 4 - fc2 = std::unique_ptr<FullyConnectedLayerFunction>(new FullyConnectedLayerFunction()); - // Softmax - smx = std::unique_ptr<SoftmaxLayerFunction>(new SoftmaxLayerFunction()); - } - - // Configure Layers - { - conv1->configure(&input, &w[0], &b[0], &conv1_out, PadStrideInfo(1, 1, 0, 0)); - pool1->configure(&conv1_out, &pool1_out, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))); - conv2->configure(&pool1_out, &w[1], &b[1], &conv2_out, PadStrideInfo(1, 1, 0, 0)); - pool2->configure(&conv2_out, &pool2_out, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))); - fc1->configure(&pool2_out, &w[2], &b[2], &fc1_out); - act1->configure(&fc1_out, &act1_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - fc2->configure(&act1_out, &w[3], &b[3], &fc2_out); - smx->configure(&fc2_out, &output); - } - - // Allocate tensors - { - input.allocator()->allocate(); - output.allocator()->allocate(); - for(auto &wi : w) - { - wi.allocator()->allocate(); - } - for(auto &bi : b) - { - bi.allocator()->allocate(); - } - conv1_out.allocator()->allocate(); - pool1_out.allocator()->allocate(); - conv2_out.allocator()->allocate(); - pool2_out.allocator()->allocate(); - fc1_out.allocator()->allocate(); - act1_out.allocator()->allocate(); - fc2_out.allocator()->allocate(); - } - } - - /** Fills the trainable parameters and input with random data. */ - void fill_random() - { - std::uniform_real_distribution<> distribution(-1, 1); - library->fill(Accessor(input), distribution, 0); - for(unsigned int i = 0; i < w.size(); ++i) - { - library->fill(Accessor(w[i]), distribution, i + 1); - library->fill(Accessor(b[i]), distribution, i + 10); - } - } - -#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()); - - 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(); - pool1.reset(); - conv2.reset(); - pool2.reset(); - fc1.reset(); - act1.reset(); - fc2.reset(); - smx.reset(); - - input.allocator()->free(); - output.allocator()->free(); - for(auto &wi : w) - { - wi.allocator()->free(); - } - for(auto &bi : b) - { - bi.allocator()->free(); - } - - conv1_out.allocator()->free(); - pool1_out.allocator()->free(); - conv2_out.allocator()->free(); - pool2_out.allocator()->free(); - fc1_out.allocator()->free(); - act1_out.allocator()->free(); - fc2_out.allocator()->free(); - } - - /** Runs the model */ - void run() - { - // Layer 1 - conv1->run(); - pool1->run(); - // Layer 2 - conv2->run(); - pool2->run(); - // Layer 3 - fc1->run(); - act1->run(); - // Layer 4 - fc2->run(); - // Softmax - smx->run(); - } - -private: - std::unique_ptr<ActivationLayerFunction> act1{ nullptr }; - std::unique_ptr<ConvolutionLayerFunction> conv1{ nullptr }, conv2{ nullptr }; - std::unique_ptr<FullyConnectedLayerFunction> fc1{ nullptr }, fc2{ nullptr }; - std::unique_ptr<PoolingLayerFunction> pool1{ nullptr }, pool2{ nullptr }; - std::unique_ptr<SoftmaxLayerFunction> smx{ nullptr }; - - TensorType input{}, output{}; - std::array<TensorType, 4> w{}, b{}; - - TensorType conv1_out{}, pool1_out{}; - TensorType conv2_out{}, pool2_out{}; - TensorType fc1_out{}, act1_out{}; - TensorType fc2_out{}; -}; -} // namespace model_objects -} // namespace test -} // namespace arm_compute -#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__ |