f4334277
 Hu Chunming
提交3rdparty
|
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
|
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2019 Intel Corporation
#include "test_precomp.hpp"
#include <stdexcept>
#include <ade/util/iota_range.hpp>
#include "logger.hpp"
#include <opencv2/gapi/plaidml/core.hpp>
#include <opencv2/gapi/plaidml/plaidml.hpp>
namespace opencv_test
{
#ifdef HAVE_PLAIDML
inline cv::gapi::plaidml::config getConfig()
{
auto read_var_from_env = [](const char* env)
{
const char* raw = std::getenv(env);
if (!raw)
{
cv::util::throw_error(std::runtime_error(std::string(env) + " is't set"));
}
return std::string(raw);
};
auto dev_id = read_var_from_env("PLAIDML_DEVICE");
auto trg_id = read_var_from_env("PLAIDML_TARGET");
return cv::gapi::plaidml::config{std::move(dev_id),
std::move(trg_id)};
}
TEST(GAPI_PlaidML_Pipelines, SimpleArithmetic)
{
cv::Size size(1920, 1080);
int type = CV_8UC1;
cv::Mat in_mat1(size, type);
cv::Mat in_mat2(size, type);
// NB: What about overflow ? PlaidML doesn't handle it
cv::randu(in_mat1, cv::Scalar::all(0), cv::Scalar::all(127));
cv::randu(in_mat2, cv::Scalar::all(0), cv::Scalar::all(127));
cv::Mat out_mat(size, type, cv::Scalar::all(0));
cv::Mat ref_mat(size, type, cv::Scalar::all(0));
////////////////////////////// G-API //////////////////////////////////////
cv::GMat in1, in2;
auto out = in1 + in2;
cv::GComputation comp(cv::GIn(in1, in2), cv::GOut(out));
comp.apply(cv::gin(in_mat1, in_mat2), cv::gout(out_mat),
cv::compile_args(getConfig(),
cv::gapi::use_only{cv::gapi::core::plaidml::kernels()}));
////////////////////////////// OpenCV /////////////////////////////////////
cv::add(in_mat1, in_mat2, ref_mat, cv::noArray(), type);
EXPECT_EQ(0, cv::norm(out_mat, ref_mat));
}
// FIXME PlaidML cpu backend does't support bitwise operations
TEST(GAPI_PlaidML_Pipelines, DISABLED_ComplexArithmetic)
{
cv::Size size(1920, 1080);
int type = CV_8UC1;
cv::Mat in_mat1(size, type);
cv::Mat in_mat2(size, type);
cv::randu(in_mat1, cv::Scalar::all(0), cv::Scalar::all(255));
cv::randu(in_mat2, cv::Scalar::all(0), cv::Scalar::all(255));
cv::Mat out_mat(size, type, cv::Scalar::all(0));
cv::Mat ref_mat(size, type, cv::Scalar::all(0));
////////////////////////////// G-API //////////////////////////////////////
cv::GMat in1, in2;
auto out = in1 | (in2 ^ (in1 & (in2 + (in1 - in2))));
cv::GComputation comp(cv::GIn(in1, in2), cv::GOut(out));
comp.apply(cv::gin(in_mat1, in_mat2), cv::gout(out_mat),
cv::compile_args(getConfig(),
cv::gapi::use_only{cv::gapi::core::plaidml::kernels()}));
////////////////////////////// OpenCV /////////////////////////////////////
cv::subtract(in_mat1, in_mat2, ref_mat, cv::noArray(), type);
cv::add(in_mat2, ref_mat, ref_mat, cv::noArray(), type);
cv::bitwise_and(in_mat1, ref_mat, ref_mat);
cv::bitwise_xor(in_mat2, ref_mat, ref_mat);
cv::bitwise_or(in_mat1, ref_mat, ref_mat);
EXPECT_EQ(0, cv::norm(out_mat, ref_mat));
}
TEST(GAPI_PlaidML_Pipelines, TwoInputOperations)
{
cv::Size size(1920, 1080);
int type = CV_8UC1;
constexpr int kNumInputs = 4;
std::vector<cv::Mat> in_mat(kNumInputs, cv::Mat(size, type));
for (int i = 0; i < kNumInputs; ++i)
{
cv::randu(in_mat[i], cv::Scalar::all(0), cv::Scalar::all(60));
}
cv::Mat out_mat(size, type, cv::Scalar::all(0));
cv::Mat ref_mat(size, type, cv::Scalar::all(0));
////////////////////////////// G-API //////////////////////////////////////
cv::GMat in[4];
auto out = (in[3] - in[0]) + (in[2] - in[1]);
cv::GComputation comp(cv::GIn(in[0], in[1], in[2], in[3]), cv::GOut(out));
// FIXME Doesn't work just apply(in_mat, out_mat, ...)
comp.apply(cv::gin(in_mat[0], in_mat[1], in_mat[2], in_mat[3]), cv::gout(out_mat),
cv::compile_args(getConfig(),
cv::gapi::use_only{cv::gapi::core::plaidml::kernels()}));
////////////////////////////// OpenCV /////////////////////////////////////
cv::subtract(in_mat[3], in_mat[0], ref_mat, cv::noArray(), type);
cv::add(ref_mat, in_mat[2], ref_mat, cv::noArray(), type);
cv::subtract(ref_mat, in_mat[1], ref_mat, cv::noArray(), type);
EXPECT_EQ(0, cv::norm(out_mat, ref_mat));
}
TEST(GAPI_PlaidML_Pipelines, TwoOutputOperations)
{
cv::Size size(1920, 1080);
int type = CV_8UC1;
constexpr int kNumInputs = 4;
std::vector<cv::Mat> in_mat(kNumInputs, cv::Mat(size, type));
for (int i = 0; i < kNumInputs; ++i)
{
cv::randu(in_mat[i], cv::Scalar::all(0), cv::Scalar::all(60));
}
std::vector<cv::Mat> out_mat(kNumInputs, cv::Mat(size, type, cv::Scalar::all(0)));
std::vector<cv::Mat> ref_mat(kNumInputs, cv::Mat(size, type, cv::Scalar::all(0)));
////////////////////////////// G-API //////////////////////////////////////
cv::GMat in[4], out[2];
out[0] = in[0] + in[3];
out[1] = in[1] + in[2];
cv::GComputation comp(cv::GIn(in[0], in[1], in[2], in[3]), cv::GOut(out[0], out[1]));
// FIXME Doesn't work just apply(in_mat, out_mat, ...)
comp.apply(cv::gin(in_mat[0], in_mat[1], in_mat[2], in_mat[3]),
cv::gout(out_mat[0], out_mat[1]),
cv::compile_args(getConfig(),
cv::gapi::use_only{cv::gapi::core::plaidml::kernels()}));
////////////////////////////// OpenCV /////////////////////////////////////
cv::add(in_mat[0], in_mat[3], ref_mat[0], cv::noArray(), type);
cv::add(in_mat[1], in_mat[2], ref_mat[1], cv::noArray(), type);
EXPECT_EQ(0, cv::norm(out_mat[0], ref_mat[0]));
EXPECT_EQ(0, cv::norm(out_mat[1], ref_mat[1]));
}
#else // HAVE_PLAIDML
TEST(GAPI_PlaidML_Pipelines, ThrowIfPlaidMLNotFound)
{
ASSERT_ANY_THROW(cv::gapi::core::plaidml::kernels());
}
#endif // HAVE_PLAIDML
} // namespace opencv_test
|
