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DNN: add the Winograd fp16 support (#23654)

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* add Winograd FP16 implementation

* fixed dispatching of FP16 code paths in dnn; use dynamic dispatcher only when NEON_FP16 is enabled in the build and the feature is present in the host CPU at runtime

* fixed some warnings

* hopefully fixed winograd on x64 (and maybe other platforms)

---------

Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>
This commit is contained in:
zihaomu
2023-11-20 18:45:37 +08:00
committed by GitHub
parent a478757483
commit b913e73d04
22 changed files with 1262 additions and 556 deletions
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modules/dnn/test/test_caffe_importer.cpp
+20 -1
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@@ -62,6 +62,10 @@ public:
findDataFile("dnn/" + model, false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
if (target == DNN_TARGET_CPU_FP16)
net.enableWinograd(false);
Mat img = imread(findDataFile("dnn/dog416.png"));
resize(img, img, Size(800, 600));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(102.9801, 115.9465, 122.7717), false, false);
@@ -219,6 +223,9 @@ TEST_P(Reproducibility_AlexNet, Accuracy)
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
if (targetId == DNN_TARGET_CPU_FP16)
net.enableWinograd(false);
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
@@ -383,6 +390,9 @@ TEST_P(Reproducibility_ResNet50, Accuracy)
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
if (targetId == DNN_TARGET_CPU_FP16)
net.enableWinograd(false);
float l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_CPU_FP16) ? 3e-5 : 1e-5;
float lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_CPU_FP16) ? 6e-3 : 1e-4;
@@ -503,6 +513,10 @@ TEST_P(Test_Caffe_nets, Colorization)
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
// This model has bad accuracy when the FP16 and Winograd are enable at same time.
if (target == DNN_TARGET_CPU_FP16)
net.enableWinograd(false);
net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel);
net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606));
@@ -568,10 +582,15 @@ TEST_P(Test_Caffe_nets, DenseNet_121)
{
l1 = 0.11; lInf = 0.5;
}
else if (target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_CPU_FP16)
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.04; lInf = 0.2;
}
else if (target == DNN_TARGET_CPU_FP16)
{
l1 = 0.06; lInf = 0.3;
}
normAssert(outs[0], ref, "", l1, lInf);
if (target != DNN_TARGET_MYRIAD || getInferenceEngineVPUType() != CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
expectNoFallbacksFromIE(model.getNetwork_());