| /* |
| * Copyright (c) 2018-2019 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.h" |
| #include "support/ToolchainSupport.h" |
| #include "utils/CommonGraphOptions.h" |
| #include "utils/GraphUtils.h" |
| #include "utils/Utils.h" |
| |
| using namespace arm_compute; |
| using namespace arm_compute::utils; |
| using namespace arm_compute::graph::frontend; |
| using namespace arm_compute::graph_utils; |
| |
| /** Example demonstrating how to implement MobileNetSSD's network using the Compute Library's graph API */ |
| class GraphSSDMobilenetExample : public Example |
| { |
| public: |
| GraphSSDMobilenetExample() |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetSSD") |
| { |
| // Add topk option |
| keep_topk_opt = cmd_parser.add_option<SimpleOption<int>>("topk", 100); |
| keep_topk_opt->set_help("Top k detections results per image."); |
| } |
| GraphSSDMobilenetExample(const GraphSSDMobilenetExample &) = delete; |
| GraphSSDMobilenetExample &operator=(const GraphSSDMobilenetExample &) = delete; |
| GraphSSDMobilenetExample(GraphSSDMobilenetExample &&) = default; // NOLINT |
| GraphSSDMobilenetExample &operator=(GraphSSDMobilenetExample &&) = default; // NOLINT |
| ~GraphSSDMobilenetExample() override = default; |
| bool do_setup(int argc, char **argv) override |
| { |
| // Parse arguments |
| cmd_parser.parse(argc, argv); |
| |
| // Consume common parameters |
| common_params = consume_common_graph_parameters(common_opts); |
| |
| // Return when help menu is requested |
| if(common_params.help) |
| { |
| cmd_parser.print_help(argv[0]); |
| return false; |
| } |
| |
| // Print parameter values |
| std::cout << common_params << std::endl; |
| |
| // Create input descriptor |
| const TensorShape tensor_shape = permute_shape(TensorShape(300, 300, 3U, 1U), DataLayout::NCHW, common_params.data_layout); |
| TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); |
| |
| // Set graph hints |
| graph << common_params.target |
| << DepthwiseConvolutionMethod::Optimized3x3 // TODO(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method |
| << common_params.fast_math_hint; |
| |
| // Create core graph |
| std::string model_path = "/cnn_data/ssd_mobilenet_model/"; |
| |
| // Create a preprocessor object |
| const std::array<float, 3> mean_rgb{ { 127.5f, 127.5f, 127.5f } }; |
| std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb, true, 0.007843f); |
| |
| // Get trainable parameters data path |
| std::string data_path = common_params.data_path; |
| |
| // Add model path to data path |
| if(!data_path.empty()) |
| { |
| data_path += model_path; |
| } |
| |
| graph << InputLayer(input_descriptor, |
| get_input_accessor(common_params, std::move(preprocessor))); |
| |
| SubStream conv_11(graph); |
| conv_11 << ConvolutionLayer( |
| 3U, 3U, 32U, |
| get_weights_accessor(data_path, "conv0_w.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(2, 2, 1, 1)) |
| .set_name("conv0"); |
| conv_11 << BatchNormalizationLayer(get_weights_accessor(data_path, "conv0_bn_mean.npy"), |
| get_weights_accessor(data_path, "conv0_bn_var.npy"), |
| get_weights_accessor(data_path, "conv0_scale_w.npy"), |
| get_weights_accessor(data_path, "conv0_scale_b.npy"), 0.00001f) |
| .set_name("conv0/bn") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/relu"); |
| |
| conv_11 << get_node_A(conv_11, data_path, "conv1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| conv_11 << get_node_A(conv_11, data_path, "conv2", 128, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| conv_11 << get_node_A(conv_11, data_path, "conv3", 128, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| conv_11 << get_node_A(conv_11, data_path, "conv4", 256, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| conv_11 << get_node_A(conv_11, data_path, "conv5", 256, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| conv_11 << get_node_A(conv_11, data_path, "conv6", 512, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| conv_11 << get_node_A(conv_11, data_path, "conv7", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| conv_11 << get_node_A(conv_11, data_path, "conv8", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| conv_11 << get_node_A(conv_11, data_path, "conv9", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| conv_11 << get_node_A(conv_11, data_path, "conv10", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| conv_11 << get_node_A(conv_11, data_path, "conv11", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_13(conv_11); |
