| /* |
| * Copyright (c) 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::utils; |
| using namespace arm_compute::graph::frontend; |
| using namespace arm_compute::graph_utils; |
| |
| /** Example demonstrating how to implement ResNeXt50 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_in, [optional] npy_out, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) |
| */ |
| class GraphResNeXt50Example : public Example |
| { |
| public: |
| void do_setup(int argc, char **argv) override |
| { |
| std::string data_path; /* Path to the trainable data */ |
| std::string npy_in; /* Input npy data */ |
| std::string npy_out; /* Output npy data */ |
| |
| // 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); |
| FastMathHint fast_math_hint = FastMathHint::DISABLED; |
| |
| // Parse arguments |
| if(argc < 2) |
| { |
| // Print help |
| std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_in] [npy_out] [fast_math_hint]\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_in] [npy_out] [fast_math_hint]\n\n"; |
| std::cout << "No data folder provided: using random values\n\n"; |
| } |
| else if(argc == 3) |
| { |
| data_path = argv[2]; |
| std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [npy_in] [npy_out] [fast_math_hint]\n\n"; |
| std::cout << "No input npy file provided: using random values\n\n"; |
| } |
| else if(argc == 4) |
| { |
| data_path = argv[2]; |
| npy_in = argv[3]; |
| std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [npy_out] [fast_math_hint]\n\n"; |
| std::cout << "No output npy file provided: skipping output accessor\n\n"; |
| } |
| else if(argc == 5) |
| { |
| data_path = argv[2]; |
| npy_in = argv[3]; |
| npy_out = argv[4]; |
| std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; |
| std::cout << "No fast math info provided: disabling fast math\n\n"; |
| } |
| else |
| { |
| data_path = argv[2]; |
| npy_in = argv[3]; |
| npy_out = argv[4]; |
| fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; |
| } |
| |
| graph << target_hint |
| << fast_math_hint |
| << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), |
| get_input_accessor(npy_in)) |
| << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"), |
| get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy")) |
| .set_name("bn_data/Scale") |
| << ConvolutionLayer( |
| 7U, 7U, 64U, |
| get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy"), |
| get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"), |
| PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR)) |
| .set_name("conv0/Convolution") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0"); |
| |
| add_residual_block(data_path, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1); |
| add_residual_block(data_path, 512, 2, 4, 2); |
| add_residual_block(data_path, 1024, 3, 6, 2); |
| add_residual_block(data_path, 2048, 4, 3, 2); |
| |
| graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool1") |
| << FlattenLayer().set_name("predictions/Reshape") |
| << OutputLayer(get_npy_output_accessor(npy_out, TensorShape(2048U), DataType::F32)); |
| |
| // Finalize graph |
| GraphConfig config; |
| config.use_tuner = (target == 2); |
| graph.finalize(target_hint, config); |
| } |
| |
| void do_run() override |
| { |
| // Run graph |
| graph.run(); |
| } |
| |
| private: |
| Stream graph{ 0, "ResNeXt50" }; |
| |
| 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) |
| { |
| for(unsigned int i = 0; i < num_units; ++i) |
| { |
| std::stringstream unit_path_ss; |
| unit_path_ss << "/cnn_data/resnext50_model/stage" << stage << "_unit" << (i + 1) << "_"; |
| std::string unit_path = unit_path_ss.str(); |
| |
| std::stringstream unit_name_ss; |
| unit_name_ss << "stage" << stage << "/unit" << (i + 1) << "/"; |
| std::string unit_name = unit_name_ss.str(); |
| |
| PadStrideInfo pad_grouped_conv(1, 1, 1, 1); |
| if(i == 0) |
| { |
| 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); |
| } |
| |
| SubStream right(graph); |
| right << ConvolutionLayer( |
| 1U, 1U, base_depth / 2, |
| get_weights_accessor(data_path, unit_path + "conv1_weights.npy"), |
| get_weights_accessor(data_path, unit_path + "conv1_biases.npy"), |
| PadStrideInfo(1, 1, 0, 0)) |
| .set_name(unit_name + "conv1/convolution") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu") |
| |
| << ConvolutionLayer( |
| 3U, 3U, base_depth / 2, |
| get_weights_accessor(data_path, unit_path + "conv2_weights.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| pad_grouped_conv, 32) |
| .set_name(unit_name + "conv2/convolution") |
| << ScaleLayer(get_weights_accessor(data_path, unit_path + "bn2_mul.npy"), |
| get_weights_accessor(data_path, unit_path + "bn2_add.npy")) |
| .set_name(unit_name + "conv1/Scale") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu") |
| |
| << ConvolutionLayer( |
| 1U, 1U, base_depth, |
| get_weights_accessor(data_path, unit_path + "conv3_weights.npy"), |
| get_weights_accessor(data_path, unit_path + "conv3_biases.npy"), |
| PadStrideInfo(1, 1, 0, 0)) |
| .set_name(unit_name + "conv3/convolution"); |
| |
| SubStream left(graph); |
| if(i == 0) |
| { |
| left << ConvolutionLayer( |
| 1U, 1U, base_depth, |
| get_weights_accessor(data_path, unit_path + "sc_weights.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0)) |
| .set_name(unit_name + "sc/convolution") |
| << ScaleLayer(get_weights_accessor(data_path, unit_path + "sc_bn_mul.npy"), |
| get_weights_accessor(data_path, unit_path + "sc_bn_add.npy")) |
| .set_name(unit_name + "sc/scale"); |
| } |
| |
| graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(unit_name + "add"); |
| graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); |
| } |
| } |
| }; |
| |
| /** Main program for ResNeXt50 |
| * |
| * @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_in, [optional] npy_out ) |
| */ |
| int main(int argc, char **argv) |
| { |
| return arm_compute::utils::run_example<GraphResNeXt50Example>(argc, argv); |
| } |