| /* |
| * 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. |
| */ |
| #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 MobileNet's network using the Compute Library's graph API */ |
| class GraphMobilenetExample : public Example |
| { |
| public: |
| GraphMobilenetExample() |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV1") |
| { |
| // Add model id option |
| model_id_opt = cmd_parser.add_option<SimpleOption<int>>("model-id", 0); |
| model_id_opt->set_help("Mobilenet model id (0: 1.0_224, else: 0.75_160"); |
| } |
| GraphMobilenetExample(const GraphMobilenetExample &) = delete; |
| GraphMobilenetExample &operator=(const GraphMobilenetExample &) = delete; |
| GraphMobilenetExample(GraphMobilenetExample &&) = default; // NOLINT |
| GraphMobilenetExample &operator=(GraphMobilenetExample &&) = default; // NOLINT |
| ~GraphMobilenetExample() 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; |
| |
| // Get model parameters |
| int model_id = model_id_opt->value(); |
| |
| // Create input descriptor |
| unsigned int spatial_size = (model_id == 0 || common_params.data_type == DataType::QASYMM8) ? 224 : 160; |
| |
| // Create input descriptor |
| const TensorShape tensor_shape = permute_shape(TensorShape(spatial_size, spatial_size, 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 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method |
| << common_params.fast_math_hint; |
| |
| // Create core graph |
| if(arm_compute::is_data_type_float(common_params.data_type)) |
| { |
| create_graph_float(input_descriptor, model_id); |
| } |
| else |
| { |
| create_graph_qasymm(input_descriptor); |
| } |
| |
| // Create common tail |
| graph << ReshapeLayer(TensorShape(1001U)).set_name("Reshape") |
| << SoftmaxLayer().set_name("Softmax") |
| << OutputLayer(get_output_accessor(common_params, 5)); |
| |
| // Finalize graph |
| GraphConfig config; |
| config.num_threads = common_params.threads; |
| config.use_tuner = common_params.enable_tuner; |
| 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> *model_id_opt{ nullptr }; |
| CommonGraphParams common_params; |
| Stream graph; |
| |
| void create_graph_float(TensorDescriptor &input_descriptor, int model_id) |
| { |
| float depth_scale = (model_id == 0) ? 1.f : 0.75; |
| std::string model_path = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/"; |
| |
| // Create a preprocessor object |
| std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(); |
| |
| // 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), false)) |
| << ConvolutionLayer( |
| 3U, 3U, 32U * depth_scale, |
| get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)) |
| .set_name("Conv2d_0") |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Conv2d_0/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6"); |
| graph << get_dwsc_node_float(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << get_dwsc_node_float(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); |
| graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a") |
| << ConvolutionLayer( |
| 1U, 1U, 1001U, |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), |
| PadStrideInfo(1, 1, 0, 0)) |
| .set_name("Logits/Conv2d_1c_1x1"); |
| } |
| |
| void create_graph_qasymm(TensorDescriptor &input_descriptor) |
| { |
| // 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 += "/cnn_data/mobilenet_qasymm8_model/"; |
| } |
| |
| // Quantization info taken from the AndroidNN QASYMM8 MobileNet example |
| const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128); |
| const QuantizationInfo mid_quant_info = QuantizationInfo(0.0784313753247f, 128); |
| |
| const std::vector<QuantizationInfo> conv_weights_quant_info = |
| { |
| QuantizationInfo(0.031778190285f, 156), // conv0 |
| QuantizationInfo(0.00604454148561f, 66) // conv14 |
| }; |
| |
| const std::vector<QuantizationInfo> depth_weights_quant_info = |
| { |
| QuantizationInfo(0.254282623529f, 129), // dwsc1 |
| QuantizationInfo(0.12828284502f, 172), // dwsc2 |
| QuantizationInfo(0.265911251307f, 83), // dwsc3 |
| QuantizationInfo(0.0985597148538f, 30), // dwsc4 |
| QuantizationInfo(0.0631204470992f, 54), // dwsc5 |
| QuantizationInfo(0.0137207424268f, 141), // dwsc6 |
| QuantizationInfo(0.0817828401923f, 125), // dwsc7 |
| QuantizationInfo(0.0393880493939f, 164), // dwsc8 |
| QuantizationInfo(0.211694166064f, 129), // dwsc9 |
| QuantizationInfo(0.158015936613f, 103), // dwsc10 |
| QuantizationInfo(0.0182712618262f, 137), // dwsc11 |
| QuantizationInfo(0.0127998134121f, 134), // dwsc12 |
| QuantizationInfo(0.299285322428f, 161) // dwsc13 |
| }; |
| |
| const std::vector<QuantizationInfo> point_weights_quant_info = |
| { |
| QuantizationInfo(0.0425766184926f, 129), // dwsc1 |
| QuantizationInfo(0.0250773020089f, 94), // dwsc2 |
| QuantizationInfo(0.015851572156f, 93), // dwsc3 |
| QuantizationInfo(0.0167811904103f, 98), // dwsc4 |
| QuantizationInfo(0.00951790809631f, 135), // dwsc5 |
| QuantizationInfo(0.00999817531556f, 128), // dwsc6 |
| QuantizationInfo(0.00590536883101f, 126), // dwsc7 |
| QuantizationInfo(0.00576109671965f, 133), // dwsc8 |
