chentong319
GitHub
parent
8af5fdeb62
commit
32ceb6968a
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@ -25,7 +25,7 @@ namespace bstd = mpark;
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#include "FrontendDialectTransformer.hpp"
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namespace onnx_mlir {
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namespace {
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namespace detail {
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/*!
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* The list of tensors initialized by the ONNX model.
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@ -37,6 +37,7 @@ public:
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FrontendGenImpl(mlir::MLIRContext &context)
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: context_(context), builder_(&context) {
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module_ = mlir::ModuleOp::create(mlir::UnknownLoc::get(&context));
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InitHandlerMap();
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}
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mlir::ModuleOp ImportONNXModel(onnx::ModelProto model) {
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@ -52,6 +53,11 @@ private:
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// mapping between string name and symbol
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OnnxMlirSymbolMapping frontend_symbols_;
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typedef void (onnx_mlir::detail::FrontendGenImpl::*ImportHandlerType)(
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const onnx::NodeProto &);
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std::map<std::string, ImportHandlerType> import_handler_map_;
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mlir::Location UnknownLoc() { return mlir::UnknownLoc::get(&context_); }
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/*!
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@ -329,7 +335,7 @@ private:
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/*!
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* Special handle for MaxPool operations.
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*/
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void ImportNodeMaxPool(onnx::NodeProto node) {
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void ImportNodeMaxPool(const onnx::NodeProto &node) {
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int nOuts = node.output().size();
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if (nOuts == 1) {
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buildOperation<mlir::ONNXMaxPoolSingleOutOp>(node);
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@ -341,7 +347,7 @@ private:
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/*!
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* Special handle for BatchNormalization operations.
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*/
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void ImportNodeBatchNormalization(onnx::NodeProto node) {
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void ImportNodeBatchNormalization(const onnx::NodeProto &node) {
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int nOuts = node.output().size();
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if (nOuts == 1) {
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// Test mode with one output.
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@ -355,7 +361,7 @@ private:
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/*!
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* Special handle for Pad operations.
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*/
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void ImportNodePad(onnx::NodeProto node) {
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void ImportNodePad(const onnx::NodeProto &node) {
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int nOps = node.input().size();
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if (nOps == 2) {
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@ -400,12 +406,16 @@ private:
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// the generic operator is used
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// one known reeason is the optional input
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(this->*(import_handler_map_[opName.str()]))(node);
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}
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void InitHandlerMap() {
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#include "src/Builder/OpBuildTable.inc"
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}
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/*!
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* Import output tensor, by doing the following:
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* - Add the type of this output tensor to a list of tensor
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* - Add the t/yp this output tensor to a list of tensor
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* types representing return types of this graph function.
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* - Add this output tensor to the list of mlir::Value
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* to be returned by the function representing computation graph.
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@ -499,7 +509,7 @@ private:
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mainFunc.setType(funcType);
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}
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}; // FrontendGenImpl class
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} // namespace
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} // namespace detail
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} // namespace onnx_mlir
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namespace onnx_mlir {
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@ -512,7 +522,7 @@ void ImportFrontendModelFile(std::string model_fname,
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auto parse_success = model.ParseFromIstream(&input);
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assert(parse_success && "Onnx Model Parsing Failed.");
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FrontendGenImpl myONNXGen(context);
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detail::FrontendGenImpl myONNXGen(context);
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module = myONNXGen.ImportONNXModel(model);
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}
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} // namespace onnx_mlir
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@ -4,351 +4,351 @@
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// Details can be found in docs/ImportONNXDefs.md .
