rename unpool2d to unpool, test=develop

Author: tink2123

paddle/fluid/operators/unpool_op.cc

@@ -167,15 +167,15 @@ class UnpoolOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(unpool2d, ops::UnpoolOp, ops::Unpool2dOpMaker,
+REGISTER_OPERATOR(unpool, ops::UnpoolOp, ops::Unpool2dOpMaker,
                   ops::UnpoolOpGradMaker<paddle::framework::OpDesc>,
                   ops::UnpoolOpGradMaker<paddle::imperative::OpBase>);
 
-REGISTER_OPERATOR(unpool2d_grad, ops::UnpoolOpGrad);
+REGISTER_OPERATOR(unpool_grad, ops::UnpoolOpGrad);
 REGISTER_OP_CPU_KERNEL(
-    unpool2d, ops::UnpoolKernel<paddle::platform::CPUDeviceContext, float>,
+    unpool, ops::UnpoolKernel<paddle::platform::CPUDeviceContext, float>,
     ops::UnpoolKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
-    unpool2d_grad,
+    unpool_grad,
     ops::UnpoolGradKernel<paddle::platform::CPUDeviceContext, float>,
     ops::UnpoolGradKernel<paddle::platform::CPUDeviceContext, double>);

python/paddle/fluid/tests/unittests/test_unpool_op.py

@@ -55,7 +55,7 @@ def unpool2dmax_forward_naive(input, indices, ksize, strides, paddings,
 
 class TestUnpoolOp(OpTest):
     def setUp(self):
-        self.op_type = "unpool2d"
+        self.op_type = "unpool"
         self.init_test_case()
         input = np.random.randint(0, 100, self.shape)
         nsize, csize, hsize, wsize = input.shape

python/paddle/nn/functional/pooling.py

@@ -743,13 +743,13 @@ def max_unpool2d(x,
                                       output_size)
 
     if in_dygraph_mode():
-        output = _C_ops.unpool2d(x, indices, 'unpooling_type', 'max', 'ksize',
-                                 kernel_size, 'strides', stride, 'paddings',
-                                 padding, "output_size", output_size,
-                                 "data_format", data_format)
+        output = _C_ops.unpool(x, indices, 'unpooling_type', 'max', 'ksize',
+                               kernel_size, 'strides', stride, 'paddings',
+                               padding, "output_size", output_size,
+                               "data_format", data_format)
         return output
 
-    op_type = "unpool2d"
+    op_type = "unpool"
     helper = LayerHelper(op_type, **locals())
     dtype = helper.input_dtype(input_param_name="x")
     unpool_out = helper.create_variable_for_type_inference(dtype)