1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
|
/*
* Copyright (c) 2016, 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.
*/
#include "arm_compute/runtime/NEON/NEFunctions.h"
#include "arm_compute/core/Types.h"
#include "utils/Utils.h"
using namespace arm_compute;
using namespace utils;
void main_cnn(int argc, const char **argv)
{
ARM_COMPUTE_UNUSED(argc);
ARM_COMPUTE_UNUSED(argv);
// The src tensor should contain the input image
Tensor src;
// The weights and biases tensors should be initialized with the values inferred with the training
Tensor weights0;
Tensor weights1;
Tensor weights2;
Tensor biases0;
Tensor biases1;
Tensor biases2;
Tensor out_conv0;
Tensor out_conv1;
Tensor out_act0;
Tensor out_act1;
Tensor out_act2;
Tensor out_pool0;
Tensor out_pool1;
Tensor out_fc0;
Tensor out_softmax;
NEConvolutionLayer conv0;
NEConvolutionLayer conv1;
NEPoolingLayer pool0;
NEPoolingLayer pool1;
NEFullyConnectedLayer fc0;
NEActivationLayer act0;
NEActivationLayer act1;
NEActivationLayer act2;
NESoftmaxLayer softmax;
/* [Initialize tensors] */
// Initialize src tensor
constexpr unsigned int width_src_image = 32;
constexpr unsigned int height_src_image = 32;
constexpr unsigned int ifm_src_img = 1;
const TensorShape src_shape(width_src_image, height_src_image, ifm_src_img);
src.allocator()->init(TensorInfo(src_shape, 1, DataType::F32));
// Initialize tensors of conv0
constexpr unsigned int kernel_x_conv0 = 5;
constexpr unsigned int kernel_y_conv0 = 5;
constexpr unsigned int ofm_conv0 = 8;
const TensorShape weights_shape_conv0(kernel_x_conv0, kernel_y_conv0, src_shape.z(), ofm_conv0);
const TensorShape biases_shape_conv0(weights_shape_conv0[3]);
const TensorShape out_shape_conv0(src_shape.x(), src_shape.y(), weights_shape_conv0[3]);
weights0.allocator()->init(TensorInfo(weights_shape_conv0, 1, DataType::F32));
biases0.allocator()->init(TensorInfo(biases_shape_conv0, 1, DataType::F32));
out_conv0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));
// Initialize tensor of act0
out_act0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));
// Initialize tensor of pool0
TensorShape out_shape_pool0 = out_shape_conv0;
out_shape_pool0.set(0, out_shape_pool0.x() / 2);
out_shape_pool0.set(1, out_shape_pool0.y() / 2);
out_pool0.allocator()->init(TensorInfo(out_shape_pool0, 1, DataType::F32));
// Initialize tensors of conv1
constexpr unsigned int kernel_x_conv1 = 3;
constexpr unsigned int kernel_y_conv1 = 3;
constexpr unsigned int ofm_conv1 = 16;
const TensorShape weights_shape_conv1(kernel_x_conv1, kernel_y_conv1, out_shape_pool0.z(), ofm_conv1);
const TensorShape biases_shape_conv1(weights_shape_conv1[3]);
const TensorShape out_shape_conv1(out_shape_pool0.x(), out_shape_pool0.y(), weights_shape_conv1[3]);
weights1.allocator()->init(TensorInfo(weights_shape_conv1, 1, DataType::F32));
biases1.allocator()->init(TensorInfo(biases_shape_conv1, 1, DataType::F32));
out_conv1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32));
// Initialize tensor of act1
out_act1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32));
// Initialize tensor of pool1
TensorShape out_shape_pool1 = out_shape_conv1;
out_shape_pool1.set(0, out_shape_pool1.x() / 2);
out_shape_pool1.set(1, out_shape_pool1.y() / 2);
out_pool1.allocator()->init(TensorInfo(out_shape_pool1, 1, DataType::F32));
// Initialize tensor of fc0
constexpr unsigned int num_labels = 128;
const TensorShape weights_shape_fc0(out_shape_pool1.x() * out_shape_pool1.y() * out_shape_pool1.z(), num_labels);
const TensorShape biases_shape_fc0(num_labels);
const TensorShape out_shape_fc0(num_labels);
weights2.allocator()->init(TensorInfo(weights_shape_fc0, 1, DataType::F32));
biases2.allocator()->init(TensorInfo(biases_shape_fc0, 1, DataType::F32));
out_fc0.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32));
// Initialize tensor of act2
out_act2.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32));
// Initialize tensor of softmax
const TensorShape out_shape_softmax(out_shape_fc0.x());
out_softmax.allocator()->init(TensorInfo(out_shape_softmax, 1, DataType::F32));
/* -----------------------End: [Initialize tensors] */
/* [Configure functions] */
// in:32x32x1: 5x5 convolution, 8 output features maps (OFM)
conv0.configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo());
// in:32x32x8, out:32x32x8, Activation function: relu
act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
// in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max
pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2));
// in:16x16x8: 3x3 convolution, 16 output features maps (OFM)
conv1.configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo());
// in:16x16x16, out:16x16x16, Activation function: relu
act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
// in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average
pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2));
// in:8x8x16, out:128
fc0.configure(&out_pool1, &weights2, &biases2, &out_fc0);
// in:128, out:128, Activation function: relu
act2.configure(&out_fc0, &out_act2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
// in:128, out:128
softmax.configure(&out_act2, &out_softmax);
/* -----------------------End: [Configure functions] */
/* [Allocate tensors] */
// Now that the padding requirements are known we can allocate the images:
src.allocator()->allocate();
weights0.allocator()->allocate();
weights1.allocator()->allocate();
weights2.allocator()->allocate();
biases0.allocator()->allocate();
biases1.allocator()->allocate();
biases2.allocator()->allocate();
out_conv0.allocator()->allocate();
out_conv1.allocator()->allocate();
out_act0.allocator()->allocate();
out_act1.allocator()->allocate();
out_act2.allocator()->allocate();
out_pool0.allocator()->allocate();
out_pool1.allocator()->allocate();
out_fc0.allocator()->allocate();
out_softmax.allocator()->allocate();
/* -----------------------End: [Allocate tensors] */
/* [Initialize weights and biases tensors] */
// Once the tensors have been allocated, the src, weights and biases tensors can be initialized
// ...
/* -----------------------[Initialize weights and biases tensors] */
/* [Execute the functions] */
conv0.run();
act0.run();
pool0.run();
conv1.run();
act1.run();
pool1.run();
fc0.run();
act2.run();
softmax.run();
/* -----------------------End: [Execute the functions] */
}
/** Main program for cnn test
*
* The example implements the following CNN architecture:
*
* Input -> conv0:5x5 -> act0:relu -> pool:2x2 -> conv1:3x3 -> act1:relu -> pool:2x2 -> fc0 -> act2:relu -> softmax
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments
*/
int main(int argc, const char **argv)
{
return utils::run_example(argc, argv, main_cnn);
}
|
