From bc4484acbbe3b6c1f06d25c1006ebd53f037767c Mon Sep 17 00:00:00 2001 From: Isabella Gottardi Date: Fri, 2 Feb 2018 11:27:32 +0000 Subject: COMPMID-889 - Implement Squeezenet 1.1 as a graph example Change-Id: I12d4af007c123b19925ceb5e3c84285e096bc13b Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118718 Tested-by: Jenkins Reviewed-by: Georgios Pinitas --- examples/graph_squeezenet_v1_1.cpp | 215 +++++++++++++++++++++++++++++++++++++ 1 file changed, 215 insertions(+) create mode 100644 examples/graph_squeezenet_v1_1.cpp (limited to 'examples/graph_squeezenet_v1_1.cpp') diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp new file mode 100644 index 0000000000..fad07e5043 --- /dev/null +++ b/examples/graph_squeezenet_v1_1.cpp @@ -0,0 +1,215 @@ +/* + * Copyright (c) 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. + */ +#include "arm_compute/graph/Graph.h" +#include "arm_compute/graph/Nodes.h" +#include "arm_compute/graph/SubGraph.h" +#include "support/ToolchainSupport.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +#include +#include + +using namespace arm_compute::utils; +using namespace arm_compute::graph; +using namespace arm_compute::graph_utils; +using namespace arm_compute::logging; + +namespace +{ +} // namespace + +/** Example demonstrating how to implement Squeezenet's v1.1 network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +class GraphSqueezenet_v1_1Example : public Example +{ +public: + void do_setup(int argc, char **argv) override + { + std::string data_path; /* Path to the trainable data */ + std::string image; /* Image data */ + std::string label; /* Label data */ + + constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */ + constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */ + constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */ + + // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON + TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0); + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 2) + { + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 3) + { + data_path = argv[2]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n"; + std::cout << "No image provided: using random values\n\n"; + } + else if(argc == 4) + { + data_path = argv[2]; + image = argv[3]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + } + + graph << target_hint + << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, 1U), 1, DataType::F32), + get_input_accessor(image, mean_r, mean_g, mean_b)) + << ConvolutionLayer( + 3U, 3U, 64U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_b.npy"), + PadStrideInfo(2, 2, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << ConvolutionLayer( + 1U, 1U, 16U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire2", 64U, 64U) + << ConvolutionLayer( + 1U, 1U, 16U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire3_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire3_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire3", 64U, 64U) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << ConvolutionLayer( + 1U, 1U, 32U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire4", 128U, 128U) + << ConvolutionLayer( + 1U, 1U, 32U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire5_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire5_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire5", 128U, 128U) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << ConvolutionLayer( + 1U, 1U, 48U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire6", 192U, 192U) + << ConvolutionLayer( + 1U, 1U, 48U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire7_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire7_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire7", 192U, 192U) + << ConvolutionLayer( + 1U, 1U, 64U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire8_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire8_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire8", 256U, 256U) + << ConvolutionLayer( + 1U, 1U, 64U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire9_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire9_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire9", 256U, 256U) + << ConvolutionLayer( + 1U, 1U, 1000U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv10_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv10_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) + << FlattenLayer() + << SoftmaxLayer() + << Tensor(get_output_accessor(label, 5)); + } + void do_run() override + { + // Run graph + graph.run(); + } + +private: + Graph graph{}; + + BranchLayer get_expand_fire_node(const std::string &data_path, std::string &¶m_path, unsigned int expand1_filt, unsigned int expand3_filt) + { + std::string total_path = "/cnn_data/squeezenet_v1_1_model/" + param_path + "_"; + SubGraph i_a; + i_a << ConvolutionLayer( + 1U, 1U, expand1_filt, + get_weights_accessor(data_path, total_path + "expand1x1_w.npy"), + get_weights_accessor(data_path, total_path + "expand1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_b; + i_b << ConvolutionLayer( + 3U, 3U, expand3_filt, + get_weights_accessor(data_path, total_path + "expand3x3_w.npy"), + get_weights_accessor(data_path, total_path + "expand3x3_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b)); + } +}; + +/** Main program for Squeezenet v1.1 + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +int main(int argc, char **argv) +{ + return arm_compute::utils::run_example(argc, argv); +} -- cgit v1.2.1