| conv_13 << get_node_A(conv_11, data_path, "conv12", 1024, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| conv_13 << get_node_A(conv_13, data_path, "conv13", 1024, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_14(conv_13); |
| conv_14 << get_node_B(conv_13, data_path, "conv14", 512, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); |
| |
| SubStream conv_15(conv_14); |
| conv_15 << get_node_B(conv_14, data_path, "conv15", 256, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); |
| |
| SubStream conv_16(conv_15); |
| conv_16 << get_node_B(conv_15, data_path, "conv16", 256, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); |
| |
| SubStream conv_17(conv_16); |
| conv_17 << get_node_B(conv_16, data_path, "conv17", 128, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); |
| |
| //mbox_loc |
| SubStream conv_11_mbox_loc(conv_11); |
| conv_11_mbox_loc << get_node_C(conv_11, data_path, "conv11_mbox_loc", 12, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_13_mbox_loc(conv_13); |
| conv_13_mbox_loc << get_node_C(conv_13, data_path, "conv13_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_14_2_mbox_loc(conv_14); |
| conv_14_2_mbox_loc << get_node_C(conv_14, data_path, "conv14_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_15_2_mbox_loc(conv_15); |
| conv_15_2_mbox_loc << get_node_C(conv_15, data_path, "conv15_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_16_2_mbox_loc(conv_16); |
| conv_16_2_mbox_loc << get_node_C(conv_16, data_path, "conv16_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_17_2_mbox_loc(conv_17); |
| conv_17_2_mbox_loc << get_node_C(conv_17, data_path, "conv17_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream mbox_loc(graph); |
| mbox_loc << ConcatLayer(std::move(conv_11_mbox_loc), std::move(conv_13_mbox_loc), conv_14_2_mbox_loc, std::move(conv_15_2_mbox_loc), |
| std::move(conv_16_2_mbox_loc), std::move(conv_17_2_mbox_loc)); |
| |
| //mbox_conf |
| SubStream conv_11_mbox_conf(conv_11); |
| conv_11_mbox_conf << get_node_C(conv_11, data_path, "conv11_mbox_conf", 63, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_13_mbox_conf(conv_13); |
| conv_13_mbox_conf << get_node_C(conv_13, data_path, "conv13_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_14_2_mbox_conf(conv_14); |
| conv_14_2_mbox_conf << get_node_C(conv_14, data_path, "conv14_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_15_2_mbox_conf(conv_15); |
| conv_15_2_mbox_conf << get_node_C(conv_15, data_path, "conv15_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_16_2_mbox_conf(conv_16); |
| conv_16_2_mbox_conf << get_node_C(conv_16, data_path, "conv16_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream conv_17_2_mbox_conf(conv_17); |
| conv_17_2_mbox_conf << get_node_C(conv_17, data_path, "conv17_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); |
| |
| SubStream mbox_conf(graph); |
| mbox_conf << ConcatLayer(std::move(conv_11_mbox_conf), std::move(conv_13_mbox_conf), std::move(conv_14_2_mbox_conf), |
| std::move(conv_15_2_mbox_conf), std::move(conv_16_2_mbox_conf), std::move(conv_17_2_mbox_conf)); |
| mbox_conf << ReshapeLayer(TensorShape(21U, 1917U)).set_name("mbox_conf/reshape"); |
| mbox_conf << SoftmaxLayer().set_name("mbox_conf/softmax"); |
| mbox_conf << FlattenLayer().set_name("mbox_conf/flat"); |
| |
| const std::vector<float> priorbox_variances = { 0.1f, 0.1f, 0.2f, 0.2f }; |
| const float priorbox_offset = 0.5f; |
