| /* | |
| * Copyright (c) 2017-2021 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/CommonGraphOptions.h" | |
| #include "utils/GraphUtils.h" | |
| #include "utils/Utils.h" | |
| using namespace arm_compute::utils; | |
| using namespace arm_compute::graph::frontend; | |
| using namespace arm_compute::graph_utils; | |
| /** Example demonstrating how to implement VGG19's network using the Compute Library's graph API */ | |
| class GraphVGG19Example : public Example | |
| { | |
| public: | |
| GraphVGG19Example() | |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "VGG19") | |
| { | |
| } | |
| bool do_setup(int argc, char **argv) override | |
| { | |
| // Parse arguments | |
| cmd_parser.parse(argc, argv); | |
| cmd_parser.validate(); | |
| // Consume common parameters | |
| common_params = consume_common_graph_parameters(common_opts); | |
| // Return when help menu is requested | |
| if(common_params.help) | |
| { | |
| cmd_parser.print_help(argv[0]); | |
| return false; | |
| } | |
| // Print parameter values | |
| std::cout << common_params << std::endl; | |
| // Get trainable parameters data path | |
| std::string data_path = common_params.data_path; | |
| // Create a preprocessor object | |
| const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } }; | |
| std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb); | |
| // Create input descriptor | |
| const auto operation_layout = common_params.data_layout; | |
| const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout); | |
| TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); | |
| // Set weights trained layout | |
| const DataLayout weights_layout = DataLayout::NCHW; | |
| graph << common_params.target | |
| << common_params.fast_math_hint | |
| << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor))) | |
| // Layer 1 | |
| << ConvolutionLayer( | |
| 3U, 3U, 64U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv1_1") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_1/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, 64U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv1_2") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_2/Relu") | |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") | |
| // Layer 2 | |
| << ConvolutionLayer( | |
| 3U, 3U, 128U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv2_1") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_1/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, 128U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv2_2") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_2/Relu") | |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") | |
| // Layer 3 | |
| << ConvolutionLayer( | |
| 3U, 3U, 256U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv3_1") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_1/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, 256U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv3_2") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_2/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, 256U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv3_3") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_3/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, 256U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv3_4") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_4/Relu") | |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool3") | |
| // Layer 4 | |
| << ConvolutionLayer( | |
| 3U, 3U, 512U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv4_1") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_1/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, 512U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv4_2") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_2/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, 512U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv4_3") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_3/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, 512U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv4_4") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_4/Relu") | |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool4") | |
| // Layer 5 | |
| << ConvolutionLayer( | |
| 3U, 3U, 512U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv5_1") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_1/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, 512U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv5_2") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_2/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, 512U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv5_3") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_3/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, 512U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("conv5_4") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_4/Relu") | |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5") | |
| // Layer 6 | |
| << FullyConnectedLayer( | |
| 4096U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy")) | |
| .set_name("fc6") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu") | |
| // Layer 7 | |
| << FullyConnectedLayer( | |
| 4096U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy")) | |
| .set_name("fc7") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1") | |
| // Layer 8 | |
| << FullyConnectedLayer( | |
| 1000U, | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy")) | |
| .set_name("fc8") | |
| // Softmax | |
| << SoftmaxLayer().set_name("prob") | |
| << OutputLayer(get_output_accessor(common_params, 5)); | |
| // Finalize graph | |
| GraphConfig config; | |
| config.num_threads = common_params.threads; | |
| config.use_tuner = common_params.enable_tuner; | |
| config.tuner_mode = common_params.tuner_mode; | |
| config.tuner_file = common_params.tuner_file; | |
| config.mlgo_file = common_params.mlgo_file; | |
| config.use_synthetic_type = arm_compute::is_data_type_quantized(common_params.data_type); | |
| config.synthetic_type = common_params.data_type; | |
| graph.finalize(common_params.target, config); | |
| return true; | |
| } | |
| void do_run() override | |
| { | |
| // Run graph | |
| graph.run(); | |
| } | |
| private: | |
| CommandLineParser cmd_parser; | |
| CommonGraphOptions common_opts; | |
| CommonGraphParams common_params; | |
| Stream graph; | |
| }; | |
| /** Main program for VGG19 | |
| * | |
| * Model is based on: | |
| * https://arxiv.org/abs/1409.1556 | |
| * "Very Deep Convolutional Networks for Large-Scale Image Recognition" | |
| * Karen Simonyan, Andrew Zisserman | |
| * | |
| * Provenance: www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel | |
| * | |
| * @note To list all the possible arguments execute the binary appended with the --help option | |
| * | |
| * @param[in] argc Number of arguments | |
| * @param[in] argv Arguments | |
| */ | |
| int main(int argc, char **argv) | |
| { | |
| return arm_compute::utils::run_example<GraphVGG19Example>(argc, argv); | |
| } |