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
|
/*
* 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/graph2.h"
#include "support/ToolchainSupport.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
#include <cstdlib>
using namespace arm_compute::utils;
using namespace arm_compute::graph2::frontend;
using namespace arm_compute::graph_utils;
/** Example demonstrating how to implement LeNet'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] batches )
*/
class GraphLenetExample : public Example
{
public:
void do_setup(int argc, char **argv) override
{
std::string data_path; /** Path to the trainable data */
unsigned int batches = 4; /** Number of batches */
// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
Target target_hint = set_target_hint2(target);
bool enable_tuning = (target == 2);
bool enable_memory_management = true;
// Parse arguments
if(argc < 2)
{
// Print help
std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches]\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] [batches]\n\n";
std::cout << "No data folder provided: using random values\n\n";
}
else if(argc == 3)
{
//Do something with argv[1]
data_path = argv[2];
std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
}
else
{
//Do something with argv[1] and argv[2]
data_path = argv[2];
batches = std::strtol(argv[3], nullptr, 0);
}
//conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
graph << target_hint
<< InputLayer(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), DataType::F32), get_input_accessor(""))
<< ConvolutionLayer(
5U, 5U, 20U,
get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"),
get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
<< ConvolutionLayer(
5U, 5U, 50U,
get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy"),
get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"),
PadStrideInfo(1, 1, 0, 0))
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
<< FullyConnectedLayer(
500U,
get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"),
get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy"))
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
<< FullyConnectedLayer(
10U,
get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy"),
get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy"))
<< SoftmaxLayer()
<< OutputLayer(get_output_accessor(""));
// Finalize graph
graph.finalize(target_hint, enable_tuning, enable_memory_management);
}
void do_run() override
{
// Run graph
graph.run();
}
private:
Stream graph{ 0, "LeNet" };
};
/** Main program for LeNet
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches )
*/
int main(int argc, char **argv)
{
return arm_compute::utils::run_example<GraphLenetExample>(argc, argv);
}
|
