COMPMID-556 - Add VGG16 graph sample code

Change-Id: I9440581e387ac5763b72444130c617424b14e1aa
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/91424
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

1 files changed, 225 insertions, 0 deletions

diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
new file mode 100644
index 0000000000..ba59503161
--- /dev/null
+++ 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.
+ */
+#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 "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] 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 */
+
+    // 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))
+          << 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] 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);
+}