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review.mlplatform.org / ml / ComputeLibrary / 09849a0e7128731473f37cf6045147db68b1c495 / . / examples / graph_vgg16.cpp
blob: cac38d30a7e2b5f6ae87f4d612249de2a4a14274 [file] [log] [blame]
Gian Marco Iodicee10bddb2017-10-11 15:03:26 +0100[diff] [blame]1/*
2 * Copyright (c) 2017 ARM Limited.
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 */
Gian Marco Iodicee10bddb2017-10-11 15:03:26 +0100[diff] [blame]24#include "arm_compute/graph/Graph.h"
25#include "arm_compute/graph/Nodes.h"
Gian Marco Iodicee10bddb2017-10-11 15:03:26 +0100[diff] [blame]26#include "support/ToolchainSupport.h"
27#include "utils/GraphUtils.h"
28#include "utils/Utils.h"
29
30#include <cstdlib>
31
32using namespace arm_compute::graph;
33using namespace arm_compute::graph_utils;
34
35/** Example demonstrating how to implement VGG16's network using the Compute Library's graph API
36 *
37 * @param[in] argc Number of arguments
Gian Marcobfa3b522017-12-12 10:08:38 +0000[diff] [blame]38 * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
Gian Marco Iodicee10bddb2017-10-11 15:03:26 +0100[diff] [blame]39 */
40void main_graph_vgg16(int argc, const char **argv)
41{
42 std::string data_path; /* Path to the trainable data */
43 std::string image; /* Image data */
44 std::string label; /* Label data */
45
46 constexpr float mean_r = 123.68f; /* Mean value to subtract from red channel */
47 constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */
48 constexpr float mean_b = 103.939f; /* Mean value to subtract from blue channel */
49
Gian Marcobfa3b522017-12-12 10:08:38 +0000[diff] [blame]50 // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
51 TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
52 ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::DIRECT;
53
Gian Marco Iodicee10bddb2017-10-11 15:03:26 +0100[diff] [blame]54 // Parse arguments
55 if(argc < 2)
56 {
57 // Print help
Gian Marcobfa3b522017-12-12 10:08:38 +0000[diff] [blame]58 std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
Gian Marco Iodicee10bddb2017-10-11 15:03:26 +0100[diff] [blame]59 std::cout << "No data folder provided: using random values\n\n";
60 }
61 else if(argc == 2)
62 {
Gian Marcobfa3b522017-12-12 10:08:38 +0000[diff] [blame]63 std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
64 std::cout << "No data folder provided: using random values\n\n";
Gian Marco Iodicee10bddb2017-10-11 15:03:26 +0100[diff] [blame]65 }
66 else if(argc == 3)
67 {
Gian Marcobfa3b522017-12-12 10:08:38 +0000[diff] [blame]68 data_path = argv[2];
69 std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
70 std::cout << "No image provided: using random values\n\n";
71 }
72 else if(argc == 4)
73 {
74 data_path = argv[2];
75 image = argv[3];
76 std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
Gian Marco Iodicee10bddb2017-10-11 15:03:26 +0100[diff] [blame]77 std::cout << "No text file with labels provided: skipping output accessor\n\n";
78 }
79 else
80 {
Gian Marcobfa3b522017-12-12 10:08:38 +0000[diff] [blame]81 data_path = argv[2];
82 image = argv[3];
83 label = argv[4];
Gian Marco Iodicee10bddb2017-10-11 15:03:26 +0100[diff] [blame]84 }
85
86 Graph graph;
87
Gian Marcobfa3b522017-12-12 10:08:38 +0000[diff] [blame]88 graph << target_hint
89 << convolution_hint
Gian Marco Iodicee10bddb2017-10-11 15:03:26 +0100[diff] [blame]90 << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
91 get_input_accessor(image, mean_r, mean_g, mean_b))
92 << ConvolutionMethodHint::DIRECT
93 // Layer 1
94 << ConvolutionLayer(
95 3U, 3U, 64U,
96 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_w.npy"),
97 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_b.npy"),
98 PadStrideInfo(1, 1, 1, 1))
99 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
100 // Layer 2
101 << ConvolutionLayer(
102 3U, 3U, 64U,
103 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_w.npy"),
104 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_b.npy"),
105 PadStrideInfo(1, 1, 1, 1))
106 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
107 << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
108 // Layer 3
109 << ConvolutionLayer(
110 3U, 3U, 128U,
111 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_w.npy"),
112 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_b.npy"),
113 PadStrideInfo(1, 1, 1, 1))
