fix bug of unpool in the module 'enet' of paddleseg (#53840)

paddle/phi/api/yaml/backward.yaml

@@ -2173,14 +2173,3 @@
   kernel:
     func: unpool3d_grad
     data_type: x
-
-- 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

@@ -1019,3 +1019,14 @@
     param : [out_grad]
   kernel :
     func : triu_grad
+
+- 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

@@ -1177,6 +1177,16 @@
     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 : zeros
   args : (IntArray shape, DataType dtype=DataType::FLOAT32, Place place=CPUPlace())
   output : Tensor(out)

paddle/phi/api/yaml/ops.yaml

@@ -2213,16 +2213,6 @@
     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 : unpool3d
   args: (Tensor x, Tensor indices, int[] ksize, int[] strides={1,1,1}, int[] paddings={0,0,0}, int[] output_size={0,0,0}, str data_format="NCDHW")
   output: Tensor(out)

paddle/phi/api/yaml/static_backward.yaml

@@ -64,3 +64,14 @@
   kernel :
     func : softmax_grad
   composite : softmax_grad(out, out_grad, axis, x_grad)
+
+- backward_op: unpool_grad
+  forward: unpool (Tensor x, Tensor indices, int[] ksize, str unpooling_type, 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/static_ops.yaml

@@ -398,3 +398,15 @@
   kernel :
     func : unique
     data_type : x
+
+- op : unpool
+  args: (Tensor x, Tensor indices, int[] ksize, str unpooling_type, int[] strides = {1,1}, int[] paddings ={0,0} ,IntArray output_size = {0,0}, str data_format="NCHW")
+  output: Tensor(out)
+  infer_meta:
+    func: UnpoolInferMeta
+    param : [x, indices, ksize, strides, paddings,output_size, data_format]
+  kernel:
+    func: unpool
+    data_type: x
+    param : [x, indices, ksize, strides, paddings,output_size, data_format]
+  backward: unpool_grad