From 9f20bda35e61e5f80dcd0c9f4cbb9646ebf0e493 Mon Sep 17 00:00:00 2001 From: Isabella Gottardi Date: Fri, 3 Nov 2017 17:16:20 +0000 Subject: COMPMID-656 - Create VGG-19 example Change-Id: Ie26904a3b232ed614a3a063f7deb24995249e820 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/94657 Tested-by: Kaizen Reviewed-by: Gian Marco Iodice --- examples/graph_vgg19.cpp | 236 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 236 insertions(+) create mode 100644 examples/graph_vgg19.cpp (limited to 'examples/graph_vgg19.cpp') diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp new file mode 100644 index 0000000000..74cb65ab69 --- /dev/null +++ b/examples/graph_vgg19.cpp @@ -0,0 +1,236 @@ +/* + * Copyright (c) 2017 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. + */ +#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */ +#error "This example needs to be built with -DARM_COMPUTE_CL" +#endif /* ARM_COMPUTE_CL */ + +#include "arm_compute/graph/Graph.h" +#include "arm_compute/graph/Nodes.h" +#include "arm_compute/runtime/CL/CLScheduler.h" +#include "arm_compute/runtime/Scheduler.h" +#include "support/ToolchainSupport.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +#include + +using namespace arm_compute::graph; +using namespace arm_compute::graph_utils; +using namespace arm_compute::logging; + +/** Example demonstrating how to implement VGG19's network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +void main_graph_vgg19(int argc, const char **argv) +{ + std::string data_path; /* Path to the trainable data */ + std::string image; /* Image data */ + std::string label; /* Label data */ + + constexpr float mean_r = 123.68f; /* Mean value to subtract from red channel */ + constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */ + constexpr float mean_b = 103.939f; /* Mean value to subtract from blue channel */ + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 2) + { + data_path = argv[1]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n"; + std::cout << "No image provided: using random values\n\n"; + } + else if(argc == 3) + { + data_path = argv[1]; + image = argv[2]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [labels]\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n\n"; + } + else + { + data_path = argv[1]; + image = argv[2]; + label = argv[3]; + } + + // Check if OpenCL is available and initialize the scheduler + TargetHint hint = TargetHint::NEON; + if(arm_compute::opencl_is_available()) + { + arm_compute::CLScheduler::get().default_init(); + hint = TargetHint::OPENCL; + } + + Graph graph; + + graph << hint + << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), + get_input_accessor(image, mean_r, mean_g, mean_b)) + // Layer 1 + << ConvolutionLayer( + 3U, 3U, 64U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 64U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))) + // Layer 2 + << ConvolutionLayer( + 3U, 3U, 128U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 128U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))) + // Layer 3 + << ConvolutionLayer( + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))) + // Layer 4 + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))) + // Layer 5 + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))) + // Layer 6 + << FullyConnectedLayer( + 4096U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy")) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + // Layer 7 + << FullyConnectedLayer( + 4096U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy")) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + // Layer 8 + << FullyConnectedLayer( + 1000U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy")) + // Softmax + << SoftmaxLayer() + << Tensor(get_output_accessor(label, 5)); + + // Run graph + graph.run(); +} + +/** Main program for VGG19 + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +int main(int argc, const char **argv) +{ + return arm_compute::utils::run_example(argc, argv, main_graph_vgg19); +} -- cgit v1.2.1