examples/graph_mobilenet_qasymm8.cpp

   1 /*
   2  * Copyright (c) 2017-2018 ARM Limited.
   3  *
   4  * SPDX-License-Identifier: MIT
   5  *
   6  * Permission is hereby granted, free of charge, to any person obtaining a copy
   7  * of this software and associated documentation files (the "Software"), to
   8  * deal in the Software without restriction, including without limitation the
   9  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  10  * sell copies of the Software, and to permit persons to whom the Software is
  11  * furnished to do so, subject to the following conditions:
  12  *
  13  * The above copyright notice and this permission notice shall be included in all
  14  * copies or substantial portions of the Software.
  15  *
  16  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  17  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  18  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  19  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  20  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  21  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  22  * SOFTWARE.
  23  */
  24 #include "arm_compute/graph.h"
  25 #include "support/ToolchainSupport.h"
  26 #include "utils/GraphUtils.h"
  27 #include "utils/Utils.h"
  28
  29 #include <cstdlib>
  30
  31 using namespace arm_compute;
  32 using namespace arm_compute::utils;
  33 using namespace arm_compute::graph::frontend;
  34 using namespace arm_compute::graph_utils;
  35
  36 /** Example demonstrating how to implement QASYMM8 MobileNet's network using the Compute Library's graph API
  37  *
  38  * @param[in] argc Number of arguments
  39  * @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, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
  40  */
  41 class GraphMobileNetQASYMM8Example : public Example
  42 {
  43 public:
  44     void do_setup(int argc, char **argv) override
  45     {
  46         std::string data_path; /* Path to the trainable data */
  47         std::string input;     /* Image data */
  48         std::string label;     /* Label data */
  49
  50         // Quantization info taken from the AndroidNN QASYMM8 MobileNet example
  51         const QuantizationInfo in_quant_info  = QuantizationInfo(0.0078125f, 128);
  52         const QuantizationInfo mid_quant_info = QuantizationInfo(0.0784313753247f, 128);
  53
  54         const std::vector<QuantizationInfo> conv_weights_quant_info =
  55         {
  56             QuantizationInfo(0.031778190285f, 156), // conv0
  57             QuantizationInfo(0.00604454148561f, 66) // conv14
  58         };
  59
  60         const std::vector<QuantizationInfo> depth_weights_quant_info =
  61         {
  62             QuantizationInfo(0.254282623529f, 129),  // dwsc1
  63             QuantizationInfo(0.12828284502f, 172),   // dwsc2
  64             QuantizationInfo(0.265911251307f, 83),   // dwsc3
  65             QuantizationInfo(0.0985597148538f, 30),  // dwsc4
  66             QuantizationInfo(0.0631204470992f, 54),  // dwsc5
  67             QuantizationInfo(0.0137207424268f, 141), // dwsc6
  68             QuantizationInfo(0.0817828401923f, 125), // dwsc7
  69             QuantizationInfo(0.0393880493939f, 164), // dwsc8
  70             QuantizationInfo(0.211694166064f, 129),  // dwsc9
  71             QuantizationInfo(0.158015936613f, 103),  // dwsc10
  72             QuantizationInfo(0.0182712618262f, 137), // dwsc11
  73             QuantizationInfo(0.0127998134121f, 134), // dwsc12
  74             QuantizationInfo(0.299285322428f, 161)   // dwsc13
  75         };
  76
  77         const std::vector<QuantizationInfo> point_weights_quant_info =
  78         {
  79             QuantizationInfo(0.0425766184926f, 129),  // dwsc1
  80             QuantizationInfo(0.0250773020089f, 94),   // dwsc2
  81             QuantizationInfo(0.015851572156f, 93),    // dwsc3
  82             QuantizationInfo(0.0167811904103f, 98),   // dwsc4
  83             QuantizationInfo(0.00951790809631f, 135), // dwsc5
  84             QuantizationInfo(0.00999817531556f, 128), // dwsc6
  85             QuantizationInfo(0.00590536883101f, 126), // dwsc7
  86             QuantizationInfo(0.00576109671965f, 133), // dwsc8
  87             QuantizationInfo(0.00830461271107f, 142), // dwsc9
  88             QuantizationInfo(0.0152327232063f, 72),   // dwsc10
  89             QuantizationInfo(0.00741417845711f, 125), // dwsc11
  90             QuantizationInfo(0.0135628981516f, 142),  // dwsc12
  91             QuantizationInfo(0.0338749065995f, 140)   // dwsc13
  92         };
  93
  94         // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
  95         const int    target         = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
  96         Target       target_hint    = set_target_hint(target);
  97         FastMathHint fast_math_hint = FastMathHint::DISABLED;
  98
  99         // Parse arguments
 100         if(argc < 2)
 101         {
 102             // Print help
 103             std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [labels] [fast_math_hint]\n\n";
 104             std::cout << "No data folder provided: using random values\n\n";
 105         }
 106         else if(argc == 2)
 107         {
 108             std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [npy_input] [labels] [fast_math_hint]\n\n";
 109             std::cout << "No input provided: using random values\n\n";
 110         }
 111         else if(argc == 4)
 112         {
 113             data_path = argv[2];
 114             input     = argv[3];
 115             std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
 116             std::cout << "No text file with labels provided: skipping output accessor\n\n";
 117         }
 118         else if(argc == 5)
 119         {
 120             data_path = argv[2];
 121             input     = argv[3];
 122             label     = argv[4];
 123             std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
 124             std::cout << "No fast math info provided: disabling fast math\n\n";
 125         }
 126         else
 127         {
 128             data_path      = argv[2];
