#ifndef CAFFE2_OPERATORS_STRING_OPS_H_
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#define CAFFE2_OPERATORS_STRING_OPS_H_
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#include "caffe2/core/operator.h"
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#include "caffe2/operators/elementwise_ops.h"
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namespace caffe2 {
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/**
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* ForEach is a unary functor that forwards each element of the input array
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* into the elementwise Functor provided, and gathers the results of each
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* call into the resulting array. Use it as an adaptor if you want to create
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* a UnaryElementwiseOp that acts on each element of the tensor per function
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* call -- this is resonable for complex types where vectorization wouldn't
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* be much of a gain, performance-wise.
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*/
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template <typename Functor>
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struct ForEach {
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explicit ForEach(OperatorBase& op) : functor(op) {}
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template <typename In, typename Out, typename Context>
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bool operator()(int n, const In* in, Out* out, Context* /*c*/) {
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for (int i = 0; i < n; ++i) {
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out[i] = functor(in[i]);
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}
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return true;
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}
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Functor functor;
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};
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template <typename ScalarFunctor, typename TypeMap = FixedType<std::string>>
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using StringElementwiseOp = UnaryElementwiseWithArgsOp<
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TensorTypes<std::string>,
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CPUContext,
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ForEach<ScalarFunctor>,
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TypeMap>;
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template <class Context>
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class StringJoinOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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template <class... Args>
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explicit StringJoinOp(Args&&... args)
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: Operator<Context>(std::forward<Args>(args)...),
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delimiter_(
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this->template GetSingleArgument<std::string>("delimiter", ",")),
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axis_(this->template GetSingleArgument<int>("axis", 0)) {
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CAFFE_ENFORCE(axis_ == 0 || axis_ == 1);
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}
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bool RunOnDevice() override {
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return DispatchHelper<TensorTypes<
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float,
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double,
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int8_t,
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uint8_t,
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int16_t,
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uint16_t,
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int32_t,
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int64_t,
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std::string,
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bool>>::call(this, Input(0));
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}
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template <typename T>
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bool DoRunWithType();
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protected:
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std::string delimiter_;
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int axis_;
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};
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_STRING_OPS_H_
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