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Diffstat (limited to 'tests/networks/MobileNetNetwork.h')
| -rw-r--r-- | tests/networks/MobileNetNetwork.h | 314 |
1 files changed, 0 insertions, 314 deletions
diff --git a/tests/networks/MobileNetNetwork.h b/tests/networks/MobileNetNetwork.h deleted file mode 100644 index ec054b237e..0000000000 --- a/tests/networks/MobileNetNetwork.h +++ /dev/null @@ -1,314 +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_MOBILENET_H__ -#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENET_H__ - -#include "tests/AssetsLibrary.h" -#include "tests/Globals.h" -#include "tests/Utils.h" - -#include "utils/Utils.h" - -#include <memory> - -using namespace arm_compute; -using namespace arm_compute::test; - -namespace arm_compute -{ -namespace test -{ -namespace networks -{ -/** MobileNet model object */ -template <typename TensorType, - typename Accessor, - typename ActivationLayerFunction, - typename ConvolutionLayerFunction, - typename DirectConvolutionLayerFunction, - typename DepthwiseConvolutionLayerFunction, - typename ReshapeFunction, - typename PoolingLayerFunction> -class MobileNetNetwork -{ -public: - /** Initialize the network. - * - * @param[in] batches Number of batches. - */ - void init(int batches) - { - _batches = batches; - - // Initialize input, output - input.allocator()->init(TensorInfo(TensorShape(224U, 224U, 3U, _batches), 1, DataType::F32)); - output.allocator()->init(TensorInfo(TensorShape(11U, _batches), 1, DataType::F32)); - // Initialize weights and biases - w_conv3x3.allocator()->init(TensorInfo(TensorShape(3U, 3U, 3U, 16U), 1, DataType::F32)); - b_conv3x3.allocator()->init(TensorInfo(TensorShape(16U), 1, DataType::F32)); - depthwise_conv_block_init(0, 16, 16); - depthwise_conv_block_init(1, 16, 32); - depthwise_conv_block_init(2, 32, 32); - depthwise_conv_block_init(3, 32, 64); - depthwise_conv_block_init(4, 64, 64); - depthwise_conv_block_init(5, 64, 128); - depthwise_conv_block_init(6, 128, 128); - depthwise_conv_block_init(7, 128, 128); - depthwise_conv_block_init(8, 128, 128); - depthwise_conv_block_init(9, 128, 128); - depthwise_conv_block_init(10, 128, 128); - depthwise_conv_block_init(11, 128, 256); - depthwise_conv_block_init(12, 256, 256); - w_conv[13].allocator()->init(TensorInfo(TensorShape(1U, 1U, 256U, 11U), 1, DataType::F32)); - b_conv[13].allocator()->init(TensorInfo(TensorShape(11U), 1, DataType::F32)); - } - - /** Build the model. */ - void build() - { - // Configure Layers - conv3x3.configure(&input, &w_conv3x3, &b_conv3x3, &conv_out[0], PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)); - conv3x3_act.configure(&conv_out[0], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)); - depthwise_conv_block_build(0, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(1, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(2, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(4, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(5, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(6, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(7, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(8, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(9, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(10, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(11, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - depthwise_conv_block_build(12, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)); - pool.configure(&conv_out[13], &pool_out, PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(2, 2, 0, 0))); - conv1x1[13].configure(&pool_out, &w_conv[13], &b_conv[13], &conv_out[14], PadStrideInfo(1, 1, 0, 0)); - logistic.configure(&conv_out[14], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); - reshape.configure(&conv_out[14], &output); - } - - /** Allocate the network. */ - void allocate() - { - input.allocator()->allocate(); - output.allocator()->allocate(); - - w_conv3x3.allocator()->allocate(); - b_conv3x3.allocator()->allocate(); - for(unsigned int i = 0; i < w_conv.size(); ++i) - { - w_conv[i].allocator()->allocate(); - b_conv[i].allocator()->allocate(); - } - for(unsigned int i = 0; i < w_dwc.size(); ++i) - { - w_dwc[i].allocator()->allocate(); - b_dwc[i].allocator()->allocate(); - } - for(auto &o : conv_out) - { - o.allocator()->allocate(); - } - for(auto &o : dwc_out) - { - o.allocator()->allocate(); - } - pool_out.allocator()->allocate(); - } - - /** Fills the trainable parameters and input with random data. */ - void fill_random() - { - unsigned int seed_idx = 0; - std::uniform_real_distribution<> distribution(-1, 1); - library->fill(Accessor(input), distribution, seed_idx++); - - library->fill(Accessor(w_conv3x3), distribution, seed_idx++); - library->fill(Accessor(b_conv3x3), distribution, seed_idx++); - for(unsigned int i = 0; i < w_conv.size(); ++i) - { - library->fill(Accessor(w_conv[i]), distribution, seed_idx++); - library->fill(Accessor(b_conv[i]), distribution, seed_idx++); - } - for(unsigned int i = 0; i < w_dwc.size(); ++i) - { - library->fill(Accessor(w_dwc[i]), distribution, seed_idx++); - library->fill(Accessor(b_dwc[i]), distribution, seed_idx++); - } - } - - /** 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() - { - input.allocator()->free(); - output.allocator()->free(); - - w_conv3x3.allocator()->free(); - b_conv3x3.allocator()->free(); - for(unsigned int i = 0; i < w_conv.size(); ++i) - { - w_conv[i].allocator()->free(); - b_conv[i].allocator()->free(); - } - for(unsigned int i = 0; i < w_dwc.size(); ++i) - { - w_dwc[i].allocator()->free(); - b_dwc[i].allocator()->free(); - } - for(auto &o : conv_out) - { - o.allocator()->free(); - } - for(auto &o : dwc_out) - { - o.allocator()->free(); - } - pool_out.allocator()->free(); - } - - /** Runs the model */ - void run() - { - conv3x3.run(); - conv3x3_act.run(); - depthwise_conv_block_run(0); - depthwise_conv_block_run(1); - depthwise_conv_block_run(2); - depthwise_conv_block_run(3); - depthwise_conv_block_run(4); - depthwise_conv_block_run(5); - depthwise_conv_block_run(6); - depthwise_conv_block_run(7); - depthwise_conv_block_run(8); - depthwise_conv_block_run(9); - depthwise_conv_block_run(10); - depthwise_conv_block_run(11); - depthwise_conv_block_run(12); - pool.run(); - conv1x1[13].run(); - logistic.run(); - reshape.run(); - } - - /** Sync the results */ - void sync() - { - sync_if_necessary<TensorType>(); - sync_tensor_if_necessary<TensorType>(output); - } - -private: - void depthwise_conv_block_init(unsigned int idx, unsigned int ifm, unsigned int ofm) - { - w_dwc[idx].allocator()->init(TensorInfo(TensorShape(3U, 3U, ifm), 1, DataType::F32)); - b_dwc[idx].allocator()->init(TensorInfo(TensorShape(ifm), 1, DataType::F32)); - w_conv[idx].allocator()->init(TensorInfo(TensorShape(1U, 1U, ifm, ofm), 1, DataType::F32)); - b_conv[idx].allocator()->init(TensorInfo(TensorShape(ofm), 1, DataType::F32)); - } - void depthwise_conv_block_build(unsigned int idx, PadStrideInfo dwc_ps, PadStrideInfo conv_ps) - { - dwc3x3[idx].configure(&conv_out[idx], &w_dwc[idx], &b_dwc[idx], &dwc_out[idx], dwc_ps); - act[2 * idx].configure(&dwc_out[idx], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)); - conv1x1[idx].configure(&dwc_out[idx], &w_conv[idx], &b_conv[idx], &conv_out[idx + 1], conv_ps); - act[2 * idx + 1].configure(&conv_out[idx], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)); - } - void depthwise_conv_block_run(unsigned int idx) - { - dwc3x3[idx].run(); - act[2 * idx].run(); - conv1x1[idx].run(); - act[2 * idx + 1].run(); - } - -private: - unsigned int _batches{ 0 }; - - ConvolutionLayerFunction conv3x3{}; - ActivationLayerFunction conv3x3_act{}; - std::array<ActivationLayerFunction, 26> act{ {} }; - std::array<DirectConvolutionLayerFunction, 14> conv1x1{ {} }; - std::array<DepthwiseConvolutionLayerFunction, 13> dwc3x3{ {} }; - PoolingLayerFunction pool{}; - ActivationLayerFunction logistic{}; - ReshapeFunction reshape{}; - - TensorType w_conv3x3{}, b_conv3x3{}; - std::array<TensorType, 14> w_conv{ {} }, b_conv{ {} }; - std::array<TensorType, 13> w_dwc{ {} }, b_dwc{ {} }; - - TensorType input{}, output{}; - - std::array<TensorType, 15> conv_out{ {} }; - std::array<TensorType, 13> dwc_out{ {} }; - TensorType pool_out{}; -}; -} // namespace networks -} // namespace test -} // namespace arm_compute -#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENET_H__ |