 129             input          = argv[3];
 130             label          = argv[4];
 131             fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
 132         }
 133
 134         graph << target_hint
 135               << DepthwiseConvolutionMethod::OPTIMIZED_3x3
 136               << fast_math_hint
 137               << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::QASYMM8, in_quant_info),
 138                             get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input))
 139               << ConvolutionLayer(
 140                   3U, 3U, 32U,
 141                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_weights.npy"),
 142                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_bias.npy"),
 143                   PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
 144                   1, conv_weights_quant_info.at(0), mid_quant_info)
 145               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f));
 146         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));
 147         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),
 148                                point_weights_quant_info.at(1));
 149         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),
 150                                point_weights_quant_info.at(2));
 151         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),
 152                                point_weights_quant_info.at(3));
 153         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),
 154                                point_weights_quant_info.at(4));
 155         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),
 156                                point_weights_quant_info.at(5));
 157         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),
 158                                point_weights_quant_info.at(6));
 159         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),
 160                                point_weights_quant_info.at(7));
 161         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),
 162                                point_weights_quant_info.at(8));
 163         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),
 164                                point_weights_quant_info.at(9));
 165         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),
 166                                point_weights_quant_info.at(10));
 167         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),
 168                                point_weights_quant_info.at(11));
 169         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),
 170                                point_weights_quant_info.at(12))
 171               << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
 172               << ConvolutionLayer(
 173                   1U, 1U, 1001U,
 174                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_weights.npy"),
 175                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_bias.npy"),
 176                   PadStrideInfo(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1))
 177               << ReshapeLayer(TensorShape(1001U))
 178               << SoftmaxLayer()
 179               << OutputLayer(get_output_accessor(label, 5));
 180
 181         // Finalize graph
 182         GraphConfig config;
 183         config.use_tuner = (target == 2);
 184         graph.finalize(target_hint, config);
 185     }
 186     void do_run() override
 187     {
 188         // Run graph
 189         graph.run();
 190     }
 191
 192 private:
 193     Stream graph{ 0, "MobileNetV1_QASYMM8" };
 194
 195     /** This function produces a depthwise separable convolution node (i.e. depthwise + pointwise layers) with ReLU6 activation after each layer.
 196      *
 197      * @param[in] data_path                Path to trainable data folder
 198      * @param[in] param_path               Prefix of specific set of weights/biases data
 199      * @param[in] conv_filt                Filters depths for pointwise convolution
 200      * @param[in] dwc_pad_stride_info      PadStrideInfo for depthwise convolution
 201      * @param[in] conv_pad_stride_info     PadStrideInfo for pointwise convolution
 202      * @param[in] depth_weights_quant_info QuantizationInfo for depthwise convolution's weights
 203      * @param[in] point_weights_quant_info QuantizationInfo for pointwise convolution's weights
 204      *
 205      * @return The complete dwsc node
 206      */
 207     BranchLayer get_dwsc_node(const std::string &data_path, std::string &&param_path,
 208                               const unsigned int conv_filt,
 209                               PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info,
 210                               QuantizationInfo depth_weights_quant_info, QuantizationInfo point_weights_quant_info)
 211     {
 212         std::string total_path = "/cnn_data/mobilenet_qasymm8_model/" + param_path + "_";
 213         SubStream   sg(graph);
 214
 215         sg << DepthwiseConvolutionLayer(
 216                3U, 3U,
 217                get_weights_accessor(data_path, total_path + "depthwise_weights.npy"),
 218                get_weights_accessor(data_path, total_path + "depthwise_bias.npy"),
 219                dwc_pad_stride_info, depth_weights_quant_info)
 220            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
 221            << ConvolutionLayer(
 222                1U, 1U, conv_filt,
 223                get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
 224                get_weights_accessor(data_path, total_path + "pointwise_bias.npy"),
 225                conv_pad_stride_info, 1, point_weights_quant_info)
 226            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f));
 227
 228         return BranchLayer(std::move(sg));
 229     }
 230 };
 231 /** Main program for MobileNetQASYMM8
 232  *
 233  * @param[in] argc Number of arguments
 234  * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
 235  */
 236 int main(int argc, char **argv)
 237 {
 238     return arm_compute::utils::run_example<GraphMobileNetQASYMM8Example>(argc, argv);
 239 }