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//********************************************************
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if (opName == "Abs")
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buildOperation<mlir::ONNXAbsOp>(node);
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if (opName == "Acos")
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buildOperation<mlir::ONNXAcosOp>(node);
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if (opName == "Acosh")
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buildOperation<mlir::ONNXAcoshOp>(node);
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if (opName == "Add")
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buildOperation<mlir::ONNXAddOp>(node);
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if (opName == "And")
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buildOperation<mlir::ONNXAndOp>(node);
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if (opName == "ArgMax")
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buildOperation<mlir::ONNXArgMaxOp>(node);
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if (opName == "ArgMin")
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buildOperation<mlir::ONNXArgMinOp>(node);
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if (opName == "Asin")
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buildOperation<mlir::ONNXAsinOp>(node);
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if (opName == "Asinh")
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buildOperation<mlir::ONNXAsinhOp>(node);
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if (opName == "Atan")
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buildOperation<mlir::ONNXAtanOp>(node);
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if (opName == "Atanh")
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buildOperation<mlir::ONNXAtanhOp>(node);
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if (opName == "AveragePool")
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buildOperation<mlir::ONNXAveragePoolOp>(node);
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if (opName == "BatchNormalization")
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ImportNodeBatchNormalization(node);
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if (opName == "BitShift")
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buildOperation<mlir::ONNXBitShiftOp>(node);
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if (opName == "Cast")
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buildOperation<mlir::ONNXCastOp>(node);
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if (opName == "Ceil")
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buildOperation<mlir::ONNXCeilOp>(node);
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if (opName == "Clip")
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buildOperation<mlir::ONNXClipOp>(node);
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if (opName == "Compress")
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buildOperation<mlir::ONNXCompressOp>(node);
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if (opName == "Concat")
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buildOperation<mlir::ONNXConcatOp>(node);
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if (opName == "ConcatFromSequence")
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buildOperation<mlir::ONNXConcatFromSequenceOp>(node);
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if (opName == "Constant")
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buildOperation<mlir::ONNXConstantOp>(node);
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if (opName == "ConstantOfShape")
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buildOperation<mlir::ONNXConstantOfShapeOp>(node);
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if (opName == "Conv")
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buildOperation<mlir::ONNXConvOp>(node);
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if (opName == "ConvInteger")
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buildOperation<mlir::ONNXConvIntegerOp>(node);
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if (opName == "ConvTranspose")
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buildOperation<mlir::ONNXConvTransposeOp>(node);
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if (opName == "Cos")
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buildOperation<mlir::ONNXCosOp>(node);
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if (opName == "Cosh")
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buildOperation<mlir::ONNXCoshOp>(node);
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if (opName == "CumSum")
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buildOperation<mlir::ONNXCumSumOp>(node);
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if (opName == "DepthToSpace")
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buildOperation<mlir::ONNXDepthToSpaceOp>(node);
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if (opName == "DequantizeLinear")
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buildOperation<mlir::ONNXDequantizeLinearOp>(node);
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if (opName == "Det")
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buildOperation<mlir::ONNXDetOp>(node);
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if (opName == "Div")
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buildOperation<mlir::ONNXDivOp>(node);
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if (opName == "Dropout")
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buildOperation<mlir::ONNXDropoutOp>(node);
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if (opName == "DynamicQuantizeLinear")
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buildOperation<mlir::ONNXDynamicQuantizeLinearOp>(node);
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if (opName == "Elu")
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buildOperation<mlir::ONNXEluOp>(node);
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if (opName == "Equal")
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buildOperation<mlir::ONNXEqualOp>(node);
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if (opName == "Erf")
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buildOperation<mlir::ONNXErfOp>(node);
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if (opName == "Exp")
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buildOperation<mlir::ONNXExpOp>(node);
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if (opName == "Expand")
