Converter fix to allow unimplemented convertToOperatorDef (pytorch#13069

)

Summary:
Pull Request resolved: pytorch#13069

simply a new fallback

Reviewed By: ZolotukhinM

Differential Revision: D10591414

fbshipit-source-id: 1ad8f16135a6c68b2df889101f06b736a3e4f7da

caffe2/opt/converter.cc

@@ -84,10 +84,14 @@ OperatorDef Converter::convertToOperatorDef(
     const nom::repr::NeuralNetOperator* nnOp) {
   auto* annotation = nnOp->getAnnotation();
   // Default to using the stored operator.
-  if (isa<Caffe2Annotation>(annotation)) {
+  if (annotation && isa<Caffe2Annotation>(annotation)) {
     return dyn_cast<Caffe2Annotation>(annotation)->getOperatorDef();
   }
-  CAFFE_THROW("TODO: Cannot yet instantiate OperatorDef from nomnigraph");
+  LOG(WARNING)
+      << "Cannot instantiate this OperatorDef from nomnigraph, falling back";
+  caffe2::OperatorDef op;
+  op.set_type(nnOp->getName());
+  return op;
 }
 
 std::vector<int> getKernelShape(std::map<std::string, caffe2::Argument> argMap) {
@@ -156,11 +160,13 @@ class ClipConverter : public Converter {
     float max = std::numeric_limits<float>::max();
 
     if (argMap.count("min")) {
-      min = static_cast<float>(argMap["min"].i());
+      CAFFE_ENFORCE(argMap["min"].has_f(), "Invalid 'min' argument");
+      min = static_cast<float>(argMap["min"].f());
     }
 
     if (argMap.count("max")) {
-      max = static_cast<float>(argMap["max"].i());
+      CAFFE_ENFORCE(argMap["max"].has_f(), "Invalid 'max' argument");
+      max = static_cast<float>(argMap["max"].f());
     }
 
     return util::make_unique<repr::Clip>(min, max);
@@ -363,13 +369,14 @@ repr::NNModule convertToNNModule(
 caffe2::OperatorDef convertToOperatorDef(
     const repr::NNGraph::NodeRef& instrNode) {
   auto *nnOp = repr::nn::get<repr::NeuralNetOperator>(instrNode);
+  auto op_type = nnOp->getName();
   auto *annotation = nnOp->getAnnotation();
   caffe2::OperatorDef op;
 
-  if (ConverterRegistry()->Has(op.type())) {
-    op = ConverterRegistry()->Create(op.type())->convertToOperatorDef(nnOp);
+  if (ConverterRegistry()->Has(op_type)) {
+    op = ConverterRegistry()->Create(op_type)->convertToOperatorDef(nnOp);
   } else if (!annotation) {
-    op.set_type(nnOp->getName());
+    op.set_type(op_type);
   } else {
     if (isa<Caffe2Annotation>(annotation)) {
       auto c2_annotation = dyn_cast<Caffe2Annotation>(annotation);