| const std::vector<float> priorbox_aspect_ratios = { 2.f, 3.f }; |
| |
| //mbox_priorbox branch |
| SubStream conv_11_mbox_priorbox(conv_11); |
| |
| conv_11_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| PriorBoxLayerInfo({ 60.f }, priorbox_variances, priorbox_offset, true, false, {}, { 2.f })) |
| .set_name("conv11/priorbox"); |
| |
| SubStream conv_13_mbox_priorbox(conv_13); |
| conv_13_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| PriorBoxLayerInfo({ 105.f }, priorbox_variances, priorbox_offset, true, false, { 150.f }, priorbox_aspect_ratios)) |
| .set_name("conv13/priorbox"); |
| |
| SubStream conv_14_2_mbox_priorbox(conv_14); |
| conv_14_2_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| PriorBoxLayerInfo({ 150.f }, priorbox_variances, priorbox_offset, true, false, { 195.f }, priorbox_aspect_ratios)) |
| .set_name("conv14/priorbox"); |
| |
| SubStream conv_15_2_mbox_priorbox(conv_15); |
| conv_15_2_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| PriorBoxLayerInfo({ 195.f }, priorbox_variances, priorbox_offset, true, false, { 240.f }, priorbox_aspect_ratios)) |
| .set_name("conv15/priorbox"); |
| |
| SubStream conv_16_2_mbox_priorbox(conv_16); |
| conv_16_2_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| PriorBoxLayerInfo({ 240.f }, priorbox_variances, priorbox_offset, true, false, { 285.f }, priorbox_aspect_ratios)) |
| .set_name("conv16/priorbox"); |
| |
| SubStream conv_17_2_mbox_priorbox(conv_17); |
| conv_17_2_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| PriorBoxLayerInfo({ 285.f }, priorbox_variances, priorbox_offset, true, false, { 300.f }, priorbox_aspect_ratios)) |
| .set_name("conv17/priorbox"); |
| |
| SubStream mbox_priorbox(graph); |
| |
| mbox_priorbox << ConcatLayer( |
| (common_params.data_layout == DataLayout::NCHW) ? DataLayoutDimension::WIDTH : DataLayoutDimension::CHANNEL, |
| std::move(conv_11_mbox_priorbox), std::move(conv_13_mbox_priorbox), std::move(conv_14_2_mbox_priorbox), |
| std::move(conv_15_2_mbox_priorbox), std::move(conv_16_2_mbox_priorbox), std::move(conv_17_2_mbox_priorbox)); |
| |
| const int num_classes = 21; |
| const bool share_location = true; |
| const DetectionOutputLayerCodeType detection_type = DetectionOutputLayerCodeType::CENTER_SIZE; |
| const int keep_top_k = keep_topk_opt->value(); |
| const float nms_threshold = 0.45f; |
| const int label_id_background = 0; |
| const float conf_thrs = 0.25f; |
| const int top_k = 100; |
| |
| SubStream detection_ouput(mbox_loc); |
| detection_ouput << DetectionOutputLayer(std::move(mbox_conf), std::move(mbox_priorbox), |
| DetectionOutputLayerInfo(num_classes, share_location, detection_type, keep_top_k, nms_threshold, top_k, label_id_background, conf_thrs)); |
| detection_ouput << OutputLayer(get_detection_output_accessor(common_params, { tensor_shape })); |
| |
| // Finalize graph |
| GraphConfig config; |
| config.num_threads = common_params.threads; |
| config.use_tuner = common_params.enable_tuner; |
| config.tuner_mode = common_params.tuner_mode; |
| config.tuner_file = common_params.tuner_file; |
| |
| graph.finalize(common_params.target, config); |
| |
| return true; |
| } |
| void do_run() override |
| { |
| // Run graph |
| graph.run(); |
| } |
| |
| private: |
| CommandLineParser cmd_parser; |
| CommonGraphOptions common_opts; |
| SimpleOption<int> *keep_topk_opt{ nullptr }; |
| CommonGraphParams common_params; |
| Stream graph; |
| |
| ConcatLayer get_node_A(IStream &master_graph, const std::string &data_path, std::string &¶m_path, |
| unsigned int conv_filt, |
| PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info) |
| { |
| const std::string total_path = param_path + "_"; |
| SubStream sg(master_graph); |
| |
| sg << DepthwiseConvolutionLayer( |
| 3U, 3U, |
| get_weights_accessor(data_path, total_path + "dw_w.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| dwc_pad_stride_info) |
