diff options
Diffstat (limited to 'tests/model_objects/LeNet5.h')
| -rw-r--r-- | tests/model_objects/LeNet5.h | 277 |
1 files changed, 277 insertions, 0 deletions
diff --git a/tests/model_objects/LeNet5.h b/tests/model_objects/LeNet5.h new file mode 100644 index 0000000000..7d5090f5d0 --- /dev/null +++ b/tests/model_objects/LeNet5.h @@ -0,0 +1,277 @@ +/* + * 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 "TensorLibrary.h" +#include "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__ |