diff --git a/paddle/operators/crop_op.cc b/paddle/operators/crop_op.cc
index 09fa13dfbbf4f850f69126aa09849421e671d29e..33fa9b792879b0dab1f4c5efb4512da950ca4496 100644
--- a/paddle/operators/crop_op.cc
+++ b/paddle/operators/crop_op.cc
@@ -29,6 +29,10 @@ class CropOp : public framework::OperatorWithKernel {
   void InferShape(const framework::InferShapeContext &ctx) const override {
     auto x_dim = ctx.Input<LoDTensor>("X")->dims();
     auto Y = ctx.Input<LoDTensor>("Y");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
+                            "Input(X) of CropOp should not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
+                            "Output(Out) of CropOp should not be null.");
     if (Y == nullptr) {
       auto shape = Attr<std::vector<int>>("shape");
       PADDLE_ENFORCE_EQ(
@@ -40,6 +44,9 @@ class CropOp : public framework::OperatorWithKernel {
       }
       ctx.Output<LoDTensor>("Out")->Resize(framework::make_ddim(tensor_shape));
     } else {
+      PADDLE_ENFORCE_EQ(framework::arity(x_dim), framework::arity(Y->dims()),
+                        "Tensor rank of both CropOp's "
+                        "inputs must be same.");
       ctx.Output<LoDTensor>("Out")->Resize(Y->dims());
     }
   }
diff --git a/paddle/operators/crop_op.cu b/paddle/operators/crop_op.cu
index 1715b2eaf9ccb4363311219e0245f965fb107b8e..561dbe48039e444429e0593f37cfd82bddce6471 100644
--- a/paddle/operators/crop_op.cu
+++ b/paddle/operators/crop_op.cu
@@ -19,8 +19,7 @@
 namespace paddle {
 namespace operators {
 
-using Tensor = framework::Tensor;
-using LoDTensor = framework::LoDTensor;
+using framework::LoDTensor;
 
 template <typename T, int D>
 __global__ void CropKernel(const int N, const int64_t* out_shape,
diff --git a/paddle/operators/crop_op.h b/paddle/operators/crop_op.h
index 7f041737a7e87d76fc6208bb2e05e881b45badab..09d42f4b7ee44091b0e41f608e3db96281e740ba 100644
--- a/paddle/operators/crop_op.h
+++ b/paddle/operators/crop_op.h
@@ -24,8 +24,7 @@ template <typename T, size_t D, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
 
-using Tensor = framework::Tensor;
-using LoDTensor = framework::LoDTensor;
+using framework::LoDTensor;
 
 template <typename Place, typename T, size_t D>
 void CropGradFunction(const framework::ExecutionContext& context) {
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index a7a38339fb2c8689778b0a86d3713f67e1447a80..c7009a604f60cda11434ad33b6c7d7caee1befdd 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -19,6 +19,7 @@ limitations under the License. */
 #include "paddle/framework/backward.h"
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/cond_op.h"
 #include "paddle/operators/net_op.h"
 #include "paddle/operators/recurrent_op.h"
 #include "paddle/platform/enforce.h"
@@ -288,6 +289,28 @@ All parameter, weight, gradient are variables in Paddle.
            [](operators::RecurrentOp &self, const operators::NetOp &net)
                -> void { self.set_stepnet(net.Clone()); });
 
+  // cond_op
+  py::class_<operators::CondOp, OperatorBase>(m, "CondOp")
+      .def_static("create",
+                  [](py::bytes protobin) -> operators::CondOp * {
+                    OpDesc desc;
+                    PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
+                                   "Cannot parse user input to OpDesc");
+                    PADDLE_ENFORCE(desc.IsInitialized(),
+                                   "User OpDesc is not initialized, reason %s",
+                                   desc.InitializationErrorString());
+                    auto cond_op = OpRegistry::CreateOp(desc);
+                    return static_cast<operators::CondOp *>(cond_op.release());
+                  })
+      .def("set_truenet",
+           [](operators::CondOp &self, const operators::NetOp &net) -> void {
+             self.set_truenet(net.Clone());
+           })
+      .def("set_falsenet",
+           [](operators::CondOp &self, const operators::NetOp &net) -> void {
+             self.set_falsenet(net.Clone());
+           });
+
   m.def("unique_integer", UniqueIntegerGenerator);
 
   m.def("is_compile_gpu", IsCompileGPU);