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11//                For Open Source Computer Vision Library
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42
43#include "perf_precomp.hpp"
44
45using namespace std;
46using namespace testing;
47using namespace perf;
48
49//////////////////////////////////////////////////////////////////////
50// GEMM
51
52#ifdef HAVE_CUBLAS
53
54CV_FLAGS(GemmFlags, 0, cv::GEMM_1_T, cv::GEMM_2_T, cv::GEMM_3_T)
55#define ALL_GEMM_FLAGS Values(GemmFlags(0), GemmFlags(cv::GEMM_1_T), GemmFlags(cv::GEMM_2_T), GemmFlags(cv::GEMM_3_T), \
56                              GemmFlags(cv::GEMM_1_T | cv::GEMM_2_T), GemmFlags(cv::GEMM_1_T | cv::GEMM_3_T), GemmFlags(cv::GEMM_1_T | cv::GEMM_2_T | cv::GEMM_3_T))
57
58DEF_PARAM_TEST(Sz_Type_Flags, cv::Size, MatType, GemmFlags);
59
60PERF_TEST_P(Sz_Type_Flags, GEMM,
61            Combine(Values(cv::Size(512, 512), cv::Size(1024, 1024)),
62                    Values(CV_32FC1, CV_32FC2, CV_64FC1),
63                    ALL_GEMM_FLAGS))
64{
65    const cv::Size size = GET_PARAM(0);
66    const int type = GET_PARAM(1);
67    const int flags = GET_PARAM(2);
68
69    cv::Mat src1(size, type);
70    declare.in(src1, WARMUP_RNG);
71
72    cv::Mat src2(size, type);
73    declare.in(src2, WARMUP_RNG);
74
75    cv::Mat src3(size, type);
76    declare.in(src3, WARMUP_RNG);
77
78    if (PERF_RUN_CUDA())
79    {
80        declare.time(5.0);
81
82        const cv::cuda::GpuMat d_src1(src1);
83        const cv::cuda::GpuMat d_src2(src2);
84        const cv::cuda::GpuMat d_src3(src3);
85        cv::cuda::GpuMat dst;
86
87        TEST_CYCLE() cv::cuda::gemm(d_src1, d_src2, 1.0, d_src3, 1.0, dst, flags);
88
89        CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
90    }
91    else
92    {
93        declare.time(50.0);
94
95        cv::Mat dst;
96
97        TEST_CYCLE() cv::gemm(src1, src2, 1.0, src3, 1.0, dst, flags);
98
99        CPU_SANITY_CHECK(dst);
100    }
101}
102
103#endif
104
105//////////////////////////////////////////////////////////////////////
106// MulSpectrums
107
108CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT)
109
110DEF_PARAM_TEST(Sz_Flags, cv::Size, DftFlags);
111
112PERF_TEST_P(Sz_Flags, MulSpectrums,
113            Combine(CUDA_TYPICAL_MAT_SIZES,
114                    Values(0, DftFlags(cv::DFT_ROWS))))
115{
116    const cv::Size size = GET_PARAM(0);
117    const int flag = GET_PARAM(1);
118
119    cv::Mat a(size, CV_32FC2);
120    cv::Mat b(size, CV_32FC2);
121    declare.in(a, b, WARMUP_RNG);
122
123    if (PERF_RUN_CUDA())
124    {
125        const cv::cuda::GpuMat d_a(a);
126        const cv::cuda::GpuMat d_b(b);
127        cv::cuda::GpuMat dst;
128
129        TEST_CYCLE() cv::cuda::mulSpectrums(d_a, d_b, dst, flag);
130
131        CUDA_SANITY_CHECK(dst);
132    }
133    else
134    {
135        cv::Mat dst;
136
137        TEST_CYCLE() cv::mulSpectrums(a, b, dst, flag);
138
139        CPU_SANITY_CHECK(dst);
140    }
141}
142
143//////////////////////////////////////////////////////////////////////
144// MulAndScaleSpectrums
145
146PERF_TEST_P(Sz, MulAndScaleSpectrums,
147            CUDA_TYPICAL_MAT_SIZES)
148{
149    const cv::Size size = GetParam();
150
151    const float scale = 1.f / size.area();
152
153    cv::Mat src1(size, CV_32FC2);
154    cv::Mat src2(size, CV_32FC2);
155    declare.in(src1,src2, WARMUP_RNG);
156
157    if (PERF_RUN_CUDA())
158    {
159        const cv::cuda::GpuMat d_src1(src1);
160        const cv::cuda::GpuMat d_src2(src2);
161        cv::cuda::GpuMat dst;
162
163        TEST_CYCLE() cv::cuda::mulAndScaleSpectrums(d_src1, d_src2, dst, cv::DFT_ROWS, scale, false);
164
165        CUDA_SANITY_CHECK(dst);
166    }
167    else
168    {
169        FAIL_NO_CPU();
170    }
171}
172
173//////////////////////////////////////////////////////////////////////
174// Dft
175
176PERF_TEST_P(Sz_Flags, Dft,
177            Combine(CUDA_TYPICAL_MAT_SIZES,
178                    Values(0, DftFlags(cv::DFT_ROWS), DftFlags(cv::DFT_INVERSE))))
179{
180    declare.time(10.0);
181
182    const cv::Size size = GET_PARAM(0);
183    const int flag = GET_PARAM(1);
184
185    cv::Mat src(size, CV_32FC2);
186    declare.in(src, WARMUP_RNG);
187
188    if (PERF_RUN_CUDA())
189    {
190        const cv::cuda::GpuMat d_src(src);
191        cv::cuda::GpuMat dst;
192
193        TEST_CYCLE() cv::cuda::dft(d_src, dst, size, flag);
194
195        CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
196    }
197    else
198    {
199        cv::Mat dst;
200
201        TEST_CYCLE() cv::dft(src, dst, flag);
202
203        CPU_SANITY_CHECK(dst);
204    }
205}
206
207//////////////////////////////////////////////////////////////////////
208// Convolve
209
210DEF_PARAM_TEST(Sz_KernelSz_Ccorr, cv::Size, int, bool);
211
212PERF_TEST_P(Sz_KernelSz_Ccorr, Convolve,
213            Combine(CUDA_TYPICAL_MAT_SIZES,
214                    Values(17, 27, 32, 64),
215                    Bool()))
216{
217    declare.time(10.0);
218
219    const cv::Size size = GET_PARAM(0);
220    const int templ_size = GET_PARAM(1);
221    const bool ccorr = GET_PARAM(2);
222
223    const cv::Mat image(size, CV_32FC1);
224    const cv::Mat templ(templ_size, templ_size, CV_32FC1);
225    declare.in(image, templ, WARMUP_RNG);
226
227    if (PERF_RUN_CUDA())
228    {
229        cv::cuda::GpuMat d_image = cv::cuda::createContinuous(size, CV_32FC1);
230        d_image.upload(image);
231
232        cv::cuda::GpuMat d_templ = cv::cuda::createContinuous(templ_size, templ_size, CV_32FC1);
233        d_templ.upload(templ);
234
235        cv::Ptr<cv::cuda::Convolution> convolution = cv::cuda::createConvolution();
236
237        cv::cuda::GpuMat dst;
238
239        TEST_CYCLE() convolution->convolve(d_image, d_templ, dst, ccorr);
240
241        CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
242    }
243    else
244    {
245        if (ccorr)
246            FAIL_NO_CPU();
247
248        cv::Mat dst;
249
250        TEST_CYCLE() cv::filter2D(image, dst, image.depth(), templ);
251
252        CPU_SANITY_CHECK(dst);
253    }
254}
255