| /* |
| * 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/graph2.h" |
| #include "support/ToolchainSupport.h" |
| #include "utils/GraphUtils.h" |
| #include "utils/Utils.h" |
| |
| #include <cstdlib> |
| #include <tuple> |
| |
| using namespace arm_compute::utils; |
| using namespace arm_compute::graph2::frontend; |
| using namespace arm_compute::graph_utils; |
| |
| 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 */ |
| |
| // Create a preprocessor object |
| const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } }; |
| std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb); |
| |
| // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON |
| const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; |
| Target target_hint = set_target_hint2(target); |
| bool enable_tuning = (target == 2); |
| bool enable_memory_management = true; |
| |
| // 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 |
| << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), DataType::F32), |
| get_input_accessor(image, std::move(preprocessor))) |
| << 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() |
| << OutputLayer(get_output_accessor(label, 5)); |
| |
| // Finalize graph |
| graph.finalize(target_hint, enable_tuning, enable_memory_management); |
| } |
| void do_run() override |
| { |
| // Run graph |
| graph.run(); |
| } |
| |
| private: |
| Stream graph{ 0, "SqueezeNetV1.1" }; |
| |
| 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 + "_"; |
| SubStream i_a(graph); |
| 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)); |
| |
| SubStream i_b(graph); |
| 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<GraphSqueezenet_v1_1Example>(argc, argv); |
| } |