114 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
115 // Layer 4
116 << ConvolutionLayer(
117 3U, 3U, 128U,
118 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_w.npy"),
119 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_b.npy"),
120 PadStrideInfo(1, 1, 1, 1))
121 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
122 << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
123 // Layer 5
124 << ConvolutionLayer(
125 3U, 3U, 256U,
126 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_w.npy"),
127 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_b.npy"),
128 PadStrideInfo(1, 1, 1, 1))
129 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
130 // Layer 6
131 << ConvolutionLayer(
132 3U, 3U, 256U,
133 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_w.npy"),
134 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_b.npy"),
135 PadStrideInfo(1, 1, 1, 1))
136 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
137 // Layer 7
138 << ConvolutionLayer(
139 3U, 3U, 256U,
140 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_w.npy"),
141 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_b.npy"),
142 PadStrideInfo(1, 1, 1, 1))
143 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
144 << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
145 // Layer 8
146 << ConvolutionLayer(
147 3U, 3U, 512U,
148 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_w.npy"),
149 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_b.npy"),
150 PadStrideInfo(1, 1, 1, 1))
151 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
152 // Layer 9
153 << ConvolutionLayer(
154 3U, 3U, 512U,
155 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_w.npy"),
156 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_b.npy"),
157 PadStrideInfo(1, 1, 1, 1))
158 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
159 // Layer 10
160 << ConvolutionLayer(
161 3U, 3U, 512U,
162 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_w.npy"),
163 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_b.npy"),
164 PadStrideInfo(1, 1, 1, 1))
165 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
166 << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
167 // Layer 11
168 << ConvolutionLayer(
169 3U, 3U, 512U,
170 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_w.npy"),
171 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_b.npy"),
172 PadStrideInfo(1, 1, 1, 1))
173 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
174 // Layer 12
175 << ConvolutionLayer(
176 3U, 3U, 512U,
177 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_w.npy"),
178 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_b.npy"),
179 PadStrideInfo(1, 1, 1, 1))
180 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
181 // Layer 13
182 << ConvolutionLayer(
183 3U, 3U, 512U,
184 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_w.npy"),
185 get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_b.npy"),
186 PadStrideInfo(1, 1, 1, 1))
187 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
188 << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
189 // Layer 14
190 << FullyConnectedLayer(
191 4096U,
192 get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_w.npy"),
193 get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_b.npy"))
194 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
195 // Layer 15
196 << FullyConnectedLayer(
197 4096U,
198 get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_w.npy"),
199 get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_b.npy"))
200 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
201 // Layer 16
202 << FullyConnectedLayer(
203 1000U,
204 get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_w.npy"),
205 get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_b.npy"))
206 // Softmax
207 << SoftmaxLayer()
208 << Tensor(get_output_accessor(label, 5));
209
210 // Run graph
211 graph.run();
212}
213
214/** Main program for VGG16
215 *
216 * @param[in] argc Number of arguments
Gian Marcobfa3b522017-12-12 10:08:38 +0000[diff] [blame]217 * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
Gian Marco Iodicee10bddb2017-10-11 15:03:26 +0100[diff] [blame]218 */
219int main(int argc, const char **argv)
220{
221 return arm_compute::utils::run_example(argc, argv, main_graph_vgg16);
222}