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#include "caffe2/operators/flatten_op.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(Flatten, FlattenOp<CPUContext>);
OPERATOR_SCHEMA(Flatten)
.NumInputs(1)
.NumOutputs(1)
.TensorInferenceFunction(TensorInferenceForFlatten)
.SetDoc(R"DOC(
Flattens the input tensor into a 2D matrix. If input tensor has shape
$(d_0, d_1, ..., d_n)$ then the output will have shape
$\bigl((d_0 * d_1 * ... * d_{(axis-1)}), (d_{axis} * d_{(axis+1)} * ... * d_n)\bigr)$.
Github Links:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/flatten_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"Flatten",
["X"],
["Y"],
axis=1
)
workspace.FeedBlob("X", np.random.rand(1,3,2,2))
print("X:", workspace.FetchBlob("X"))
workspace.RunOperatorOnce(op)
print("Y:", workspace.FetchBlob("Y"))
```
**Result**
```
X: [[[[0.53432311 0.23734561]
[0.56481598 0.52152617]]
[[0.33662627 0.32472711]
[0.17939016 0.97175851]]
[[0.87226421 0.49045439]
[0.92470531 0.30935077]]]]
Y: [[0.53432311 0.23734561 0.56481598 0.52152617 0.33662627 0.32472711
0.17939016 0.97175851 0.87226421 0.49045439 0.92470531 0.30935077]]
```
</details>
)DOC")
.Input(0, "X", "*(type: Tensor)* Input Tensor of rank >= axis.")
.Output(
0,
"Y",
"*(type: Tensor)* A 2D tensor with the contents of the input tensor, "
"with input dimensions up to `axis` flattened to the outer dimension "
"of the output and the remaining input dimensions flattened into the "
"inner dimension of the output.")
.Arg(
"axis",
"*(type: int; default: 1)* Indicates up to which input dimensions "
"(exclusive) should be flattened to the outer dimension of the output.")
.InheritOnnxSchema();
class GetFlattenGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"ResizeLike", "", vector<string>{GO(0), I(0)}, vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(Flatten, GetFlattenGradient);
} // namespace caffe2
|