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buildOperation<mlir::ONNXExpandOp>(node);
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if (opName == "EyeLike")
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buildOperation<mlir::ONNXEyeLikeOp>(node);
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if (opName == "Flatten")
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buildOperation<mlir::ONNXFlattenOp>(node);
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if (opName == "Floor")
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buildOperation<mlir::ONNXFloorOp>(node);
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if (opName == "GRU")
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buildOperation<mlir::ONNXGRUOp>(node);
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if (opName == "Gather")
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buildOperation<mlir::ONNXGatherOp>(node);
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if (opName == "GatherElements")
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buildOperation<mlir::ONNXGatherElementsOp>(node);
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if (opName == "GatherND")
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buildOperation<mlir::ONNXGatherNDOp>(node);
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if (opName == "Gemm")
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buildOperation<mlir::ONNXGemmOp>(node);
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if (opName == "GlobalAveragePool")
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buildOperation<mlir::ONNXGlobalAveragePoolOp>(node);
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if (opName == "GlobalLpPool")
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buildOperation<mlir::ONNXGlobalLpPoolOp>(node);
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if (opName == "GlobalMaxPool")
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buildOperation<mlir::ONNXGlobalMaxPoolOp>(node);
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if (opName == "Greater")
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buildOperation<mlir::ONNXGreaterOp>(node);
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if (opName == "HardSigmoid")
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buildOperation<mlir::ONNXHardSigmoidOp>(node);
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if (opName == "Hardmax")
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buildOperation<mlir::ONNXHardmaxOp>(node);
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if (opName == "Identity")
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buildOperation<mlir::ONNXIdentityOp>(node);
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if (opName == "If")
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buildOperation<mlir::ONNXIfOp>(node);
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if (opName == "InstanceNormalization")
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buildOperation<mlir::ONNXInstanceNormalizationOp>(node);
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if (opName == "IsInf")
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buildOperation<mlir::ONNXIsInfOp>(node);
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if (opName == "IsNaN")
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buildOperation<mlir::ONNXIsNaNOp>(node);
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if (opName == "LRN")
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buildOperation<mlir::ONNXLRNOp>(node);
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if (opName == "LSTM")
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buildOperation<mlir::ONNXLSTMOp>(node);
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if (opName == "LeakyRelu")
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buildOperation<mlir::ONNXLeakyReluOp>(node);
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if (opName == "Less")
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buildOperation<mlir::ONNXLessOp>(node);
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if (opName == "Log")
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buildOperation<mlir::ONNXLogOp>(node);
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if (opName == "LogSoftmax")
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buildOperation<mlir::ONNXLogSoftmaxOp>(node);
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if (opName == "Loop")
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buildOperation<mlir::ONNXLoopOp>(node);
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if (opName == "LpNormalization")
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buildOperation<mlir::ONNXLpNormalizationOp>(node);
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if (opName == "LpPool")
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buildOperation<mlir::ONNXLpPoolOp>(node);
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if (opName == "MatMul")
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buildOperation<mlir::ONNXMatMulOp>(node);
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if (opName == "MatMulInteger")
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buildOperation<mlir::ONNXMatMulIntegerOp>(node);
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if (opName == "Max")
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buildOperation<mlir::ONNXMaxOp>(node);
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if (opName == "MaxPool")
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ImportNodeMaxPool(node);
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if (opName == "MaxRoiPool")
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buildOperation<mlir::ONNXMaxRoiPoolOp>(node);
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if (opName == "MaxUnpool")
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buildOperation<mlir::ONNXMaxUnpoolOp>(node);
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if (opName == "Mean")
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buildOperation<mlir::ONNXMeanOp>(node);
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if (opName == "MeanVarianceNormalization")
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buildOperation<mlir::ONNXMeanVarianceNormalizationOp>(node);
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if (opName == "Min")
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buildOperation<mlir::ONNXMinOp>(node);
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if (opName == "Mod")
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buildOperation<mlir::ONNXModOp>(node);
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if (opName == "Mul")
