Blame - examples/graph_vgg19.cpp - ml/ComputeLibrary

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

1

/*

Michele Di Giorgio

e3fba0a

2018-02-14 14:18:01 +0000

[diff] [blame]

2

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

3

*

4

* SPDX-License-Identifier: MIT

5

*

6

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

7

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

8

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

9

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

10

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

11

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

12

*

13

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

14

* copies or substantial portions of the Software.

15

*

16

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

17

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

18

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

19

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

20

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

21

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

22

* SOFTWARE.

23

*/

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

24

#include "arm_compute/graph/Graph.h"

25

#include "arm_compute/graph/Nodes.h"

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

26

#include "support/ToolchainSupport.h"

27

#include "utils/GraphUtils.h"

28

#include "utils/Utils.h"

#include <cstdlib>

Michalis Spyrou

2018-01-10 14:08:50 +0000

[diff] [blame]

32

using namespace arm_compute::utils;

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

33

using namespace arm_compute::graph;

34

using namespace arm_compute::graph_utils;

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

35

36

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

37

*

38

* @param[in] argc Number of arguments

Gian Marco

bfa3b52

2017-12-12 10:08:38 +0000

[diff] [blame]

39

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

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

40

*/

Michalis Spyrou

2b5f0f2

2018-01-10 14:08:50 +0000

[diff] [blame]

41

class GraphVGG19Example : public Example

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

42

{

Michalis Spyrou

2b5f0f2

2018-01-10 14:08:50 +0000

[diff] [blame]

43

public:

44

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

45

{

46

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

47

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

48

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

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

49

Georgios Pinitas

140fdc7

2018-02-16 11:42:38 +0000

[diff] [blame]

50

// Create a preprocessor object

51

const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };

52

std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

53

Michele Di Giorgio

e3fba0a

2018-02-14 14:18:01 +0000

[diff] [blame]

54

// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON

55

const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;

56

TargetHint target_hint = set_target_hint(int_target_hint);

Michalis Spyrou

2b5f0f2

2018-01-10 14:08:50 +0000

[diff] [blame]

57

ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::DIRECT;

Gian Marco

bfa3b52

2017-12-12 10:08:38 +0000

[diff] [blame]

58

Michalis Spyrou

2b5f0f2

2018-01-10 14:08:50 +0000

[diff] [blame]

// Parse arguments

if(argc < 2)

{

// Print help

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

64

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";

69

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";

75

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";

82

std::cout << "No text file with labels provided: skipping output accessor\n\n";

}

else

{

data_path = argv[2];

image = argv[3];

label = argv[4];

}

Michele Di Giorgio

2018-02-14 14:18:01 +0000

[diff] [blame]

91

// Initialize graph

92

graph.graph_init(int_target_hint == 2);

93

Michalis Spyrou

2b5f0f2

2018-01-10 14:08:50 +0000

[diff] [blame]

94

graph << target_hint

95

<< convolution_hint

96

<< Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),

Georgios Pinitas

140fdc7

2018-02-16 11:42:38 +0000

[diff] [blame]

97

get_input_accessor(image, std::move(preprocessor)))

Michalis Spyrou

2b5f0f2

2018-01-10 14:08:50 +0000

[diff] [blame]

// Layer 1

<< ConvolutionLayer(

3U, 3U, 64U,

get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy"),

102

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

103

PadStrideInfo(1, 1, 1, 1))

104

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

105

<< ConvolutionLayer(

106

3U, 3U, 64U,

107

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

108

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

109

PadStrideInfo(1, 1, 1, 1))

110

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

111

<< 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"),

116

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

117

PadStrideInfo(1, 1, 1, 1))

118

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

119

<< ConvolutionLayer(

120

3U, 3U, 128U,

121

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

122

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

123

PadStrideInfo(1, 1, 1, 1))

124

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

125

<< 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"),

130

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

131

PadStrideInfo(1, 1, 1, 1))

132

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

133

<< ConvolutionLayer(

134

3U, 3U, 256U,

135

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

136

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

137

PadStrideInfo(1, 1, 1, 1))

138

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

139

<< ConvolutionLayer(

140

3U, 3U, 256U,

141

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

142

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

143

PadStrideInfo(1, 1, 1, 1))

144

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

145

<< ConvolutionLayer(

146

3U, 3U, 256U,

147

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

148

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

149

PadStrideInfo(1, 1, 1, 1))

150

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

151

<< 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"),

156

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

157

PadStrideInfo(1, 1, 1, 1))

158

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

159

<< ConvolutionLayer(

160

3U, 3U, 512U,

161

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

162

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

163

PadStrideInfo(1, 1, 1, 1))

164

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

165

<< ConvolutionLayer(

166

3U, 3U, 512U,

167

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

168

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

169

PadStrideInfo(1, 1, 1, 1))

170

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

171

<< ConvolutionLayer(

172

3U, 3U, 512U,

173

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

174

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

175

PadStrideInfo(1, 1, 1, 1))

176

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

177

<< 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"),

182

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

183

PadStrideInfo(1, 1, 1, 1))

184

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

185

<< ConvolutionLayer(

186

3U, 3U, 512U,

187

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

188

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

189

PadStrideInfo(1, 1, 1, 1))

190

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

191

<< ConvolutionLayer(

192

3U, 3U, 512U,

193

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

194

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

195

PadStrideInfo(1, 1, 1, 1))

196

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

197

<< ConvolutionLayer(

198

3U, 3U, 512U,

199

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

200

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

201

PadStrideInfo(1, 1, 1, 1))

202

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

203

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

204

// Layer 6

205

<< FullyConnectedLayer(

206

4096U,

207

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

208

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

209

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

210

// Layer 7

211

<< FullyConnectedLayer(

212

4096U,

213

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

214

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

215

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

216

// Layer 8

217

<< FullyConnectedLayer(

218

1000U,

219

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

220

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

221

// Softmax

222

<< SoftmaxLayer()

223

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

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

224

}

Michalis Spyrou

2b5f0f2

2018-01-10 14:08:50 +0000

[diff] [blame]

225

void do_run() override

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

226

{

Michalis Spyrou

2b5f0f2

2018-01-10 14:08:50 +0000

[diff] [blame]

227

// Run graph

228

graph.run();

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

229

}

230

Michalis Spyrou

2b5f0f2

2018-01-10 14:08:50 +0000

[diff] [blame]

231

private:

232

Graph graph{};

233

};

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

234

235

/** Main program for VGG19

236

*

237

* @param[in] argc Number of arguments

Gian Marco

bfa3b52

2017-12-12 10:08:38 +0000

[diff] [blame]

238

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

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

239

*/

Anthony Barbier

6db0ff5

2018-01-05 10:59:12 +0000

[diff] [blame]

240

int main(int argc, char **argv)

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

241

{

Michalis Spyrou

2b5f0f2

2018-01-10 14:08:50 +0000

[diff] [blame]

242

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

Isabella Gottardi

9f20bda

2017-11-03 17:16:20 +0000

[diff] [blame]

243

}