| /* |
| * 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/GraphUtils.h" |
| #include "utils/Utils.h" |
| |
| #include <cstdlib> |
| |
| 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 QASYMM8 MobileNet's network using the Compute Library's graph API |
| * |
| * @param[in] argc Number of arguments |
| * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels ) |
| */ |
| class GraphMobileNetQASYMM8Example : public Example |
| { |
| public: |
| void do_setup(int argc, char **argv) override |
| { |
| std::string data_path; /* Path to the trainable data */ |
| std::string input; /* Image data */ |
| std::string label; /* Label data */ |
| |
| // 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 |
| }; |
| |
| // 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_hint(target); |
| |
| // Parse arguments |
| if(argc < 2) |
| { |
| // Print help |
| std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [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] [npy_input] [labels]\n\n"; |
| std::cout << "No input provided: using random values\n\n"; |
| } |
| else if(argc == 4) |
| { |
| data_path = argv[2]; |
| input = 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]; |
| input = argv[3]; |
| label = argv[4]; |
| } |
| |
| graph << target_hint |
| << DepthwiseConvolutionMethod::OPTIMIZED_3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method |
| << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::QASYMM8, in_quant_info), |
| get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input)) |
| << ConvolutionLayer( |
| 3U, 3U, 32U, |
| get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_weights.npy"), |
| get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_bias.npy"), |
| PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), |
| 1, conv_weights_quant_info.at(0), mid_quant_info) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)); |
| graph << get_dwsc_node(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(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(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(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(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(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(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(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(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(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(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(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(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)) |
| << ConvolutionLayer( |
| 1U, 1U, 1001U, |
| get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_weights.npy"), |
| get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_bias.npy"), |
| PadStrideInfo(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1)) |
| << ReshapeLayer(TensorShape(1001U)) |
| << SoftmaxLayer() |
| << OutputLayer(get_output_accessor(label, 5)); |
| |
| // Finalize graph |
| GraphConfig config; |
| config.use_function_memory_manager = true; |
| config.use_tuner = (target == 2); |
| graph.finalize(target_hint, config); |
| } |
| void do_run() override |
| { |
| // Run graph |
| graph.run(); |
| } |
| |
| private: |
| Stream graph{ 0, "MobileNetV1_QASYMM8" }; |
| |
| /** This function produces a depthwise separable convolution node (i.e. depthwise + pointwise layers) with ReLU6 activation after each layer. |
| * |
| * @param[in] data_path Path to trainable data folder |
| * @param[in] param_path Prefix of specific set of weights/biases data |
| * @param[in] conv_filt Filters depths for pointwise convolution |
| * @param[in] dwc_pad_stride_info PadStrideInfo for depthwise convolution |
| * @param[in] conv_pad_stride_info PadStrideInfo for pointwise convolution |
| * @param[in] depth_weights_quant_info QuantizationInfo for depthwise convolution's weights |
| * @param[in] point_weights_quant_info QuantizationInfo for pointwise convolution's weights |
| * |
| * @return The complete dwsc node |
| */ |
| BranchLayer get_dwsc_node(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 = "/cnn_data/mobilenet_qasymm8_model/" + 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, depth_weights_quant_info) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)) |
| << 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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)); |
| |
| return BranchLayer(std::move(sg)); |
| } |
| }; |
| /** Main program for MobileNetQASYMM8 |
| * |
| * @param[in] argc Number of arguments |
| * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels ) |
| */ |
| int main(int argc, char **argv) |
| { |
| return arm_compute::utils::run_example<GraphMobileNetQASYMM8Example>(argc, argv); |
| } |