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Merge pull request #12875 from dkurt:dnn_enet_accuracy

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Alexander Alekhin 2018-10-19 15:33:48 +00:00
commit a692d87cfb
1 changed files with 44 additions and 2 deletions
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46
modules/dnn/test/test_torch_importer.cpp
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@ -287,6 +287,46 @@ TEST_P(Test_Torch_nets, OpenFace_accuracy)
normAssert(out, outRef, "", default_l1, default_lInf);
}
static Mat getSegmMask(const Mat& scores)
{
const int rows = scores.size[2];
const int cols = scores.size[3];
const int numClasses = scores.size[1];
Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
Mat maxVal(rows, cols, CV_32FC1, Scalar(0));
for (int ch = 0; ch < numClasses; ch++)
{
for (int row = 0; row < rows; row++)
{
const float *ptrScore = scores.ptr<float>(0, ch, row);
uint8_t *ptrMaxCl = maxCl.ptr<uint8_t>(row);
float *ptrMaxVal = maxVal.ptr<float>(row);
for (int col = 0; col < cols; col++)
{
if (ptrScore[col] > ptrMaxVal[col])
{
ptrMaxVal[col] = ptrScore[col];
ptrMaxCl[col] = (uchar)ch;
}
}
}
}
return maxCl;
}
// Computer per-class intersection over union metric.
static void normAssertSegmentation(const Mat& ref, const Mat& test)
{
CV_Assert_N(ref.dims == 4, test.dims == 4);
const int numClasses = ref.size[1];
CV_Assert(numClasses == test.size[1]);
Mat refMask = getSegmMask(ref);
Mat testMask = getSegmMask(test);
EXPECT_EQ(countNonZero(refMask != testMask), 0);
}
TEST_P(Test_Torch_nets, ENet_accuracy)
{
checkBackend();
@ -313,14 +353,16 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
// Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
// thresholds for ENet must be changed. Accuracy of results was checked on
// Cityscapes dataset and difference in mIOU with Torch is 10E-4%
normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.5);
normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
normAssertSegmentation(ref, out);
const int N = 3;
for (int i = 0; i < N; i++)
{
net.setInput(inputBlob, "");
Mat out = net.forward();
normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.5);
normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
normAssertSegmentation(ref, out);
}
}