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buildOperation<mlir::ONNXMulOp>(node);
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if (opName == "Multinomial")
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buildOperation<mlir::ONNXMultinomialOp>(node);
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if (opName == "Neg")
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buildOperation<mlir::ONNXNegOp>(node);
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if (opName == "NonMaxSuppression")
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buildOperation<mlir::ONNXNonMaxSuppressionOp>(node);
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if (opName == "NonZero")
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buildOperation<mlir::ONNXNonZeroOp>(node);
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if (opName == "Not")
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buildOperation<mlir::ONNXNotOp>(node);
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if (opName == "OneHot")
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buildOperation<mlir::ONNXOneHotOp>(node);
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if (opName == "Or")
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buildOperation<mlir::ONNXOrOp>(node);
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if (opName == "PRelu")
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buildOperation<mlir::ONNXPReluOp>(node);
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if (opName == "Pad")
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ImportNodePad(node);
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if (opName == "Pow")
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buildOperation<mlir::ONNXPowOp>(node);
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if (opName == "QLinearConv")
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buildOperation<mlir::ONNXQLinearConvOp>(node);
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if (opName == "QLinearMatMul")
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buildOperation<mlir::ONNXQLinearMatMulOp>(node);
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if (opName == "QuantizeLinear")
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buildOperation<mlir::ONNXQuantizeLinearOp>(node);
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if (opName == "RNN")
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buildOperation<mlir::ONNXRNNOp>(node);
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if (opName == "RandomNormal")
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buildOperation<mlir::ONNXRandomNormalOp>(node);
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if (opName == "RandomNormalLike")
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buildOperation<mlir::ONNXRandomNormalLikeOp>(node);
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if (opName == "RandomUniform")
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buildOperation<mlir::ONNXRandomUniformOp>(node);
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if (opName == "RandomUniformLike")
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buildOperation<mlir::ONNXRandomUniformLikeOp>(node);
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if (opName == "Range")
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buildOperation<mlir::ONNXRangeOp>(node);
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if (opName == "Reciprocal")
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buildOperation<mlir::ONNXReciprocalOp>(node);
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if (opName == "ReduceL1")
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buildOperation<mlir::ONNXReduceL1Op>(node);
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if (opName == "ReduceL2")
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buildOperation<mlir::ONNXReduceL2Op>(node);
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if (opName == "ReduceLogSum")
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buildOperation<mlir::ONNXReduceLogSumOp>(node);
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if (opName == "ReduceLogSumExp")
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buildOperation<mlir::ONNXReduceLogSumExpOp>(node);
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if (opName == "ReduceMax")
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buildOperation<mlir::ONNXReduceMaxOp>(node);
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if (opName == "ReduceMean")
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buildOperation<mlir::ONNXReduceMeanOp>(node);
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if (opName == "ReduceMin")
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buildOperation<mlir::ONNXReduceMinOp>(node);
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if (opName == "ReduceProd")
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buildOperation<mlir::ONNXReduceProdOp>(node);
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if (opName == "ReduceSum")
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buildOperation<mlir::ONNXReduceSumOp>(node);
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if (opName == "ReduceSumSquare")
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buildOperation<mlir::ONNXReduceSumSquareOp>(node);
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if (opName == "Relu")
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buildOperation<mlir::ONNXReluOp>(node);
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if (opName == "Reshape")
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buildOperation<mlir::ONNXReshapeOp>(node);
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if (opName == "Resize")
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buildOperation<mlir::ONNXResizeOp>(node);
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if (opName == "ReverseSequence")
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buildOperation<mlir::ONNXReverseSequenceOp>(node);
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if (opName == "RoiAlign")
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buildOperation<mlir::ONNXRoiAlignOp>(node);
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if (opName == "Round")
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buildOperation<mlir::ONNXRoundOp>(node);
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if (opName == "Scan")
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buildOperation<mlir::ONNXScanOp>(node);
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if (opName == "Scatter")
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buildOperation<mlir::ONNXScatterOp>(node);
