Support static graph code-gen for unpool (#52947)

paddle/fluid/operators/unpool_op.cc

@@ -24,64 +24,6 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-class Unpool2dOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput(
-        "X",
-        "(Tensor) The input tensor of unpool operator. "
-        "The format of input tensor is NCHW. Where N is batch size, C is the "
-        "number of channels, H and W is the height and width of feature.");
-    AddInput(
-        "Indices",
-        "(Tensor) The input tensor of the indices given out by MaxPool2d. "
-        "The format of input tensor is NCHW. Where N is batch size, C is the "
-        "number of channels, H and W is the height and width of feature.");
-    AddOutput("Out",
-              "(Tensor) The output tensor of unpool operator."
-              "The format of output tensor is also NCHW."
-              "Where N is batch size, C is "
-              "the number of channels, H and W is the height and "
-              "width of feature.");
-    AddAttr<std::vector<int>>(
-        "ksize",
-        "(vector), the unpooling window size(height, width) "
-        "of unpooling operator.");
-    AddAttr<std::vector<int>>("strides",
-                              "(vector, default:{1, 1}), "
-                              "strides (height, width) of unpooling operator.")
-        .SetDefault({1, 1});
-    AddAttr<std::vector<int>>("paddings",
-                              "(vector default:{0,0}), "
-                              "paddings (height, width) of unpooling operator.")
-        .SetDefault({0, 0});
-    AddAttr<std::string>(
-        "unpooling_type",
-        "(string), unpooling type, can be \"max\" for max-unpooling ")
-        .InEnum({"max"});
-    AddAttr<std::vector<int>>("output_size",
-                              "(vector, optional). The shape of output.")
-        .SetDefault({0, 0})
-        .SupportTensor();
-    AddAttr<std::string>(
-        "data_format",
-        "(string, default NCHW) Only used in "
-        "An optional string from: \"NHWC\", \"NCHW\". "
-        "Defaults to \"NHWC\". Specify the data format of the output data, "
-        "the input will be transformed automatically. ")
-        .SetDefault("NCHW");
-    AddComment(R"DOC(
-Input shape is: $(N, C_{in}, H_{in}, W_{in})$, Output shape is:
-$(N, C_{out}, H_{out}, W_{out})$, where
-$$
-H_{out} = (H_{in}-1) * strides[0] - 2 * paddings[0] + ksize[0] \\
-W_{out} = (W_{in}-1) * strides[1] - 2 * paddings[1] + ksize[1]
-$$
-Paper: http://www.matthewzeiler.com/wp-content/uploads/2017/07/iccv2011.pdf
-)DOC");
-  }
-};
-
 class Unpool3dOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
@@ -200,18 +142,6 @@ class Unpool3dOpGradMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-class UnpoolOpGrad : public framework::OperatorWithKernel {
- protected:
-  phi::KernelKey GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
-    return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(ctx, "X"),
-                          ctx.GetPlace());
-  }
-
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-};
-
 class Unpool3dOpGrad : public framework::OperatorWithKernel {
  protected:
   phi::KernelKey GetExpectedKernelType(
@@ -228,21 +158,6 @@ class Unpool3dOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-DECLARE_INFER_SHAPE_FUNCTOR(unpool,
-                            UnpoolInferShapeFunctor,
-                            PD_INFER_META(phi::UnpoolInferMeta));
-REGISTER_OPERATOR(unpool,
-                  ops::UnpoolOp,
-                  ops::Unpool2dOpMaker,
-                  ops::UnpoolOpGradMaker<paddle::framework::OpDesc>,
-                  ops::UnpoolOpGradMaker<paddle::imperative::OpBase>,
-                  UnpoolInferShapeFunctor);
-
-DECLARE_INFER_SHAPE_FUNCTOR(unpool_grad,
-                            UnpoolGradInferShapeFunctor,
-                            PD_INFER_META(phi::UnchangedInferMeta));
-
-REGISTER_OPERATOR(unpool_grad, ops::UnpoolOpGrad, UnpoolGradInferShapeFunctor);
 
 DECLARE_INFER_SHAPE_FUNCTOR(unpool,
                             Unpool3dInferShapeFunctor,

paddle/phi/api/yaml/backward.yaml

@@ -2064,3 +2064,14 @@
   kernel :
     func : where_grad
   no_need_buffer : x, y
+
+- backward_op: unpool_grad
+  forward: unpool (Tensor x, Tensor indices, int[] ksize, int[] strides = {1,1}, int[] paddings ={0,0} ,IntArray output_size = {0,0}, str data_format="NCHW") -> Tensor(out)
+  args: (Tensor x, Tensor indices, Tensor out, Tensor out_grad, int[] ksize, int[] strides, int[] paddings, IntArray output_size, str data_format)
+  output: Tensor(x_grad)
+  infer_meta:
+    func: UnchangedInferMeta
+    param : [x]
+  kernel:
+    func: unpool_grad
+    data_type: x

paddle/phi/api/yaml/legacy_backward.yaml

@@ -1053,14 +1053,3 @@
   kernel:
     func: unpool3d_grad
     data_type: x
-
-- backward_op: unpool_grad
-  forward: unpool (Tensor x, Tensor indices, int[] ksize, int[] strides, int[] padding,  IntArray output_size, str data_format) -> Tensor(out)
-  args: (Tensor x, Tensor indices, Tensor out, Tensor out_grad, int[] ksize, int[] strides, int[] padding, IntArray output_size, str data_format)
-  output: Tensor(x_grad)
-  infer_meta:
-    func: UnchangedInferMeta
-    param : [x]
-  kernel:
-    func: unpool_grad
-    data_type: x

paddle/phi/api/yaml/legacy_ops.yaml

@@ -1217,16 +1217,6 @@
     func : unique
     data_type : x
 
-- op : unpool
-  args: (Tensor x, Tensor indices, int[] ksize, int[] strides, int[] padding, IntArray output_size, str data_format)
-  output: Tensor(out)
-  infer_meta:
-    func: UnpoolInferMeta
-  kernel:
-    func: unpool
-    data_type: x
-  backward: unpool_grad
-
 - op : unpool3d
   args: (Tensor x, Tensor indices, int[] ksize, int[] strides, int[] padding, int[] output_size, str data_format)
   output: Tensor(out)

paddle/phi/api/yaml/op_compat.yaml

@@ -2411,6 +2411,16 @@
   outputs :
     {out : Out, index : Index, counts : Counts}
 
+- op : unpool
+  inputs :
+    {x : X, indices: Indices}
+  outputs :
+    out : Out
+  int_array :
+    output_size:
+      data_type : int
+      support_tensor : true
+
 - op : unsqueeze (unsqueeze2)
   backward : unsqueeze_grad (unsqueeze2_grad), unsqueeze_double_grad(unsqueeze2_double_grad)
   inputs :

paddle/phi/api/yaml/ops.yaml

@@ -2133,6 +2133,16 @@
     data_type : x
   optional : index, counts
 
+- op : unpool
+  args: (Tensor x, Tensor indices, int[] ksize, int[] strides = {1,1}, int[] paddings ={0,0} ,IntArray output_size = {0,0}, str data_format="NCHW")
+  output: Tensor(out)
+  infer_meta:
+    func: UnpoolInferMeta
+  kernel:
+    func: unpool
+    data_type: x
+  backward: unpool_grad
+
 - op : unsqueeze
   args : (Tensor x, IntArray axis = {})
   output : Tensor(out), Tensor(xshape)