#ifndef CAFFE2_OPERATORS_FLATTEN_OP_H_
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#define CAFFE2_OPERATORS_FLATTEN_OP_H_
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#include "caffe2/core/operator.h"
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namespace caffe2 {
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template <class Context>
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class FlattenOp : 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 FlattenOp(Args&&... args)
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: Operator<Context>(std::forward<Args>(args)...),
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axis_(this->template GetSingleArgument<int>("axis", 1)) {}
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bool RunOnDevice() override {
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auto& input = Input(0);
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auto* output = Output(0);
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CAFFE_ENFORCE_GE(
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input.dim(), axis_, "The rank of the tensor must be >= axis.");
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output->Resize(input.size_to_dim(axis_), input.size_from_dim(axis_));
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context_.CopyItemsSameDevice(
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input.dtype(),
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input.numel(),
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input.raw_data(),
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output->raw_mutable_data(input.dtype()));
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return true;
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}
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private:
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int axis_;
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};
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inline std::vector<TensorShape> TensorInferenceForFlatten(
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const OperatorDef& def,
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const std::vector<TensorShape>& in) {
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ArgumentHelper helper(def);
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const int axis = helper.GetSingleArgument<int>("axis", 1);
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std::vector<TensorShape> out(1);
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int64_t outer = 1;
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int64_t inner = 1;
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std::size_t index = 0;
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for (auto d : in[0].dims()) {
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if (index < axis) {
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outer *= d;
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} else {
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inner *= d;
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}
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++index;
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}
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out[0].set_data_type(in[0].data_type());
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out[0].add_dims(outer);
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out[0].add_dims(inner);
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return out;
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}
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_FLATTEN_OP_H_
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