| QuantizationInfo(0.00830461271107f, 142), // dwsc9 |
| QuantizationInfo(0.0152327232063f, 72), // dwsc10 |
| QuantizationInfo(0.00741417845711f, 125), // dwsc11 |
| QuantizationInfo(0.0135628981516f, 142), // dwsc12 |
| QuantizationInfo(0.0338749065995f, 140) // dwsc13 |
| }; |
| |
| graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info), |
| get_weights_accessor(data_path, common_params.image)) |
| << ConvolutionLayer( |
| 3U, 3U, 32U, |
| get_weights_accessor(data_path, "Conv2d_0_weights.npy"), |
| get_weights_accessor(data_path, "Conv2d_0_bias.npy"), |
| PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), |
| 1, conv_weights_quant_info.at(0), mid_quant_info) |
| .set_name("Conv2d_0") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6"); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_1", 64U, PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0), point_weights_quant_info.at(0)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1), |
| point_weights_quant_info.at(1)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2), |
| point_weights_quant_info.at(2)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_4", 256U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3), |
| point_weights_quant_info.at(3)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4), |
| point_weights_quant_info.at(4)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5), |
| point_weights_quant_info.at(5)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6), |
| point_weights_quant_info.at(6)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7), |
| point_weights_quant_info.at(7)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8), |
| point_weights_quant_info.at(8)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9), |
| point_weights_quant_info.at(9)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10), |
| point_weights_quant_info.at(10)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_12", 1024U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11), |
| point_weights_quant_info.at(11)); |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12), |
| point_weights_quant_info.at(12)) |
| << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a") |
| << ConvolutionLayer( |
| 1U, 1U, 1001U, |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"), |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_bias.npy"), |
| PadStrideInfo(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1)) |
| .set_name("Logits/Conv2d_1c_1x1"); |
| } |
| |
| ConcatLayer get_dwsc_node_float(const std::string &data_path, std::string &¶m_path, |
| unsigned int conv_filt, |
| PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info) |
| { |
| std::string total_path = param_path + "_"; |
| SubStream sg(graph); |
| sg << DepthwiseConvolutionLayer( |
| 3U, 3U, |
| get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| dwc_pad_stride_info) |
| .set_name(total_path + "depthwise/depthwise") |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name(total_path + "depthwise/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6") |
| << ConvolutionLayer( |
| 1U, 1U, conv_filt, |
| get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| conv_pad_stride_info) |
| .set_name(total_path + "pointwise/Conv2D") |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name(total_path + "pointwise/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6"); |
| |
| return ConcatLayer(std::move(sg)); |
| } |
| |
| ConcatLayer get_dwsc_node_qasymm(const std::string &data_path, std::string &¶m_path, |
| const unsigned int conv_filt, |
| PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info, |
| QuantizationInfo depth_weights_quant_info, QuantizationInfo point_weights_quant_info) |
| { |
| std::string total_path = param_path + "_"; |
| SubStream sg(graph); |
| |
| sg << DepthwiseConvolutionLayer( |
| 3U, 3U, |
| get_weights_accessor(data_path, total_path + "depthwise_weights.npy"), |
| get_weights_accessor(data_path, total_path + "depthwise_bias.npy"), |
| dwc_pad_stride_info, 1, depth_weights_quant_info) |
| .set_name(total_path + "depthwise/depthwise") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6") |
| << ConvolutionLayer( |
| 1U, 1U, conv_filt, |
| get_weights_accessor(data_path, total_path + "pointwise_weights.npy"), |
| get_weights_accessor(data_path, total_path + "pointwise_bias.npy"), |
| conv_pad_stride_info, 1, point_weights_quant_info) |
| .set_name(total_path + "pointwise/Conv2D") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6"); |
| |
| return ConcatLayer(std::move(sg)); |
| } |
| }; |
| |
| /** Main program for MobileNetV1 |
| * |
| * Model is based on: |
| * https://arxiv.org/abs/1704.04861 |
| * "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" |
| * Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam |
| * |
| * Provenance: download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz |
| * download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_160.tgz |
| * |
| * @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<GraphMobilenetExample>(argc, argv); |
| } |