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if (opName == "ScatterElements")
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buildOperation<mlir::ONNXScatterElementsOp>(node);
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if (opName == "ScatterND")
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buildOperation<mlir::ONNXScatterNDOp>(node);
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if (opName == "Selu")
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buildOperation<mlir::ONNXSeluOp>(node);
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if (opName == "SequenceAt")
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buildOperation<mlir::ONNXSequenceAtOp>(node);
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if (opName == "SequenceConstruct")
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buildOperation<mlir::ONNXSequenceConstructOp>(node);
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if (opName == "SequenceEmpty")
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buildOperation<mlir::ONNXSequenceEmptyOp>(node);
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if (opName == "SequenceErase")
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buildOperation<mlir::ONNXSequenceEraseOp>(node);
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if (opName == "SequenceInsert")
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buildOperation<mlir::ONNXSequenceInsertOp>(node);
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if (opName == "SequenceLength")
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buildOperation<mlir::ONNXSequenceLengthOp>(node);
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if (opName == "Shape")
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buildOperation<mlir::ONNXShapeOp>(node);
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if (opName == "Shrink")
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buildOperation<mlir::ONNXShrinkOp>(node);
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if (opName == "Sigmoid")
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buildOperation<mlir::ONNXSigmoidOp>(node);
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if (opName == "Sign")
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buildOperation<mlir::ONNXSignOp>(node);
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if (opName == "Sin")
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buildOperation<mlir::ONNXSinOp>(node);
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if (opName == "Sinh")
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buildOperation<mlir::ONNXSinhOp>(node);
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if (opName == "Size")
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buildOperation<mlir::ONNXSizeOp>(node);
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if (opName == "Slice")
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buildOperation<mlir::ONNXSliceOp>(node);
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if (opName == "Softmax")
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buildOperation<mlir::ONNXSoftmaxOp>(node);
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if (opName == "Softplus")
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buildOperation<mlir::ONNXSoftplusOp>(node);
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if (opName == "Softsign")
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buildOperation<mlir::ONNXSoftsignOp>(node);
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if (opName == "SpaceToDepth")
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buildOperation<mlir::ONNXSpaceToDepthOp>(node);
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if (opName == "Split")
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buildOperation<mlir::ONNXSplitOp>(node);
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if (opName == "SplitToSequence")
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buildOperation<mlir::ONNXSplitToSequenceOp>(node);
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if (opName == "Sqrt")
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buildOperation<mlir::ONNXSqrtOp>(node);
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if (opName == "Squeeze")
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buildOperation<mlir::ONNXSqueezeOp>(node);
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if (opName == "StringNormalizer")
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buildOperation<mlir::ONNXStringNormalizerOp>(node);
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if (opName == "Sub")
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buildOperation<mlir::ONNXSubOp>(node);
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if (opName == "Sum")
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buildOperation<mlir::ONNXSumOp>(node);
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if (opName == "Tan")
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buildOperation<mlir::ONNXTanOp>(node);
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if (opName == "Tanh")
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buildOperation<mlir::ONNXTanhOp>(node);
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if (opName == "TfIdfVectorizer")
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buildOperation<mlir::ONNXTfIdfVectorizerOp>(node);
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if (opName == "ThresholdedRelu")
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buildOperation<mlir::ONNXThresholdedReluOp>(node);
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if (opName == "Tile")
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buildOperation<mlir::ONNXTileOp>(node);
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if (opName == "TopK")
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buildOperation<mlir::ONNXTopKOp>(node);
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if (opName == "Transpose")
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buildOperation<mlir::ONNXTransposeOp>(node);
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if (opName == "Unique")
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buildOperation<mlir::ONNXUniqueOp>(node);
|
||||
if (opName == "Unsqueeze")
|
||||
buildOperation<mlir::ONNXUnsqueezeOp>(node);
|
||||
if (opName == "Upsample")
|
||||
buildOperation<mlir::ONNXUpsampleOp>(node);
|
||||
if (opName == "Where")
|
||||
buildOperation<mlir::ONNXWhereOp>(node);
|
||||
if (opName == "Xor")
|
||||
buildOperation<mlir::ONNXXorOp>(node);
|
||||
if (opName == "ArrayFeatureExtractor")
|
||||
buildOperation<mlir::ONNXArrayFeatureExtractorOp>(node);
|
||||
if (opName == "Binarizer")
|
||||
