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dnn(test): update ONNX conformance filters (4.x)

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This commit is contained in:
Alexander Alekhin
2021-12-15 20:46:28 +00:00
parent 299f9837b7
commit 6d677bbd63
9 changed files with 381 additions and 90 deletions
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modules/dnn/src/layers/convolution_layer.cpp
+10
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@@ -647,6 +647,7 @@ public:
virtual Ptr<BackendNode> initVkCom(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_VULKAN
CV_Assert(!blobs.empty());
int out_channel = blobs[0].size[0];
bool has_bias = hasBias() || fusedBias;
int filter_size[2] = {kernel.height, kernel.width};
@@ -712,6 +713,7 @@ public:
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_HALIDE
CV_Assert(!blobs.empty());
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
const int inpCn = inputBuffer.channels();
@@ -760,6 +762,7 @@ public:
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
std::vector<size_t> dims = input->getDims();
CV_Assert(dims.size() == 4 || dims.size() == 5);
@@ -824,6 +827,7 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
CV_Assert_N(inputs.size() >= 1, nodes.size() >= 1);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> dims = ieInpNode->get_shape();
@@ -917,6 +921,7 @@ public:
#ifdef HAVE_WEBNN
virtual Ptr<BackendNode> initWebnn(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
CV_Assert_N(inputs.size() >= 1, nodes.size() >= 1);
Ptr<WebnnBackendNode> node = nodes[0].dynamicCast<WebnnBackendNode>();
auto& webnnInpOperand = node->operand;
@@ -2156,6 +2161,7 @@ public:
auto output_wrapper = outputs[0].dynamicCast<CUDABackendWrapper>();
auto output_shape = output_wrapper->getShape();
CV_Assert(!blobs.empty());
const auto output_feature_maps = blobs[0].size[0];
const auto input_feature_maps = input_shape[1];
const auto input_feature_maps_per_group = blobs[0].size[1];
@@ -2917,6 +2923,7 @@ public:
const std::vector<Ptr<BackendWrapper>>& outputs
) override
{
CV_Assert(!blobs.empty());
auto context = reinterpret_cast<csl::CSLContext*>(context_);
CV_Assert(inputs.size() == 1);
@@ -2974,6 +2981,7 @@ public:
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_HALIDE
CV_Assert(!blobs.empty());
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
int inW, inH, inC, inN;
@@ -3027,6 +3035,7 @@ public:
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
InferenceEngine::Layout layout = blobs[0].dims == 5? InferenceEngine::Layout::NCDHW :
InferenceEngine::Layout::OIHW;
@@ -3086,6 +3095,7 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
const int outGroupCn = blobs[0].size[1];
const int group = numOutput / outGroupCn;
CV_Assert(group == 1);