Isabella Gottardi | 88d5b22 | 2018-04-06 12:24:55 +0100 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 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::utils; |
| 32 | using namespace arm_compute::graph::frontend; |
| 33 | using namespace arm_compute::graph_utils; |
| 34 | |
| 35 | /** Example demonstrating how to implement ResNeXt50 network using the Compute Library's graph API |
| 36 | * |
| 37 | * @param[in] argc Number of arguments |
| 38 | * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_in, [optional] npy_out, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) |
| 39 | */ |
| 40 | class GraphResNeXt50Example : public Example |
| 41 | { |
| 42 | public: |
| 43 | void do_setup(int argc, char **argv) override |
| 44 | { |
| 45 | std::string data_path; /* Path to the trainable data */ |
| 46 | std::string npy_in; /* Input npy data */ |
| 47 | std::string npy_out; /* Output npy data */ |
| 48 | |
| 49 | // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON |
| 50 | const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; |
| 51 | Target target_hint = set_target_hint(target); |
| 52 | FastMathHint fast_math_hint = FastMathHint::DISABLED; |
| 53 | |
| 54 | // Parse arguments |
| 55 | if(argc < 2) |
| 56 | { |
| 57 | // Print help |
| 58 | std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_in] [npy_out] [fast_math_hint]\n\n"; |
| 59 | std::cout << "No data folder provided: using random values\n\n"; |
| 60 | } |
| 61 | else if(argc == 2) |
| 62 | { |
| 63 | std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [npy_in] [npy_out] [fast_math_hint]\n\n"; |
| 64 | std::cout << "No data folder provided: using random values\n\n"; |
| 65 | } |
| 66 | else if(argc == 3) |
| 67 | { |
| 68 | data_path = argv[2]; |
| 69 | std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [npy_in] [npy_out] [fast_math_hint]\n\n"; |
| 70 | std::cout << "No input npy file provided: using random values\n\n"; |
| 71 | } |
| 72 | else if(argc == 4) |
| 73 | { |
| 74 | data_path = argv[2]; |
| 75 | npy_in = argv[3]; |
| 76 | std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [npy_out] [fast_math_hint]\n\n"; |
| 77 | std::cout << "No output npy file provided: skipping output accessor\n\n"; |
| 78 | } |
| 79 | else if(argc == 5) |
| 80 | { |
| 81 | data_path = argv[2]; |
| 82 | npy_in = argv[3]; |
| 83 | npy_out = argv[4]; |
| 84 | std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; |
| 85 | std::cout << "No fast math info provided: disabling fast math\n\n"; |
| 86 | } |
| 87 | else |
| 88 | { |
| 89 | data_path = argv[2]; |
| 90 | npy_in = argv[3]; |
| 91 | npy_out = argv[4]; |
| 92 | fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; |
| 93 | } |
| 94 | |
| 95 | graph << target_hint |
| 96 | << fast_math_hint |
| 97 | << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), |
| 98 | get_input_accessor(npy_in)) |
| 99 | << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"), |
| 100 | get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy")) |
| 101 | .set_name("bn_data/Scale") |
| 102 | << ConvolutionLayer( |
| 103 | 7U, 7U, 64U, |
| 104 | get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy"), |
| 105 | get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"), |
| 106 | PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR)) |
| 107 | .set_name("conv0/Convolution") |
| 108 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu") |
| 109 | << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0"); |
| 110 | |
| 111 | add_residual_block(data_path, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1); |
| 112 | add_residual_block(data_path, 512, 2, 4, 2); |
| 113 | add_residual_block(data_path, 1024, 3, 6, 2); |
| 114 | add_residual_block(data_path, 2048, 4, 3, 2); |
| 115 | |
| 116 | graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool1") |