buildOperation<mlir::ONNXBinarizerOp>(node);
|
||||
if (opName == "CastMap")
|
||||
buildOperation<mlir::ONNXCastMapOp>(node);
|
||||
if (opName == "CategoryMapper")
|
||||
buildOperation<mlir::ONNXCategoryMapperOp>(node);
|
||||
if (opName == "DictVectorizer")
|
||||
buildOperation<mlir::ONNXDictVectorizerOp>(node);
|
||||
if (opName == "FeatureVectorizer")
|
||||
buildOperation<mlir::ONNXFeatureVectorizerOp>(node);
|
||||
if (opName == "Imputer")
|
||||
buildOperation<mlir::ONNXImputerOp>(node);
|
||||
if (opName == "LabelEncoder")
|
||||
buildOperation<mlir::ONNXLabelEncoderOp>(node);
|
||||
if (opName == "LinearClassifier")
|
||||
buildOperation<mlir::ONNXLinearClassifierOp>(node);
|
||||
if (opName == "LinearRegressor")
|
||||
buildOperation<mlir::ONNXLinearRegressorOp>(node);
|
||||
if (opName == "Normalizer")
|
||||
buildOperation<mlir::ONNXNormalizerOp>(node);
|
||||
if (opName == "OneHotEncoder")
|
||||
buildOperation<mlir::ONNXOneHotEncoderOp>(node);
|
||||
if (opName == "SVMClassifier")
|
||||
buildOperation<mlir::ONNXSVMClassifierOp>(node);
|
||||
if (opName == "SVMRegressor")
|
||||
buildOperation<mlir::ONNXSVMRegressorOp>(node);
|
||||
if (opName == "Scaler")
|
||||
buildOperation<mlir::ONNXScalerOp>(node);
|
||||
if (opName == "TreeEnsembleClassifier")
|
||||
buildOperation<mlir::ONNXTreeEnsembleClassifierOp>(node);
|
||||
if (opName == "TreeEnsembleRegressor")
|
||||
buildOperation<mlir::ONNXTreeEnsembleRegressorOp>(node);
|
||||
if (opName == "ZipMap")
|
||||
buildOperation<mlir::ONNXZipMapOp>(node);
|
||||
import_handler_map_["Abs"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAbsOp>;
|
||||
import_handler_map_["Acos"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAcosOp>;
|
||||
import_handler_map_["Acosh"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAcoshOp>;
|
||||
import_handler_map_["Add"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAddOp>;
|
||||
import_handler_map_["And"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAndOp>;
|
||||
import_handler_map_["ArgMax"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXArgMaxOp>;
|
||||
import_handler_map_["ArgMin"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXArgMinOp>;
|
||||
import_handler_map_["Asin"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAsinOp>;
|
||||
import_handler_map_["Asinh"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAsinhOp>;
|
||||
import_handler_map_["Atan"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAtanOp>;
|
||||
import_handler_map_["Atanh"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAtanhOp>;
|
||||
import_handler_map_["AveragePool"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXAveragePoolOp>;
|
||||
import_handler_map_["BatchNormalization"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::ImportNodeBatchNormalization;
|
||||
import_handler_map_["BitShift"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXBitShiftOp>;
|
||||
import_handler_map_["Cast"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCastOp>;
|
||||
import_handler_map_["Ceil"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCeilOp>;
|
||||
import_handler_map_["Clip"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXClipOp>;
|
||||
import_handler_map_["Compress"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCompressOp>;
|
||||
import_handler_map_["Concat"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConcatOp>;
|
||||
import_handler_map_["ConcatFromSequence"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConcatFromSequenceOp>;
|
||||
import_handler_map_["Constant"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConstantOp>;
|
||||
import_handler_map_["ConstantOfShape"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConstantOfShapeOp>;
|
||||
import_handler_map_["Conv"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConvOp>;
|
||||
import_handler_map_["ConvInteger"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConvIntegerOp>;
|
||||
import_handler_map_["ConvTranspose"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXConvTransposeOp>;
|
||||
import_handler_map_["Cos"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCosOp>;
|
||||
import_handler_map_["Cosh"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCoshOp>;
|
||||
import_handler_map_["CumSum"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCumSumOp>;
|
||||
import_handler_map_["DepthToSpace"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDepthToSpaceOp>;
|
||||
import_handler_map_["DequantizeLinear"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDequantizeLinearOp>;
|
||||
import_handler_map_["Det"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDetOp>;
|
||||
import_handler_map_["Div"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDivOp>;
|
||||
import_handler_map_["Dropout"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDropoutOp>;
|
||||
import_handler_map_["DynamicQuantizeLinear"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDynamicQuantizeLinearOp>;
|
||||
import_handler_map_["Elu"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXEluOp>;
|
||||
import_handler_map_["Equal"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXEqualOp>;
|
||||
import_handler_map_["Erf"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXErfOp>;
|
||||
import_handler_map_["Exp"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXExpOp>;
|
||||
import_handler_map_["Expand"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXExpandOp>;
|
||||
import_handler_map_["EyeLike"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXEyeLikeOp>;
|
||||
import_handler_map_["Flatten"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXFlattenOp>;
|
||||
import_handler_map_["Floor"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXFloorOp>;
|
||||
import_handler_map_["GRU"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGRUOp>;
|
||||
import_handler_map_["Gather"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGatherOp>;
|
||||
import_handler_map_["GatherElements"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGatherElementsOp>;
|
||||
import_handler_map_["GatherND"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGatherNDOp>;
|
||||
import_handler_map_["Gemm"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGemmOp>;
|
||||
import_handler_map_["GlobalAveragePool"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGlobalAveragePoolOp>;
|
||||
import_handler_map_["GlobalLpPool"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGlobalLpPoolOp>;
|
||||
import_handler_map_["GlobalMaxPool"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGlobalMaxPoolOp>;
|
||||
import_handler_map_["Greater"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGreaterOp>;
|
||||
import_handler_map_["HardSigmoid"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXHardSigmoidOp>;
