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#include "caffe2/operators/elementwise_logical_ops.h"
namespace caffe2 {
namespace {
REGISTER_CPU_OPERATOR(Where, WhereOp<CPUContext>);
// Input: C, X, Y, output: Z
OPERATOR_SCHEMA(Where)
.NumInputs(3)
.NumOutputs(1)
.AllowInplace({{1, 2}})
.IdenticalTypeAndShapeOfInput(1)
.SetDoc(R"DOC(
Operator Where takes three input data (Tensor, Tensor, Tensor) and
produces one output data (Tensor) where z = c ? x : y is applied elementwise.
)DOC")
.Input(0, "C", "input tensor containing booleans")
.Input(1, "X", "input tensor")
.Input(2, "Y", "input tensor")
.Output(0, "Z", "output tensor");
SHOULD_NOT_DO_GRADIENT(Where);
REGISTER_CPU_OPERATOR(IsMemberOf, IsMemberOfOp<CPUContext>);
// Input: X, output: Y
OPERATOR_SCHEMA(IsMemberOf)
.NumInputs(1)
.NumOutputs(1)
.TensorInferenceFunction(
[](const OperatorDef&, const vector<TensorShape>& input_types) {
vector<TensorShape> out(1);
out[0] = input_types[0];
out[0].set_data_type(TensorProto_DataType::TensorProto_DataType_BOOL);
return out;
})
.Arg("value", "*(type: []; default: -)* List of values to check for membership.")
.Arg("dtype", "*(type: TensorProto_DataType; default: -)* The data type for the elements of the output tensor. Strictly must be one of the types from DataType enum in TensorProto.")
.SetDoc(R"DOC(
The *IsMemberOf* op takes an input tensor *X* and a list of values as argument, and produces one output data tensor *Y*. The output tensor is the same shape as *X* and contains booleans. The output is calculated as the function *f(x) = x in value* and is applied to *X* elementwise.
Github Links:
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/elementwise_logical_ops.cc
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/elementwise_logical_ops.h
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"IsMemberOf",
["X"],
["Y"],
value=[0,2,4,6,8],
)
// Use a not-empty tensor
workspace.FeedBlob("X", np.array([0,1,2,3,4,5,6,7,8]).astype(np.int32))
print("X:\n", workspace.FetchBlob("X"))
workspace.RunOperatorOnce(op)
print("Y: \n", workspace.FetchBlob("Y"))
```
**Result**
```
// value=[0,2,4,6,8]
X:
[0 1 2 3 4 5 6 7 8]
Y:
[ True False True False True False True False True]
```
</details>
)DOC")
.Input(0, "X", "Input tensor of any shape")
.Output(0, "Y", "Output tensor (same size as X containing booleans)");
SHOULD_NOT_DO_GRADIENT(IsMemberOf);
} // namespace
template <>
std::unordered_set<int32_t>& IsMemberOfValueHolder::get<int32_t>() {
return int32_values_;
}
template <>
std::unordered_set<int64_t>& IsMemberOfValueHolder::get<int64_t>() {
return int64_values_;
}
template <>
std::unordered_set<bool>& IsMemberOfValueHolder::get<bool>() {
return bool_values_;
}
template <>
std::unordered_set<string>& IsMemberOfValueHolder::get<string>() {
return string_values_;
}
} // namespace caffe2
|
