Blame - examples/graph_vgg19.cpp - ml/ComputeLibrary

Isabella Gottardi

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/*

Anthony Barbier

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Isabella Gottardi

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*

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* SPDX-License-Identifier: MIT

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*

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* Permission is hereby granted, free of charge, to any person obtaining a copy

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* of this software and associated documentation files (the "Software"), to

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* deal in the Software without restriction, including without limitation the

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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or

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* sell copies of the Software, and to permit persons to whom the Software is

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* furnished to do so, subject to the following conditions:

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*

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* The above copyright notice and this permission notice shall be included in all

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* copies or substantial portions of the Software.

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*

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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

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* SOFTWARE.

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*/

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#include "arm_compute/graph/Graph.h"

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#include "arm_compute/graph/Nodes.h"

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#include "support/ToolchainSupport.h"

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#include "utils/GraphUtils.h"

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#include "utils/Utils.h"

#include <cstdlib>

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using namespace arm_compute::utils;

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using namespace arm_compute::graph;

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using namespace arm_compute::graph_utils;

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/** Example demonstrating how to implement VGG19's network using the Compute Library's graph API

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*

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* @param[in] argc Number of arguments

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* @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )

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*/

Michalis Spyrou

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class GraphVGG19Example : public Example

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{

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public:

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void do_setup(int argc, char **argv) override

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{

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std::string data_path; /* Path to the trainable data */

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std::string image; /* Image data */

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std::string label; /* Label data */

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Michalis Spyrou

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constexpr float mean_r = 123.68f; /* Mean value to subtract from red channel */

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constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */

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constexpr float mean_b = 103.939f; /* Mean value to subtract from blue channel */

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Michalis Spyrou

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// Set target. 0 (NEON), 1 (OpenCL). By default it is NEON

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TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);

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ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::DIRECT;

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Michalis Spyrou

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// Parse arguments

if(argc < 2)

{

// Print help

std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";

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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";

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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";

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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";

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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),

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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"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< ConvolutionLayer(

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3U, 3U, 64U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< 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"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< ConvolutionLayer(

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3U, 3U, 128U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< 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"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< ConvolutionLayer(

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3U, 3U, 256U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< ConvolutionLayer(

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3U, 3U, 256U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< ConvolutionLayer(

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3U, 3U, 256U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< 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"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< ConvolutionLayer(

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3U, 3U, 512U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< ConvolutionLayer(

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3U, 3U, 512U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< ConvolutionLayer(

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3U, 3U, 512U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< 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"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< ConvolutionLayer(

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3U, 3U, 512U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< ConvolutionLayer(

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3U, 3U, 512U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< ConvolutionLayer(

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3U, 3U, 512U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"),

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PadStrideInfo(1, 1, 1, 1))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))

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// Layer 6

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<< FullyConnectedLayer(

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4096U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy"))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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// Layer 7

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<< FullyConnectedLayer(

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4096U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy"))

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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))

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// Layer 8

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<< FullyConnectedLayer(

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1000U,

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy"),

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get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy"))

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// Softmax

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<< SoftmaxLayer()

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<< Tensor(get_output_accessor(label, 5));

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}

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void do_run() override

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{

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// Run graph

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graph.run();

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}

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private:

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Graph graph{};

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};

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/** Main program for VGG19

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*

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* @param[in] argc Number of arguments

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* @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )

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*/

Anthony Barbier

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int main(int argc, char **argv)

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{

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return arm_compute::utils::run_example<GraphVGG19Example>(argc, argv);

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}