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/*
* Copyright (c) 2017-2018 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::utils;
using namespace arm_compute::graph;
using namespace arm_compute::graph_utils;
namespace
{
/** This function checks if we can use GEMM-based convolution trying to allocate a memory of size "size_in_bytes"
*
* @param[in] size_in_bytes Memory size in bytes needed for VGG-16
*
* @return The convolution layer hint
*/
ConvolutionMethodHint convolution_hint_vgg16(size_t size_in_bytes)
{
return ((get_mem_free_from_meminfo() * 1024) >= size_in_bytes) ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
}
} // namespace
/** 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 )
*/
class GraphVGG16Example : public Example
{
public:
void do_setup(int argc, char **argv) override
{
std::string data_path; /* Path to the trainable data */
std::string image; /* Image data */
std::string label; /* Label data */
// Create a preprocessor object
const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
TargetHint target_hint = set_target_hint(int_target_hint);
// Check if we can use GEMM-based convolutions evaluating if the platform has at least 1.8 GB of available memory
const size_t memory_required = 1932735283L;
ConvolutionMethodHint convolution_hint = convolution_hint_vgg16(memory_required);
// 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 << target_hint
<< convolution_hint
<< Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
get_input_accessor(image, std::move(preprocessor)))
// 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));
// In order to enable the OpenCL tuner, graph_init() has to be called only when all nodes have been instantiated
graph.graph_init(int_target_hint == 2);
}
void do_run() override
{
// Run graph
graph.run();
}
private:
Graph graph{};
};
/** 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, char **argv)
{
return arm_compute::utils::run_example<GraphVGG16Example>(argc, argv);
}
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