| 117 | << FlattenLayer().set_name("predictions/Reshape") |
| 118 | << OutputLayer(get_npy_output_accessor(npy_out, TensorShape(2048U), DataType::F32)); |
| 119 | |
| 120 | // Finalize graph |
| 121 | GraphConfig config; |
| 122 | config.use_tuner = (target == 2); |
| 123 | graph.finalize(target_hint, config); |
| 124 | } |
| 125 | |
| 126 | void do_run() override |
| 127 | { |
| 128 | // Run graph |
| 129 | graph.run(); |
| 130 | } |
| 131 | |
| 132 | private: |
| 133 | Stream graph{ 0, "ResNeXt50" }; |
| 134 | |
| 135 | void add_residual_block(const std::string &data_path, unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1) |
| 136 | { |
| 137 | for(unsigned int i = 0; i < num_units; ++i) |
| 138 | { |
| 139 | std::stringstream unit_path_ss; |
| 140 | unit_path_ss << "/cnn_data/resnext50_model/stage" << stage << "_unit" << (i + 1) << "_"; |
| 141 | std::string unit_path = unit_path_ss.str(); |
| 142 | |
| 143 | std::stringstream unit_name_ss; |
| 144 | unit_name_ss << "stage" << stage << "/unit" << (i + 1) << "/"; |
| 145 | std::string unit_name = unit_name_ss.str(); |
| 146 | |
| 147 | PadStrideInfo pad_grouped_conv(1, 1, 1, 1); |
| 148 | if(i == 0) |
| 149 | { |
| 150 | pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1) : PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1, DimensionRoundingType::FLOOR); |
| 151 | } |
| 152 | |
| 153 | SubStream right(graph); |
| 154 | right << ConvolutionLayer( |
| 155 | 1U, 1U, base_depth / 2, |
| 156 | get_weights_accessor(data_path, unit_path + "conv1_weights.npy"), |
| 157 | get_weights_accessor(data_path, unit_path + "conv1_biases.npy"), |
| 158 | PadStrideInfo(1, 1, 0, 0)) |
| 159 | .set_name(unit_name + "conv1/convolution") |
| 160 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu") |
| 161 | |
| 162 | << ConvolutionLayer( |
| 163 | 3U, 3U, base_depth / 2, |
| 164 | get_weights_accessor(data_path, unit_path + "conv2_weights.npy"), |
| 165 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 166 | pad_grouped_conv, 32) |
| 167 | .set_name(unit_name + "conv2/convolution") |
| 168 | << ScaleLayer(get_weights_accessor(data_path, unit_path + "bn2_mul.npy"), |
| 169 | get_weights_accessor(data_path, unit_path + "bn2_add.npy")) |
| 170 | .set_name(unit_name + "conv1/Scale") |
| 171 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu") |
| 172 | |
| 173 | << ConvolutionLayer( |
| 174 | 1U, 1U, base_depth, |
| 175 | get_weights_accessor(data_path, unit_path + "conv3_weights.npy"), |
| 176 | get_weights_accessor(data_path, unit_path + "conv3_biases.npy"), |
| 177 | PadStrideInfo(1, 1, 0, 0)) |
| 178 | .set_name(unit_name + "conv3/convolution"); |
| 179 | |
| 180 | SubStream left(graph); |
| 181 | if(i == 0) |
| 182 | { |
| 183 | left << ConvolutionLayer( |
| 184 | 1U, 1U, base_depth, |
| 185 | get_weights_accessor(data_path, unit_path + "sc_weights.npy"), |
| 186 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 187 | PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0)) |
| 188 | .set_name(unit_name + "sc/convolution") |
| 189 | << ScaleLayer(get_weights_accessor(data_path, unit_path + "sc_bn_mul.npy"), |
| 190 | get_weights_accessor(data_path, unit_path + "sc_bn_add.npy")) |
| 191 | .set_name(unit_name + "sc/scale"); |
| 192 | } |
| 193 | |
| 194 | graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(unit_name + "add"); |
| 195 | graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); |
| 196 | } |
| 197 | } |
| 198 | }; |
| 199 | |
| 200 | /** Main program for ResNeXt50 |
| 201 | * |
| 202 | * @param[in] argc Number of arguments |
| 203 | * @param[in] argv Arguments ( [[optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_in, [optional] npy_out ) |
| 204 | */ |
| 205 | int main(int argc, char **argv) |
| 206 | { |
| 207 | return arm_compute::utils::run_example<GraphResNeXt50Example>(argc, argv); |
| 208 | } |