|
||||
import_handler_map_["Hardmax"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXHardmaxOp>;
|
||||
import_handler_map_["Identity"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXIdentityOp>;
|
||||
import_handler_map_["If"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXIfOp>;
|
||||
import_handler_map_["InstanceNormalization"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXInstanceNormalizationOp>;
|
||||
import_handler_map_["IsInf"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXIsInfOp>;
|
||||
import_handler_map_["IsNaN"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXIsNaNOp>;
|
||||
import_handler_map_["LRN"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLRNOp>;
|
||||
import_handler_map_["LSTM"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLSTMOp>;
|
||||
import_handler_map_["LeakyRelu"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLeakyReluOp>;
|
||||
import_handler_map_["Less"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLessOp>;
|
||||
import_handler_map_["Log"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLogOp>;
|
||||
import_handler_map_["LogSoftmax"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLogSoftmaxOp>;
|
||||
import_handler_map_["Loop"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLoopOp>;
|
||||
import_handler_map_["LpNormalization"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLpNormalizationOp>;
|
||||
import_handler_map_["LpPool"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLpPoolOp>;
|
||||
import_handler_map_["MatMul"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMatMulOp>;
|
||||
import_handler_map_["MatMulInteger"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMatMulIntegerOp>;
|
||||
import_handler_map_["Max"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMaxOp>;
|
||||
import_handler_map_["MaxPool"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::ImportNodeMaxPool;
|
||||
import_handler_map_["MaxRoiPool"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMaxRoiPoolOp>;
|
||||
import_handler_map_["MaxUnpool"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMaxUnpoolOp>;
|
||||
import_handler_map_["Mean"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMeanOp>;
|
||||
import_handler_map_["MeanVarianceNormalization"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMeanVarianceNormalizationOp>;
|
||||
import_handler_map_["Min"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMinOp>;
|
||||
import_handler_map_["Mod"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXModOp>;
|
||||
import_handler_map_["Mul"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMulOp>;
|
||||
import_handler_map_["Multinomial"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXMultinomialOp>;
|
||||
import_handler_map_["Neg"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXNegOp>;
|
||||
import_handler_map_["NonMaxSuppression"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXNonMaxSuppressionOp>;
|
||||
import_handler_map_["NonZero"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXNonZeroOp>;
|
||||
import_handler_map_["Not"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXNotOp>;
|
||||
import_handler_map_["OneHot"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXOneHotOp>;
|
||||
import_handler_map_["Or"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXOrOp>;
|
||||
import_handler_map_["PRelu"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXPReluOp>;
|
||||
import_handler_map_["Pad"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::ImportNodePad;
|
||||
import_handler_map_["Pow"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXPowOp>;
|
||||
import_handler_map_["QLinearConv"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXQLinearConvOp>;
|
||||
import_handler_map_["QLinearMatMul"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXQLinearMatMulOp>;
|
||||
import_handler_map_["QuantizeLinear"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXQuantizeLinearOp>;
|
||||
import_handler_map_["RNN"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRNNOp>;
|
||||
import_handler_map_["RandomNormal"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRandomNormalOp>;
|
||||
import_handler_map_["RandomNormalLike"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRandomNormalLikeOp>;
|
||||
import_handler_map_["RandomUniform"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRandomUniformOp>;
|
||||
import_handler_map_["RandomUniformLike"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRandomUniformLikeOp>;
|
||||
import_handler_map_["Range"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRangeOp>;
|
||||
import_handler_map_["Reciprocal"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReciprocalOp>;
|
||||
import_handler_map_["ReduceL1"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceL1Op>;
|
||||
import_handler_map_["ReduceL2"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceL2Op>;
|
||||
import_handler_map_["ReduceLogSum"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceLogSumOp>;
|
||||
import_handler_map_["ReduceLogSumExp"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceLogSumExpOp>;
|
||||
import_handler_map_["ReduceMax"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceMaxOp>;
|
||||
import_handler_map_["ReduceMean"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceMeanOp>;
|
||||
import_handler_map_["ReduceMin"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceMinOp>;
|
||||
import_handler_map_["ReduceProd"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceProdOp>;
|
||||
import_handler_map_["ReduceSum"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceSumOp>;
|
||||
import_handler_map_["ReduceSumSquare"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReduceSumSquareOp>;
|
||||
import_handler_map_["Relu"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReluOp>;
|
||||
import_handler_map_["Reshape"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReshapeOp>;
|
||||
import_handler_map_["Resize"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXResizeOp>;
|
||||
import_handler_map_["ReverseSequence"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXReverseSequenceOp>;
|
||||
import_handler_map_["RoiAlign"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRoiAlignOp>;
|
||||
import_handler_map_["Round"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXRoundOp>;
|
||||
import_handler_map_["Scan"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXScanOp>;
|
||||
import_handler_map_["Scatter"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXScatterOp>;
|
||||
import_handler_map_["ScatterElements"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXScatterElementsOp>;
|
||||
