sheep94lion opened a new issue #15333: Compile error for custom operators in C URL: https://github.com/apache/incubator-mxnet/issues/15333 ## Description I define a custom operator under src/operator/contrib/ I met compiling errors saying: In file included from /home/yizhao/Code/mxnet-dev/3rdparty/dmlc-core/include``` /dmlc/registry.h:14:0, from include/mxnet/operator_util.h:37, from src/operator/contrib/./rpn_inv_normalize_op-inl.h:4, from src/operator/contrib/rpn_inv_normalize_op.cc:1: /home/yizhao/Code/mxnet-dev/3rdparty/dmlc-core/include/dmlc/./parameter.h: In instantiation of ‘void dmlc::parameter::FieldEntryBase<TEntry, DType>::Set(void*, const string&) const [with TEntry = dmlc::parameter::FieldEntry<float*>; DType = float*; std::string = std::basic_string<char>]’: src/operator/contrib/rpn_inv_normalize_op.cc:20:1: required from here /home/yizhao/Code/mxnet-dev/3rdparty/dmlc-core/include/dmlc/./parameter.h:558:8: error: cannot bind ‘std::basic_istream<char>’ lvalue to ‘std::basic_istream<char>&&’ is >> this->Get(head); ^ In file included from /usr/include/c++/4.8/sstream:38:0, from /home/yizhao/Code/mxnet-dev/3rdparty/dmlc-core/include/dmlc/./logging.h:20, from /home/yizhao/Code/mxnet-dev/3rdparty/dmlc-core/include/dmlc/registry.h:13, from include/mxnet/operator_util.h:37, from src/operator/contrib/./rpn_inv_normalize_op-inl.h:4, from src/operator/contrib/rpn_inv_normalize_op.cc:1: /usr/include/c++/4.8/istream:872:5: error: initializing argument 1 of ‘std::basic_istream<_CharT, _Traits>& std::operator>>(std::basic_istream<_CharT, _Traits>&&, _Tp&) [with _CharT = char; _Traits = std::char_traits<char>; _Tp = float*]’ operator>>(basic_istream<_CharT, _Traits>&& __is, _Tp& __x) ^ make: *** [Makefile:532: build/src/operator/contrib/rpn_inv_normalize_op.o] Error 1 ``` I explored the related source files mentioned in the error message but it is hard to locate the problem in my code. I follow the tutorial from https://mxnet.incubator.apache.org/versions/master/faq/add_op_in_backend.html. My codes are as following: rpn_inv_normalize_op-inl.h ``` #ifndef MXNET_OPERATOR_CONTRIB_RPN_INV_NORMALIZE_OP_INL_H_ #define MXNET_OPERATOR_CONTRIB_RPN_INV_NORMALIZE_OP_INL_H_ #include <mxnet/operator_util.h> #include <vector> #include "../mshadow_op.h" #include "../mxnet_op.h" #include "../operator_common.h" #include "../elemwise_op_common.h" #include "../tensor/init_op.h" namespace mxnet { namespace op { struct RpnInvNormalizeParam: public dmlc::Parameter<RpnInvNormalizeParam> { int num_anchors; float *bbox_mean, *bbox_std; DMLC_DECLARE_PARAMETER(RpnInvNormalizeParam) { DMLC_DECLARE_FIELD(num_anchors) .describe("Num of anchors."); DMLC_DECLARE_FIELD(bbox_mean) .describe("Bbox mean."); DMLC_DECLARE_FIELD(bbox_std) .describe("Bbox std."); } }; inline bool RpnInvNormalizeOpShape(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector* in_attrs, mxnet::ShapeVector* out_attrs) { return true; } inline bool RpnInvNormalizeOpType(const nnvm::NodeAttrs& attrs, std::vector<int>* in_attrs, std::vector<int>* out_attrs) { return true; } template<typename xpu> void RpnInvNormalizeOpForward(const nnvm::NodeAttrs& attrs, const OpContext& ctx, const std::vector<TBlob>& inputs, const std::vector<OpReqType>& req, const std::vector<TBlob>& outputs) { mshadow::Stream<xpu> *s = ctx.get_stream<xpu>(); const TBlob& in_data = inputs[0]; const TBlob& out_data = outputs[0]; const RpnInvNormalizeParam& param = nnvm::get<RpnInvNormalizeParam>(attrs.parsed); using namespace mxnet_op; MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, { index_t i1, i2, i3, i4; mshadow::Tensor<xpu, 4, DType> in_ts = in_data.get<xpu, 4, DType>(s); mshadow::Tensor<xpu, 4, DType> out_ts = out_data.get<xpu, 4, DType>(s); for (i1 = 0; i1 < out_ts.size(0); i1++) { for (i2 = 0; i2 < out_ts.size(1); i2++) { for (i3 = 0; i3 < out_ts.size(2); i3++) { for (i4 = 0; i4 < out_ts.size(3); i4++) { float std = param.bbox_std[i2 % 4]; float mean = param.bbox_mean[i2 % 4]; KERNEL_ASSIGN(out_ts[i1][i2][i3][i4], req[0], in_ts[i1][i2][i3][i4] * std + mean); } } } } }); } } } #endif ``` ## Environment info (Required) ``` What to do: 1. Download the diagnosis script from https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py 2. Run the script using `python diagnose.py` and paste its output here. ``` Package used (Python/R/Scala/Julia): (I'm using ...) For Scala user, please provide: 1. Java version: (`java -version`) 2. Maven version: (`mvn -version`) 3. Scala runtime if applicable: (`scala -version`) For R user, please provide R `sessionInfo()`: ## Build info (Required if built from source) Compiler (gcc/clang/mingw/visual studio): MXNet commit hash: (Paste the output of `git rev-parse HEAD` here.) Build config: (Paste the content of config.mk, or the build command.) ## Error Message: (Paste the complete error message, including stack trace.) ## Minimum reproducible example (If you are using your own code, please provide a short script that reproduces the error. Otherwise, please provide link to the existing example.) ## Steps to reproduce (Paste the commands you ran that produced the error.) 1. 2. ## What have you tried to solve it? 1. 2.
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