| /* |
| * 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 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] image, [optional] labels ) |
| */ |
| class GraphMobilenetExample : 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 |
| std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(); |
| |
| // 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); |
| ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::GEMM; |
| |
| // Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160) |
| int model_id = (argc > 2) ? std::strtol(argv[2], nullptr, 10) : 0; |
| ARM_COMPUTE_ERROR_ON_MSG(model_id > 1, "Invalid model ID. Model must be 0 (MobileNetV1_1.0_224) or 1 (MobileNetV1_0.75_160)"); |
| float depth_scale = (model_id == 0) ? 1.f : 0.75; |
| unsigned int spatial_size = (model_id == 0) ? 224 : 160; |
| std::string model_path = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/"; |
| |
| // Parse arguments |
| if(argc < 2) |
| { |
| // Print help |
| std::cout << "Usage: " << argv[0] << " [target] [model] [path_to_data] [image] [labels]\n\n"; |
| std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n"; |
| std::cout << "No data folder provided: using random values\n\n"; |
| } |
| else if(argc == 2) |
| { |
| std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [path_to_data] [image] [labels]\n\n"; |
| std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n"; |
| std::cout << "No data folder provided: using random values\n\n"; |
| } |
| else if(argc == 3) |
| { |
| std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [path_to_data] [image] [labels]\n\n"; |
| std::cout << "No data folder provided: using random values\n\n"; |
| } |
| else if(argc == 4) |
| { |
| data_path = argv[3]; |
| std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [image] [labels]\n\n"; |
| std::cout << "No image provided: using random values\n\n"; |
| } |
| else if(argc == 5) |
| { |
| data_path = argv[3]; |
| image = argv[4]; |
| 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[3]; |
| image = argv[4]; |
| label = argv[5]; |
| } |
| |
| // Add model path to data path |
| if(!data_path.empty()) |
| { |
| data_path += model_path; |
| } |
| |
| // Initialize graph |
| graph.graph_init(int_target_hint == 2); |
| |
| graph << target_hint |
| << convolution_hint |
| << Tensor(TensorInfo(TensorShape(spatial_size, spatial_size, 3U, 1U), 1, DataType::F32), |
| get_input_accessor(image, std::move(preprocessor), false)) |
| << ConvolutionLayer( |
| 3U, 3U, 32U * depth_scale, |
| get_weights_accessor(data_path, "Conv2d_0_weights.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)) |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"), |
| 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) |
| << get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)) |
| << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) |
| << ConvolutionLayer( |
| 1U, 1U, 1001U, |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"), |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), |
| PadStrideInfo(1, 1, 0, 0)) |
| << ReshapeLayer(TensorShape(1001U)) |
| << SoftmaxLayer() |
| << Tensor(get_output_accessor(label, 5)); |
| } |
| void do_run() override |
| { |
| // Run graph |
| graph.run(); |
| } |
| |
| private: |
| Graph graph{}; |
| |
| BranchLayer get_dwsc_node(const std::string &data_path, std::string &¶m_path, |
| unsigned int conv_filt, |
| PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info) |
| { |
| std::string total_path = param_path + "_"; |
| SubGraph sg; |
| sg << DepthwiseConvolutionLayer( |
| 3U, 3U, |
| get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| dwc_pad_stride_info, |
| true) |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), |
| 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) |
| << ConvolutionLayer( |
| 1U, 1U, conv_filt, |
| get_weights_accessor(data_path, total_path + "pointwise_weights.npy"), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| conv_pad_stride_info) |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"), |
| 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)); |
| |
| return BranchLayer(std::move(sg)); |
| } |
| }; |
| |
| /** Main program for MobileNetV1 |
| * |
| * @param[in] argc Number of arguments |
| * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), |
| * [optional] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160), |
| * [optional] Path to the weights folder, |
| * [optional] image, |
| * [optional] labels ) |
| */ |
| int main(int argc, char **argv) |
| { |
| return arm_compute::utils::run_example<GraphMobilenetExample>(argc, argv); |
| } |