import_handler_map_["ScatterND"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXScatterNDOp>;
|
||||
import_handler_map_["Selu"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSeluOp>;
|
||||
import_handler_map_["SequenceAt"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceAtOp>;
|
||||
import_handler_map_["SequenceConstruct"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceConstructOp>;
|
||||
import_handler_map_["SequenceEmpty"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceEmptyOp>;
|
||||
import_handler_map_["SequenceErase"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceEraseOp>;
|
||||
import_handler_map_["SequenceInsert"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceInsertOp>;
|
||||
import_handler_map_["SequenceLength"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSequenceLengthOp>;
|
||||
import_handler_map_["Shape"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXShapeOp>;
|
||||
import_handler_map_["Shrink"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXShrinkOp>;
|
||||
import_handler_map_["Sigmoid"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSigmoidOp>;
|
||||
import_handler_map_["Sign"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSignOp>;
|
||||
import_handler_map_["Sin"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSinOp>;
|
||||
import_handler_map_["Sinh"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSinhOp>;
|
||||
import_handler_map_["Size"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSizeOp>;
|
||||
import_handler_map_["Slice"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSliceOp>;
|
||||
import_handler_map_["Softmax"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSoftmaxOp>;
|
||||
import_handler_map_["Softplus"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSoftplusOp>;
|
||||
import_handler_map_["Softsign"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSoftsignOp>;
|
||||
import_handler_map_["SpaceToDepth"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSpaceToDepthOp>;
|
||||
import_handler_map_["Split"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSplitOp>;
|
||||
import_handler_map_["SplitToSequence"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSplitToSequenceOp>;
|
||||
import_handler_map_["Sqrt"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSqrtOp>;
|
||||
import_handler_map_["Squeeze"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSqueezeOp>;
|
||||
import_handler_map_["StringNormalizer"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXStringNormalizerOp>;
|
||||
import_handler_map_["Sub"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSubOp>;
|
||||
import_handler_map_["Sum"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSumOp>;
|
||||
import_handler_map_["Tan"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTanOp>;
|
||||
import_handler_map_["Tanh"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTanhOp>;
|
||||
import_handler_map_["TfIdfVectorizer"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTfIdfVectorizerOp>;
|
||||
import_handler_map_["ThresholdedRelu"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXThresholdedReluOp>;
|
||||
import_handler_map_["Tile"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTileOp>;
|
||||
import_handler_map_["TopK"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTopKOp>;
|
||||
import_handler_map_["Transpose"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTransposeOp>;
|
||||
import_handler_map_["Unique"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXUniqueOp>;
|
||||
import_handler_map_["Unsqueeze"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXUnsqueezeOp>;
|
||||
import_handler_map_["Upsample"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXUpsampleOp>;
|
||||
import_handler_map_["Where"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXWhereOp>;
|
||||
import_handler_map_["Xor"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXXorOp>;
|
||||
import_handler_map_["ArrayFeatureExtractor"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXArrayFeatureExtractorOp>;
|
||||
import_handler_map_["Binarizer"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXBinarizerOp>;
|
||||
import_handler_map_["CastMap"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCastMapOp>;
|
||||
import_handler_map_["CategoryMapper"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXCategoryMapperOp>;
|
||||
import_handler_map_["DictVectorizer"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXDictVectorizerOp>;
|
||||
import_handler_map_["FeatureVectorizer"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXFeatureVectorizerOp>;
|
||||
import_handler_map_["Imputer"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXImputerOp>;
|
||||
import_handler_map_["LabelEncoder"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLabelEncoderOp>;
|
||||
import_handler_map_["LinearClassifier"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLinearClassifierOp>;
|
||||
import_handler_map_["LinearRegressor"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXLinearRegressorOp>;
|
||||
import_handler_map_["Normalizer"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXNormalizerOp>;
|
||||
import_handler_map_["OneHotEncoder"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXOneHotEncoderOp>;
|
||||
import_handler_map_["SVMClassifier"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSVMClassifierOp>;
|
||||
import_handler_map_["SVMRegressor"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXSVMRegressorOp>;
|
||||
import_handler_map_["Scaler"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXScalerOp>;
|
||||
import_handler_map_["TreeEnsembleClassifier"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTreeEnsembleClassifierOp>;
|
||||
import_handler_map_["TreeEnsembleRegressor"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXTreeEnsembleRegressorOp>;
|
||||
import_handler_map_["ZipMap"] =
|
||||
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXZipMapOp>;
|
||||
|
|
|
|||
|
|
@ -954,7 +954,7 @@ special cases:
|
|||
|
||||
def gen_op_importer(schema, file):
|
||||
indent = inc_indent()
|
||||
s = indent + 'if (opName == "' + schema.name + '")\n'
|
||||
s = indent + 'import_handler_map_["' + schema.name +'"] = \n '
|
||||
|
||||
expected_num_operands = len(schema.inputs)
|
||||
expected_num_results = len(schema.outputs)
|
||||
|
|
@ -978,8 +978,8 @@ def gen_op_importer(schema, file):
|
|||
args.append(
|
||||
'/* expected_num_results = */ {}'.format(expected_num_results))
|
||||
"""
|
||||
s += inc_indent(indent) + " {}({});\n".format(
|
||||
handler_func, ", ".join(args))
|
||||
s += inc_indent(indent) + '&onnx_mlir::detail::FrontendGenImpl::'
|
||||
s += handler_func+';\n'
|
||||
|
||||
file.write(s)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue