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 © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "../InferenceTest.hpp"
#include "../ImagePreprocessor.hpp"
#include "armnnTfLiteParser/ITfLiteParser.hpp"
#include "boost/program_options.hpp"
#include <fstream>
using namespace armnnTfLiteParser;
std::vector<ImageSet> ParseDataset(const std::string& filename)
{
std::ifstream read(filename);
std::vector<ImageSet> imageSet;
if (read.is_open())
{
// Get the images and the correct corresponding label from the given file
for (std::string line; std::getline(read, line);)
{
stringstream ss(line);
std::string image_name;
std::string label;
getline(ss, image_name, ' ');
getline(ss, label, ' ');
imageSet.push_back(ImageSet(image_name, std::stoi(label)));
}
}
else
{
// Use the default images
imageSet.push_back(ImageSet("Dog.jpg", 209));
// top five predictions in tensorflow:
// -----------------------------------
// 209:Labrador retriever 0.949995
// 160:Rhodesian ridgeback 0.0270182
// 208:golden retriever 0.0192866
// 853:tennis ball 0.000470382
// 239:Greater Swiss Mountain dog 0.000464451
imageSet.push_back(ImageSet("Cat.jpg", 283));
// top five predictions in tensorflow:
// -----------------------------------
// 283:tiger cat 0.579016
// 286:Egyptian cat 0.319676
// 282:tabby, tabby cat 0.0873346
// 288:lynx, catamount 0.011163
// 289:leopard, Panthera pardus 0.000856755
imageSet.push_back(ImageSet("shark.jpg", 3));
// top five predictions in tensorflow:
// -----------------------------------
// 3:great white shark, white shark, ... 0.996926
// 4:tiger shark, Galeocerdo cuvieri 0.00270528
// 149:killer whale, killer, orca, ... 0.000121848
// 395:sturgeon 7.78977e-05
// 5:hammerhead, hammerhead shark 6.44127e-055
};
return imageSet;
}
std::string GetLabelsFilenameFromOptions(int argc, char* argv[])
{
namespace po = boost::program_options;
po::options_description desc("Validation Options");
std::string fn("");
desc.add_options()
("labels", po::value<std::string>(&fn), "Filename of a text file where in each line contains an image "
"filename and the correct label the network should predict when fed that image");
po::variables_map vm;
po::parsed_options parsed = po::command_line_parser(argc, argv).options(desc).allow_unregistered().run();
po::store(parsed, vm);
if (vm.count("labels"))
{
fn = vm["labels"].as<std::string>();
}
return fn;
}
int main(int argc, char* argv[])
{
int retVal = EXIT_FAILURE;
try
{
// Coverity fix: The following code may throw an exception of type std::length_error.
const std::string labels_file = GetLabelsFilenameFromOptions(argc,argv);
std::vector<ImageSet> imageSet = ParseDataset(labels_file);
std::vector<unsigned int> indices(imageSet.size());
std::generate(indices.begin(), indices.end(), [n = 0] () mutable { return n++; });
armnn::TensorShape inputTensorShape({ 1, 224, 224, 3 });
using DataType = uint8_t;
using DatabaseType = ImagePreprocessor<DataType>;
using ParserType = armnnTfLiteParser::ITfLiteParser;
using ModelType = InferenceModel<ParserType, DataType>;
// Coverity fix: ClassifierInferenceTestMain() may throw uncaught exceptions.
retVal = armnn::test::ClassifierInferenceTestMain<DatabaseType,
ParserType>(
argc, argv,
"mobilenet_v1_1.0_224_quant.tflite", // model name
true, // model is binary
"input", // input tensor name
"MobilenetV1/Predictions/Reshape_1", // output tensor name
indices, // vector of indices to select which images to validate
[&imageSet](const char* dataDir, const ModelType & model) {
// we need to get the input quantization parameters from
// the parsed model
return DatabaseType(
dataDir,
224,
224,
imageSet,
1);
},
&inputTensorShape);
}
catch (const std::exception& e)
{
// Coverity fix: BOOST_LOG_TRIVIAL (typically used to report errors) may throw an
// exception of type std::length_error.
// Using stderr instead in this context as there is no point in nesting try-catch blocks here.
std::cerr << "WARNING: " << *argv << ": An error has occurred when running "
"the classifier inference tests: " << e.what() << std::endl;
}
return retVal;
}
|
