mirror of
https://github.com/opencv/opencv.git
synced 2026-07-26 05:43:05 +04:00
Teddy-Yangjiale
78 lines
2.4 KiB
C++
78 lines
2.4 KiB
C++
// 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.
|
|
|
|
#include "test_precomp.hpp"
|
|
|
|
namespace opencv_test { namespace {
|
|
|
|
static Mat referenceMatMul(const Mat& A, const Mat& B, bool trans_a, float alpha)
|
|
{
|
|
const int M = trans_a ? A.cols : A.rows;
|
|
const int K = trans_a ? A.rows : A.cols;
|
|
const int N = B.cols;
|
|
Mat expected(M, N, CV_32F);
|
|
|
|
for (int m = 0; m < M; m++)
|
|
{
|
|
float* dst = expected.ptr<float>(m);
|
|
for (int n = 0; n < N; n++)
|
|
{
|
|
float acc = 0.f;
|
|
for (int k = 0; k < K; k++)
|
|
{
|
|
const float a = trans_a ? A.ptr<float>(k)[m] : A.ptr<float>(m)[k];
|
|
acc += a * B.ptr<float>(k)[n];
|
|
}
|
|
dst[n] = alpha * acc;
|
|
}
|
|
}
|
|
return expected;
|
|
}
|
|
|
|
// Exercises the fastGemmThin path (constant-B MatMul). M covers every register
|
|
// block width and the multi-block remainder, N covers full strips and partial
|
|
// column tails for any VLEN <= 512.
|
|
typedef testing::TestWithParam<tuple<int, int, int, int, float>> DNN_FastGemmThin;
|
|
|
|
TEST_P(DNN_FastGemmThin, MatMulAccuracy)
|
|
{
|
|
int M = get<0>(GetParam()), N = get<1>(GetParam()), K = get<2>(GetParam());
|
|
int trans_a = get<3>(GetParam());
|
|
float alpha = get<4>(GetParam());
|
|
|
|
RNG rng(0x5EED);
|
|
Mat B(K, N, CV_32F); rng.fill(B, RNG::UNIFORM, -1.f, 1.f);
|
|
Mat A(trans_a ? K : M, trans_a ? M : K, CV_32F);
|
|
rng.fill(A, RNG::UNIFORM, -1.f, 1.f);
|
|
|
|
LayerParams lp;
|
|
lp.type = "MatMul";
|
|
lp.name = "thin_matmul";
|
|
lp.set("transA", trans_a != 0);
|
|
lp.set("transB", false);
|
|
lp.set("alpha", alpha);
|
|
lp.blobs.push_back(B);
|
|
|
|
Net net;
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
net.setInputsNames(std::vector<String>{ "A" });
|
|
net.setInput(A, "A");
|
|
Mat actual = net.forward();
|
|
Mat expected = referenceMatMul(A, B, trans_a != 0, alpha);
|
|
|
|
EXPECT_LE(cv::norm(expected, actual, NORM_INF), 2e-5f * std::max(K, 1))
|
|
<< "M=" << M << ", N=" << N << ", K=" << K
|
|
<< ", trans_a=" << trans_a << ", alpha=" << alpha;
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNN_FastGemmThin, Combine(
|
|
Values(1, 2, 3, 4, 5),
|
|
Values(16, 19, 64, 67, 80, 83),
|
|
Values(1, 3, 4, 7, 16, 64),
|
|
Values(0, 1),
|
|
Values(1.f, -0.5f)
|
|
));
|
|
|
|
}} // namespace
|