| .set_name(param_path + "/dw") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "dw_bn_mean.npy"), |
| get_weights_accessor(data_path, total_path + "dw_bn_var.npy"), |
| get_weights_accessor(data_path, total_path + "dw_scale_w.npy"), |
| get_weights_accessor(data_path, total_path + "dw_scale_b.npy"), 0.00001f) |
| .set_name(param_path + "/dw/bn") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "dw/relu") |
| |
| << ConvolutionLayer( |
| 1U, 1U, conv_filt, |
| get_weights_accessor(data_path, total_path + "w.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| conv_pad_stride_info) |
| .set_name(param_path + "/pw") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "bn_mean.npy"), |
| get_weights_accessor(data_path, total_path + "bn_var.npy"), |
| get_weights_accessor(data_path, total_path + "scale_w.npy"), |
| get_weights_accessor(data_path, total_path + "scale_b.npy"), 0.00001f) |
| .set_name(param_path + "/pw/bn") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "pw/relu"); |
| |
| return ConcatLayer(std::move(sg)); |
| } |
| |
| ConcatLayer get_node_B(IStream &master_graph, const std::string &data_path, std::string &¶m_path, |
| unsigned int conv_filt, |
| PadStrideInfo conv_pad_stride_info_1, PadStrideInfo conv_pad_stride_info_2) |
| { |
| const std::string total_path = param_path + "_"; |
| SubStream sg(master_graph); |
| |
| sg << ConvolutionLayer( |
| 1, 1, conv_filt / 2, |
| get_weights_accessor(data_path, total_path + "1_w.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| conv_pad_stride_info_1) |
| .set_name(total_path + "1/conv") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "1_bn_mean.npy"), |
| get_weights_accessor(data_path, total_path + "1_bn_var.npy"), |
| get_weights_accessor(data_path, total_path + "1_scale_w.npy"), |
| get_weights_accessor(data_path, total_path + "1_scale_b.npy"), 0.00001f) |
| .set_name(total_path + "1/bn") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(total_path + "1/relu"); |
| |
| sg << ConvolutionLayer( |
| 3, 3, conv_filt, |
| get_weights_accessor(data_path, total_path + "2_w.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| conv_pad_stride_info_2) |
| .set_name(total_path + "2/conv") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "2_bn_mean.npy"), |
| get_weights_accessor(data_path, total_path + "2_bn_var.npy"), |
| get_weights_accessor(data_path, total_path + "2_scale_w.npy"), |
| get_weights_accessor(data_path, total_path + "2_scale_b.npy"), 0.00001f) |
| .set_name(total_path + "2/bn") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(total_path + "2/relu"); |
| |
| return ConcatLayer(std::move(sg)); |
| } |
| |
| ConcatLayer get_node_C(IStream &master_graph, const std::string &data_path, std::string &¶m_path, |
| unsigned int conv_filt, PadStrideInfo conv_pad_stride_info) |
| { |
| const std::string total_path = param_path + "_"; |
| SubStream sg(master_graph); |
| sg << ConvolutionLayer( |
| 1U, 1U, conv_filt, |
| get_weights_accessor(data_path, total_path + "w.npy"), |
| get_weights_accessor(data_path, total_path + "b.npy"), |
| conv_pad_stride_info) |
| .set_name(param_path + "/conv"); |
| if(common_params.data_layout == DataLayout::NCHW) |
| { |
| sg << PermuteLayer(PermutationVector(2U, 0U, 1U), DataLayout::NHWC).set_name(param_path + "/perm"); |
| } |
| sg << FlattenLayer().set_name(param_path + "/flat"); |
| |
| return ConcatLayer(std::move(sg)); |
| } |
| }; |
| |
| /** Main program for MobileNetSSD |
| * |
| * Model is based on: |
| * http://arxiv.org/abs/1512.02325 |
| * SSD: Single Shot MultiBox Detector |
| * Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg |
| * |
| * Provenance: https://github.com/chuanqi305/MobileNet-SSD |
| * |
| * @note To list all the possible arguments execute the binary appended with the --help option |
| * |
| * @param[in] argc Number of arguments |
| * @param[in] argv Arguments |
| */ |
| int main(int argc, char **argv) |
| { |
| return arm_compute::utils::run_example<GraphSSDMobilenetExample>(argc, argv); |
| } |