| /* |
| * 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 VGG16's network using the Compute Library's graph API */ |
| class GraphVGG16Example : public Example |
| { |
| public: |
| GraphVGG16Example() |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "VGG16") |
| { |
| } |
| 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; |
| |
| // Create graph |
| 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/vgg16_model/conv1_1_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv1_1") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_1/Relu") |
| // Layer 2 |
| << ConvolutionLayer( |
| 3U, 3U, 64U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_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 3 |
| << ConvolutionLayer( |
| 3U, 3U, 128U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv2_1") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_1/Relu") |
| // Layer 4 |
| << ConvolutionLayer( |
| 3U, 3U, 128U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_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 5 |
| << ConvolutionLayer( |
| 3U, 3U, 256U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv3_1") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_1/Relu") |
| // Layer 6 |
| << ConvolutionLayer( |
| 3U, 3U, 256U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv3_2") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_2/Relu") |
| // Layer 7 |
| << ConvolutionLayer( |
| 3U, 3U, 256U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv3_3") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_3/Relu") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool3") |
| // Layer 8 |
| << ConvolutionLayer( |
| 3U, 3U, 512U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv4_1") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_1/Relu") |
| // Layer 9 |
| << ConvolutionLayer( |
| 3U, 3U, 512U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv4_2") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_2/Relu") |
| // Layer 10 |
| << ConvolutionLayer( |
| 3U, 3U, 512U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv4_3") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_3/Relu") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool4") |
| // Layer 11 |
| << ConvolutionLayer( |
| 3U, 3U, 512U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv5_1") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_1/Relu") |
| // Layer 12 |
| << ConvolutionLayer( |
| 3U, 3U, 512U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv5_2") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_2/Relu") |
| // Layer 13 |
| << ConvolutionLayer( |
| 3U, 3U, 512U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv5_3") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_3/Relu") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5") |
| // Layer 14 |
| << FullyConnectedLayer( |
| 4096U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_b.npy")) |
| .set_name("fc6") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu") |
| // Layer 15 |
| << FullyConnectedLayer( |
| 4096U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_b.npy")) |
| .set_name("fc7") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1") |
| // Layer 16 |
| << FullyConnectedLayer( |
| 1000U, |
| get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/vgg16_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 VGG16 |
| * |
| * 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_16_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<GraphVGG16Example>(argc, argv); |
| } |
