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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);
const auto& data_dims = GetDimsVector(in[0]);
const auto& indices_dims = GetDimsVector(in[1]);
vector<int> output_dims =
caffe2::gather_helper::calc_output_shape_vector<int>(
data_dims, indices_dims, 1, false);
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 second outer dimension (axis == 1) of DATA indexed by INDICES,
and concatenate them in an output tensor of rank q + (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 + (r - 1).")
.InheritOnnxSchema();
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