arm_compute v17.12
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
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+/*
+ * 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.
+ */
+#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::graph;
+using namespace arm_compute::graph_utils;
+
+/** Example demonstrating how to implement VGG16'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), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ */
+void main_graph_vgg16(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 */
+
+ // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
+ TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
+ ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::DIRECT;
+
+ // 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];
+ }
+
+ Graph graph;
+
+ graph << target_hint
+ << convolution_hint
+ << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
+ get_input_accessor(image, mean_r, mean_g, mean_b))
+ << ConvolutionMethodHint::DIRECT
+ // Layer 1
+ << ConvolutionLayer(
+ 3U, 3U, 64U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_b.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ // Layer 2
+ << ConvolutionLayer(
+ 3U, 3U, 64U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_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 3
+ << ConvolutionLayer(
+ 3U, 3U, 128U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_b.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ // Layer 4
+ << ConvolutionLayer(
+ 3U, 3U, 128U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_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 5
+ << ConvolutionLayer(
+ 3U, 3U, 256U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_b.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ // Layer 6
+ << ConvolutionLayer(
+ 3U, 3U, 256U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_b.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ // Layer 7
+ << ConvolutionLayer(
+ 3U, 3U, 256U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_b.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+ // Layer 8
+ << ConvolutionLayer(
+ 3U, 3U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_b.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ // Layer 9
+ << ConvolutionLayer(
+ 3U, 3U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_b.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ // Layer 10
+ << ConvolutionLayer(
+ 3U, 3U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_b.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+ // Layer 11
+ << ConvolutionLayer(
+ 3U, 3U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_b.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ // Layer 12
+ << ConvolutionLayer(
+ 3U, 3U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_b.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ // Layer 13
+ << ConvolutionLayer(
+ 3U, 3U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_b.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+ // Layer 14
+ << FullyConnectedLayer(
+ 4096U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_b.npy"))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ // Layer 15
+ << FullyConnectedLayer(
+ 4096U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_b.npy"))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ // Layer 16
+ << FullyConnectedLayer(
+ 1000U,
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_b.npy"))
+ // Softmax
+ << SoftmaxLayer()
+ << Tensor(get_output_accessor(label, 5));
+
+ // Run graph
+ graph.run();
+}
+
+/** Main program for VGG16
+ *
+ * @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, const char **argv)
+{
+ return arm_compute::utils::run_example(argc, argv, main_graph_vgg16);
+}