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added support check

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This commit is contained in:
Abhishek Gola
2026-07-07 18:59:39 +05:30
parent 0843a0bb17
commit e7dc3a9a9b
1 changed files with 41 additions and 9 deletions
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modules/dnn/src/net_impl2.cpp
+41 -9
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@@ -585,6 +585,32 @@ void Net::Impl::finalizeGraph(const Ptr<Graph>& graph, bool useCUDA)
g->exec_.assign(nops, Ptr<Layer>());
g->execBackend_.assign(nops, DNN_BACKEND_OPENCV);
#ifdef HAVE_CUDA
// Whole-graph CUDA gate: run on CUDA only if *every* op has a CUDA executor; if any op is
// unsupported (or the graph has control-flow subgraphs) run the entire graph on CPU. This
// avoids partial CPU<->CUDA execution and its host/device coherence hazards for now.
std::vector<Ptr<Layer> > cudaExecs;
bool graphOnCuda = false;
if (useCUDA && cudaInfo) {
graphOnCuda = true;
cudaExecs.assign(nops, Ptr<Layer>());
for (i = 0; i < nops; i++) {
const Ptr<LayerInfo>& op = prog[i];
if (!op)
continue;
if (op->subgraphs()) { graphOnCuda = false; break; } // control-flow bodies stay on CPU
Ptr<Layer> e = LayerFactory::createExec(op->type, DNN_BACKEND_CUDA, op, &cudaInfo->context);
if (!e) { graphOnCuda = false; break; }
cudaExecs[i] = e;
}
if (!graphOnCuda) {
cudaExecs.clear();
CV_LOG_INFO(NULL, cv::format("DNN/NewEngine: graph '%s' has layer(s) without CUDA support; "
"running the whole graph on CPU", graph->name().c_str()));
}
}
#endif
for (i = 0; i < nops; i++) {
const Ptr<LayerInfo>& op = prog[i];
if (!op)
@@ -600,13 +626,10 @@ void Net::Impl::finalizeGraph(const Ptr<Graph>& graph, bool useCUDA)
Ptr<Layer> exec;
int backend = DNN_BACKEND_OPENCV;
#ifdef HAVE_CUDA
// Try the optimized backend first; its create() returns null if the op is unsupported.
if (useCUDA && cudaInfo && !subs) {
exec = LayerFactory::createExec(op->type, DNN_BACKEND_CUDA, op, &cudaInfo->context);
if (exec) {
exec->preferableTarget = preferableTarget;
backend = DNN_BACKEND_CUDA;
}
if (graphOnCuda && cudaExecs[i]) {
exec = cudaExecs[i];
exec->preferableTarget = preferableTarget;
backend = DNN_BACKEND_CUDA;
}
#endif
if (!exec) {
@@ -1390,15 +1413,24 @@ void Net::Impl::forwardGraph(Ptr<Graph>& graph, InputArrayOfArrays inputs_,
for (i = 0; i < n_gr_inputs; i++) {
Mat m = inputs_.getMat((int)i);
setGraphInput(graph, i, m);
}
}
#ifdef HAVE_CUDA
// Graph inputs are host-authoritative and may be updated between forward() calls
// (setInput writes them outside this function, so the loop above is skipped when
// forward() is called with no explicit inputs). Mark their device copies stale each
// forward so the current input is re-uploaded; otherwise a second forward with a
// changed input would read the previous forward's stale device data.
if (cudaInfo) {
for (i = 0; i < n_gr_inputs; i++) {
Arg ginp = gr_inputs[i];
if (ginp.idx < (int)argWrappers.size()) {
if (ginp.idx >= 0 && ginp.idx < (int)argWrappers.size()) {
Ptr<CUDABackendWrapper> cw = argWrappers[ginp.idx].dynamicCast<CUDABackendWrapper>();
if (cw) cw->setHostDirty();
}
#endif
}
}
#endif
for (size_t opidx = 0; opidx < nops; opidx++) {
const Ptr<LayerInfo>& op = prog.at(opidx);