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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_layers.cpp
+39 -4
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@@ -141,6 +141,8 @@ TEST_P(Test_Caffe_layers, Convolution)
TEST_P(Test_Caffe_layers, DeConvolution)
{
if(target == DNN_TARGET_CUDA_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
testLayerUsingCaffeModels("layer_deconvolution", true, false);
}
@@ -372,7 +374,13 @@ TEST_P(Test_Caffe_layers, Conv_Elu)
net.setPreferableTarget(target);
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
double l1 = default_l1, lInf = default_lInf;
if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.0002;
lInf = 0.0005;
}
normAssert(ref, out, "", l1, lInf);
}
class Layer_LSTM_Test : public ::testing::Test
@@ -843,6 +851,11 @@ TEST_P(Test_Caffe_layers, PriorBox_repeated)
double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-3 : 1e-5;
double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-3 : 1e-4;
if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 7e-5;
lInf = 0.0005;
}
normAssert(out, ref, "", l1, lInf);
}
@@ -876,7 +889,9 @@ TEST_P(Test_Caffe_layers, PriorBox_squares)
0.25, 0.0, 1.0, 1.0,
0.1f, 0.1f, 0.2f, 0.2f,
0.1f, 0.1f, 0.2f, 0.2f);
double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 2e-5 : 1e-5;
double l1 = 1e-5;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16)
l1 = 2e-5;
normAssert(out.reshape(1, 4), ref, "", l1);
}
@@ -1225,6 +1240,11 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
// Output values are in range [0, 637.5].
double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.06 : 1e-6;
double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.3 : 1e-5;
if (targetId == DNN_TARGET_CUDA_FP16)
{
l1 = 0.06;
lInf = 0.3;
}
normAssert(out, ref, "", l1, lInf);
}
@@ -1537,8 +1557,17 @@ TEST_P(Layer_Test_ShuffleChannel, Accuracy)
net.setPreferableTarget(targetId);
Mat out = net.forward();
double l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 5e-2 : 1e-5;
double lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 7e-2 : 1e-4;
double l1 = 1e-5, lInf = 1e-4;
if (targetId == DNN_TARGET_OPENCL_FP16)
{
l1 = 5e-2;
lInf = 7e-2;
}
else if (targetId == DNN_TARGET_CUDA_FP16)
{
l1 = 0.06;
lInf = 0.07;
}
for (int n = 0; n < inpShapeVec[0]; ++n)
{
for (int c = 0; c < inpShapeVec[1]; ++c)
@@ -1593,6 +1622,9 @@ TEST_P(Layer_Test_Eltwise_unequal, accuracy_input_0_truncate)
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
if (backendId == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
Net net;
LayerParams lp;
lp.type = "Eltwise";
@@ -1656,6 +1688,9 @@ TEST_P(Layer_Test_Eltwise_unequal, accuracy_input_0)
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
if (backendId == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
Net net;
LayerParams lp;
lp.type = "Eltwise";