From 93dcd83caacc01eef99c550bd50a8d5393e55f1c Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Fri, 27 Oct 2017 12:48:20 +0100 Subject: COMPMID-645: MobileNet network and benchmark Change-Id: Id9c50abdbd28e7e95fd460240cec51af1d6f1036 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/95580 Reviewed-by: Anthony Barbier Tested-by: Kaizen --- tests/benchmark/CL/SYSTEM/MobileNet.cpp | 62 ++++++ tests/benchmark/fixtures/MobileNetFixture.h | 79 ++++++++ tests/networks/MobileNetNetwork.h | 302 ++++++++++++++++++++++++++++ 3 files changed, 443 insertions(+) create mode 100644 tests/benchmark/CL/SYSTEM/MobileNet.cpp create mode 100644 tests/benchmark/fixtures/MobileNetFixture.h create mode 100644 tests/networks/MobileNetNetwork.h diff --git a/tests/benchmark/CL/SYSTEM/MobileNet.cpp b/tests/benchmark/CL/SYSTEM/MobileNet.cpp new file mode 100644 index 0000000000..c745a0acab --- /dev/null +++ b/tests/benchmark/CL/SYSTEM/MobileNet.cpp @@ -0,0 +1,62 @@ +/* + * 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. + */ +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/CL/CLTensorAllocator.h" +#include "arm_compute/runtime/CL/functions/CLActivationLayer.h" +#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" +#include "arm_compute/runtime/CL/functions/CLDepthwiseConvolution.h" +#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h" +#include "arm_compute/runtime/CL/functions/CLPoolingLayer.h" +#include "arm_compute/runtime/CL/functions/CLReshapeLayer.h" +#include "tests/CL/CLAccessor.h" +#include "tests/benchmark/fixtures/MobileNetFixture.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "utils/TypePrinter.h" + +namespace arm_compute +{ +namespace test +{ +using CLMobileNetFixture = MobileNetFixture; + +TEST_SUITE(CL) +TEST_SUITE(SYSTEM_TEST) + +REGISTER_FIXTURE_DATA_TEST_CASE(MobileNet, CLMobileNetFixture, framework::DatasetMode::ALL, + framework::dataset::make("Batches", { 1, 4, 8 })); + +TEST_SUITE_END() +TEST_SUITE_END() +} // namespace test +} // namespace arm_compute diff --git a/tests/benchmark/fixtures/MobileNetFixture.h b/tests/benchmark/fixtures/MobileNetFixture.h new file mode 100644 index 0000000000..6c1ee300c1 --- /dev/null +++ b/tests/benchmark/fixtures/MobileNetFixture.h @@ -0,0 +1,79 @@ +/* + * 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_MOBILENETFIXTURE +#define ARM_COMPUTE_TEST_MOBILENETFIXTURE + +#include "tests/AssetsLibrary.h" +#include "tests/Utils.h" +#include "tests/framework/Fixture.h" +#include "tests/networks/MobileNetNetwork.h" + +namespace arm_compute +{ +namespace test +{ +template +class MobileNetFixture : public framework::Fixture +{ +public: + template + void setup(int batches) + { + network.init(batches); + network.build(); + network.allocate(); + network.fill_random(); + } + + void run() + { + network.run(); + } + + void teardown() + { + network.clear(); + } + +private: + networks::MobileNetNetwork + network{}; +}; +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_MOBILENETFIXTURE */ diff --git a/tests/networks/MobileNetNetwork.h b/tests/networks/MobileNetNetwork.h new file mode 100644 index 0000000000..74dce0e348 --- /dev/null +++ b/tests/networks/MobileNetNetwork.h @@ -0,0 +1,302 @@ +/* + * 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_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 + +using namespace arm_compute; +using namespace arm_compute::test; + +namespace arm_compute +{ +namespace test +{ +namespace networks +{ +/** MobileNet model object */ +template +class MobileNetNetwork +{ +public: + 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); + } + + 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 get_classifications() + { + std::vector 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(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(); + } + +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 act{ {} }; + std::array conv1x1{ {} }; + std::array dwc3x3{ {} }; + PoolingLayerFunction pool{}; + ActivationLayerFunction logistic{}; + ReshapeFunction reshape{}; + + TensorType w_conv3x3{}, b_conv3x3{}; + std::array w_conv{ {} }, b_conv{ {} }; + std::array w_dwc{ {} }, b_dwc{ {} }; + + TensorType input{}, output{}; + + std::array conv_out{ {} }; + std::array dwc_out{ {} }; + TensorType pool_out{}; +}; +} // namespace networks +} // namespace test +} // namespace arm_compute +#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENET_H__ -- cgit v1.2.1