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batch_gather_ops.cc
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batch_gather_ops.cc
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#include "caffe2/operators/batch_gather_ops.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(BatchGather, BatchGatherOp<CPUContext>);
REGISTER_CPU_OPERATOR(BatchGatherGradient, BatchGatherGradientOp<CPUContext>);
OPERATOR_SCHEMA(BatchGather)
.NumInputs(2)
.NumOutputs(1)
.TensorInferenceFunction([](const OperatorDef& def,
const vector<TensorShape>& in) {
vector<TensorShape> out(1);
ArgumentHelper helper(def);
vector<int> output_dims;
const auto& data_dims = GetDimsVector(in[0]);
const auto& indices_dims = GetDimsVector(in[1]);
output_dims.push_back(data_dims[0]);
output_dims.insert(
output_dims.end(), indices_dims.begin(), indices_dims.end());
output_dims.insert(
output_dims.end(), data_dims.begin() + 2, data_dims.end());
out[0] = CreateTensorShape(output_dims, TensorProto::FLOAT);
return out;
})
.SetDoc(R"DOC(
Batch gather operation, first dimension in DATA is the batch size.
Given DATA tensor of rank r >= 2, and INDICES tensor of rank q >= 1, gather
entries of the outer-most dimension of DATA indexed by INDICES, and concatenate
them in an output tensor of rank (q - 1) + (r - 1).
Example:
DATA = [
[1.0, 1.2, 2.4, 4.5],
[2.3, 3.4, 3.6, 2.3],
[4.5, 5.7, 1.2, 4.5],
]
INDICES = [
[0, 2],
]
OUTPUT = [
[1.0, 2.4],
[2.3, 3.6],
[4.5, 1.2],
]
)DOC")
.Input(0, "DATA", "Tensor of rank r >= 2.")
.Input(1, "INDICES", "Tensor of int32/int64 indices, of any rank q.")
.Output(0, "OUTPUT", "Tensor of rank (q - 1) + (r - 1).");
OPERATOR_SCHEMA(BatchGatherGradient).NumInputs(3).NumOutputs(1);
class GetBatchGatherGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
using Op = BatchGatherOp<CPUContext>;
return SingleGradientDef(
"BatchGatherGradient",
"",
vector<string>{I(Op::DATA), I(Op::INDICES), GO(0)},
vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(BatchGather, GetBatchGatherGradient);
} // namespace caffe2