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Merge pull request #16010 from YashasSamaga:cuda4dnn-fp16-tests

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* enable tests for DNN_TARGET_CUDA_FP16

* disable deconvolution tests

* disable shortcut tests

* fix typos and some minor changes

* dnn(test): skip CUDA FP16 test too (run_pool_max)
This commit is contained in:
Yashas Samaga B L
2019-12-20 19:06:32 +05:30
committed by Alexander Alekhin
parent 4de1efd3c8
commit 1fac1421e5
10 changed files with 461 additions and 107 deletions
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modules/dnn/test/test_model.cpp
+43 -12
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@@ -157,9 +157,13 @@ TEST_P(Test_Model, DetectRegion)
bool swapRB = true;
double confThreshold = 0.24;
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-2 : 8e-5;
double iouDiff = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 1.6e-2 : 1e-5;
double nmsThreshold = (target == DNN_TARGET_MYRIAD) ? 0.397 : 0.4;
double scoreDiff = 8e-5, iouDiff = 1e-5;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16)
{
scoreDiff = 1e-2;
iouDiff = 1.6e-2;
}
testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences,
refBoxes, scoreDiff, iouDiff, confThreshold, nmsThreshold, size,
@@ -188,11 +192,15 @@ TEST_P(Test_Model, DetectionOutput)
Scalar mean = Scalar(102.9801, 115.9465, 122.7717);
Size size{800, 600};
double scoreDiff = (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ?
4e-3 : default_l1;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16) ? 1.8e-1 : 1e-5;
double scoreDiff = default_l1, iouDiff = 1e-5;
float confThreshold = 0.8;
double nmsThreshold = 0.0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CUDA_FP16)
{
if (backend == DNN_BACKEND_OPENCV)
scoreDiff = 4e-3;
iouDiff = 1.8e-1;
}
testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences, refBoxes,
scoreDiff, iouDiff, confThreshold, nmsThreshold, size, mean);
@@ -232,10 +240,22 @@ TEST_P(Test_Model, DetectionMobilenetSSD)
double scale = 1.0 / 127.5;
Size size{300, 300};
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.7e-2 : 1e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || (target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)) ? 6.91e-2 : 1e-5;
double scoreDiff = 1e-5, iouDiff = 1e-5;
if (target == DNN_TARGET_OPENCL_FP16)
{
scoreDiff = 1.7e-2;
iouDiff = 6.91e-2;
}
else if (target == DNN_TARGET_MYRIAD)
{
scoreDiff = 1.7e-2;
if (getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
iouDiff = 6.91e-2;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
scoreDiff = 4e-4;
}
float confThreshold = FLT_MIN;
double nmsThreshold = 0.0;
@@ -263,6 +283,10 @@ TEST_P(Test_Model, Keypoints_pose)
Scalar mean = Scalar(128, 128, 128);
bool swapRB = false;
// Ref. Range: [58.6875, 508.625]
if (target == DNN_TARGET_CUDA_FP16)
norm = 20; // l1 = 1.5, lInf = 20
testKeypointsModel(weights, "", inp, exp, norm, size, mean, scale, swapRB);
}
@@ -283,8 +307,11 @@ TEST_P(Test_Model, Keypoints_face)
Scalar mean = Scalar();
bool swapRB = false;
testKeypointsModel(weights, "", inp, exp, norm, size, mean, scale, swapRB);
// Ref. Range: [-1.1784188, 1.7758257]
if (target == DNN_TARGET_CUDA_FP16)
norm = 0.004; // l1 = 0.0006, lInf = 0.004
testKeypointsModel(weights, "", inp, exp, norm, size, mean, scale, swapRB);
}
TEST_P(Test_Model, Detection_normalized)
@@ -301,10 +328,14 @@ TEST_P(Test_Model, Detection_normalized)
double scale = 1.0 / 127.5;
Size size{300, 300};
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 5e-3 : 1e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : 1e-5;
double scoreDiff = 1e-5, iouDiff = 1e-5;
float confThreshold = FLT_MIN;
double nmsThreshold = 0.0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16)
{
scoreDiff = 5e-3;
iouDiff = 0.09;
}
testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences, refBoxes,
scoreDiff, iouDiff, confThreshold, nmsThreshold, size, mean, scale);
}