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#include "caffe2/operators/shape_op.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(Shape, ShapeOp<CPUContext>);
OPERATOR_SCHEMA(Shape)
.NumInputs(1)
.NumOutputs(1)
.Arg(
"axes",
"*(type: int[])* Array of interested axes."
"If given, this operator only returns the dimensions of the given axes."
"Otherwise, the operator returns the dimensions of all axes.")
.TensorInferenceFunction([](const OperatorDef& def,
const vector<TensorShape>& in) {
ArgumentHelper args(def);
const vector<int>& axes = args.GetRepeatedArgument<int>("axes");
vector<TensorShape> out(1);
if (axes.empty()) {
out[0].add_dims(in[0].dims().size());
} else {
out[0].add_dims(axes.size());
}
out[0].set_data_type(TensorProto::INT64);
return out;
})
.SetDoc(R"DOC(
Produce a 1D int64 tensor with the shape of the input tensor.
If called with an optional argument `axes`, the result will only
contain the dimensions of specified axes.
Github Link:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/shape_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Shape",
["X"],
["shape"],
)
workspace.FeedBlob("X", (np.random.randint(10, size=(2,3))))
print("X:", workspace.FetchBlob("X"))
workspace.RunOperatorOnce(op)
print("shape:", workspace.FetchBlob("shape"))
```
**Result**
```
X:
[[3 2 5]
[5 7 3]]
shape: [2 3]
```
</details>
)DOC")
.Input(0,"X", "*(type: Tensor)* Input tensor.")
.Output(0,"shape", "*(type: Tensor)* Output tensor containing shape of input tensor.");
SHOULD_NOT_DO_GRADIENT(Shape);
} // namespace caffe2
|
