| /* |
| * 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/Graph.h" |
| #include "arm_compute/graph/Nodes.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; |
| using namespace arm_compute::graph_utils; |
| |
| /** Example demonstrating how to implement Microsoft's ResNet50 network using the Compute Library's graph API |
| * |
| * @param[in] argc Number of arguments |
| * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) |
| */ |
| class GraphResNet50Example : public Example |
| { |
| public: |
| void do_setup(int argc, char **argv) override |
| { |
| std::string data_path; /* Path to the trainable data */ |
| std::string image; /* Image data */ |
| std::string label; /* Label data */ |
| |
| // Create a preprocessor object |
| const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } }; |
| std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb, |
| false /* Do not convert to BGR */); |
| |
| // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON |
| const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; |
| TargetHint target_hint = set_target_hint(int_target_hint); |
| |
| // Parse arguments |
| if(argc < 2) |
| { |
| // Print help |
| std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [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] [image] [labels]\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] << " [image] [labels]\n\n"; |
| std::cout << "No image provided: using random values\n\n"; |
| } |
| else if(argc == 4) |
| { |
| data_path = argv[2]; |
| image = 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]; |
| image = argv[3]; |
| label = argv[4]; |
| } |
| |
| // Initialize the graph |
| graph.graph_init(int_target_hint == 2); |
| |
| graph << target_hint |
| << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), |
| get_input_accessor(image, std::move(preprocessor), false /* Do not convert to BGR */)) |
| << ConvolutionLayer( |
| 7U, 7U, 64U, |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(2, 2, 3, 3)) |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"), |
| 0.0000100099996416f) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))); |
| |
| add_residual_block(data_path, "block1", 64, 3, 2); |
| add_residual_block(data_path, "block2", 128, 4, 2); |
| add_residual_block(data_path, "block3", 256, 6, 2); |
| add_residual_block(data_path, "block4", 512, 3, 1); |
| |
| graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) |
| << ConvolutionLayer( |
| 1U, 1U, 1000U, |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy"), |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_biases.npy"), |
| PadStrideInfo(1, 1, 0, 0)) |
| << FlattenLayer() |
| << SoftmaxLayer() |
| << Tensor(get_output_accessor(label, 5)); |
| } |
| void do_run() override |
| { |
| // Run graph |
| graph.run(); |
| } |
| |
| private: |
| Graph graph{}; |
| |
| void add_residual_block(const std::string &data_path, const std::string &name, unsigned int base_depth, unsigned int num_units, unsigned int stride) |
| { |
| for(unsigned int i = 0; i < num_units; ++i) |
| { |
| std::stringstream unit; |
| unit << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_"; |
| std::string unit_name = unit.str(); |
| |
| unsigned int middle_stride = 1; |
| |
| if(i == (num_units - 1)) |
| { |
| middle_stride = stride; |
| } |
| |
| SubGraph right; |
| right << ConvolutionLayer( |
| 1U, 1U, base_depth, |
| get_weights_accessor(data_path, unit_name + "conv1_weights.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(1, 1, 0, 0)) |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_beta.npy"), |
| 0.0000100099996416f) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) |
| |
| << ConvolutionLayer( |
| 3U, 3U, base_depth, |
| get_weights_accessor(data_path, unit_name + "conv2_weights.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(middle_stride, middle_stride, 1, 1)) |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_beta.npy"), |
| 0.0000100099996416f) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) |
| |
| << ConvolutionLayer( |
| 1U, 1U, base_depth * 4, |
| get_weights_accessor(data_path, unit_name + "conv3_weights.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(1, 1, 0, 0)) |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_beta.npy"), |
| 0.0000100099996416f); |
| |
| if(i == 0) |
| { |
| SubGraph left; |
| left << ConvolutionLayer( |
| 1U, 1U, base_depth * 4, |
| get_weights_accessor(data_path, unit_name + "shortcut_weights.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(1, 1, 0, 0)) |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_beta.npy"), |
| 0.0000100099996416f); |
| |
| graph << ResidualLayer(std::move(left), std::move(right)); |
| } |
| else if(middle_stride > 1) |
| { |
| SubGraph left; |
| left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)) |
| // TODO (alegil01) : Remove once we understand why a single node graph does not run in CL |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f)); |
| |
| graph << ResidualLayer(std::move(left), std::move(right)); |
| } |
| else |
| { |
| graph << ResidualLayer(std::move(right)); |
| } |
| |
| graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| } |
| } |
| }; |
| |
| /** Main program for ResNet50 |
| * |
| * @param[in] argc Number of arguments |
| * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) |
| */ |
| int main(int argc, char **argv) |
| { |
| return arm_compute::utils::run_example<GraphResNet50Example>(argc, argv); |
| } |