diff --git a/paddle/operators/crop_op.cc b/paddle/operators/crop_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..75fa42fc18b1bde7731584e830dcbe3f7393c6f9
--- /dev/null
+++ b/paddle/operators/crop_op.cc
@@ -0,0 +1,81 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/operators/crop_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class CropOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    auto dim0 = ctx.Input<Tensor>("X")->dims();
+    auto Y = ctx.Input<Tensor>("Y");
+    if (Y == nullptr) {
+      auto shape = GetAttr<std::vector<int>>("shape");
+      PADDLE_ENFORCE_EQ(
+          shape.size(), dim0.size(),
+          "Shape size should be equal to dimention size of input tensor.");
+      ctx.Output<Tensor>("Out")->Resize(paddle::framework::make_ddim(shape));
+    } else {
+      ctx.Output<Tensor>("Out")->Resize(Y->dims());
+    }
+  }
+};
+
+class CropOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  CropOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The input of crop op");
+    AddInput("Y", "The input used as reference for cropping. ");
+    AddOutput("Out", "The output of crop op.");
+    AddComment(R"DOC(
+Crop Operator.
+)DOC");
+    AddAttr<std::vector<int>>("offsets", "The offsets for cropping.");
+    AddAttr<std::vector<int>>("shape", "The shape for cropping.");
+  }
+};
+
+class CropOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+    auto x_dims = ctx.Input<Tensor>("X")->dims();
+    auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+
+    x_grad->Resize(x_dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(crop, ops::CropOp, ops::CropOpMaker, crop_grad, ops::CropOpGrad);
+REGISTER_OP_CPU_KERNEL(crop,
+                       ops::CropKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(crop_grad,
+                       ops::CropGradKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/crop_op.cu b/paddle/operators/crop_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..5afed4946598b89c6a72fb5e9a921bc2b1b631da
--- /dev/null
+++ b/paddle/operators/crop_op.cu
@@ -0,0 +1,22 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/crop_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(crop,
+                       ops::CropKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(crop_grad,
+                       ops::CropGradKernel<paddle::platform::GPUPlace, float>);
diff --git a/paddle/operators/crop_op.h b/paddle/operators/crop_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..40e05869ddde1652b590455f15b41656e8604999
--- /dev/null
+++ b/paddle/operators/crop_op.h
@@ -0,0 +1,138 @@
+/* Copyright (c) 2016 CropdleCropdle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#pragma once
+
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+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;
+
+template <typename Place, typename T, size_t D>
+void CropFunction(const framework::ExecutionContext& context) {
+  auto* x = context.Input<Tensor>("X");
+  auto* out = context.Output<Tensor>("Out");
+  out->mutable_data<T>(context.GetPlace());
+  auto x_dims = x->dims();
+  auto out_dims = out->dims();
+
+  auto offsets = context.op().GetAttr<std::vector<int>>("offsets");
+  PADDLE_ENFORCE_EQ(
+      x_dims.size(), offsets.size(),
+      "Offsets size should be equal to dimension size of input tensor.");
+
+  Eigen::array<std::pair<int, int>, D> paddings;
+  for (size_t i = 0; i < D; ++i) {
+    paddings[i].first = -(offsets[i]);
+    paddings[i].second = -(x_dims[i] - out_dims[i] - offsets[i]);
+  }
+
+  auto x_tensor = EigenTensor<T, D>::From(*x);
+  auto out_tensor = EigenTensor<T, D>::From(*out);
+  auto place = context.GetEigenDevice<Place>();
+  out_tensor.device(place) = x_tensor.pad(paddings, 0);
+}
+
+template <typename Place, typename T>
+class CropKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    int dim = context.Input<Tensor>("X")->dims().size();
+    switch (dim) {
+      case 1:
+        CropFunction<Place, T, 1>(context);
+        break;
+      case 2:
+        CropFunction<Place, T, 2>(context);
+        break;
+      case 3:
+        CropFunction<Place, T, 3>(context);
+        break;
+      case 4:
+        CropFunction<Place, T, 4>(context);
+        break;
+      case 5:
+        CropFunction<Place, T, 5>(context);
+        break;
+      case 6:
+        CropFunction<Place, T, 6>(context);
+        break;
+      default:
+        LOG(ERROR) << "Only ranks up to 6 supported.";
+    }
+  }
+};
+
+template <typename Place, typename T, size_t D>
+void CropGradFunction(const framework::ExecutionContext& context) {
+  auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
+  auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
+  d_x->mutable_data<T>(context.GetPlace());
+  auto d_x_dims = d_x->dims();
+  auto d_out_dims = d_out->dims();
+
+  auto offsets = context.op().GetAttr<std::vector<int>>("offsets");
+
+  Eigen::array<std::pair<int, int>, D> paddings;
+  for (int i = 0; i < d_out_dims.size(); ++i) {
+    paddings[i].first = offsets[i];
+    paddings[i].second = d_x_dims[i] - d_out_dims[i] - offsets[i];
+  }
+
+  auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
+  auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
+  auto place = context.GetEigenDevice<Place>();
+  d_x_tensor.device(place) = d_out_tensor.pad(paddings, 0);
+}
+
+template <typename Place, typename T>
+class CropGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    size_t dim =
+        context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
+    switch (dim) {
+      case 1:
+        CropGradFunction<Place, T, 1>(context);
+        break;
+      case 2:
+        CropGradFunction<Place, T, 2>(context);
+        break;
+      case 3:
+        CropGradFunction<Place, T, 3>(context);
+        break;
+      case 4:
+        CropGradFunction<Place, T, 4>(context);
+        break;
+      case 5:
+        CropGradFunction<Place, T, 5>(context);
+        break;
+      case 6:
+        CropGradFunction<Place, T, 6>(context);
+        break;
+      default:
+        LOG(ERROR) << "Only ranks up to 6 supported.";
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 6896422617be0a3c73dc7b0d7cc1113075fa2f4b..e2ea5c92af81ae26415c92e2cc9e89f3418c900f 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -48,6 +48,7 @@ USE_NO_KERNEL_OP(identity);
 USE_OP(minus);
 USE_CPU_ONLY_OP(gather);
 USE_CPU_ONLY_OP(scatter);
+USE_OP(crop);
 
 namespace paddle {
 namespace framework {
diff --git a/python/paddle/v2/framework/tests/test_crop_op.py b/python/paddle/v2/framework/tests/test_crop_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..27d8332acfa83b0392aafddf3b5cdff8647884d9
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_crop_op.py
@@ -0,0 +1,35 @@
+import unittest
+import numpy as np
+from paddle.v2.framework.op import Operator
+from gradient_checker import GradientChecker
+from op_test_util import OpTestMeta
+
+
+class TestCropOp(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        self.type = "crop"
+        self.inputs = {'X': np.random.random((16, 16)).astype("float32"), }
+        self.attrs = {}
+        self.attrs['offsets'] = [2, 3]
+        self.attrs['shape'] = [8, 8]
+        self.outputs = {'Out': self.inputs['X'][2:10, 3:11]}
+
+
+class TestCropGradOp(GradientChecker):
+    def setUp(self):
+        self.op = Operator(
+            type="crop", X="X", Out="Out", offsets=[2, 3], shape=[8, 8])
+        self.inputs = {'X': np.random.random((16, 16)).astype("float32"), }
+
+    def test_normal(self):
+        self.check_grad(
+            self.op, self.inputs, set(["X"]), "Out", max_relative_error=0.5)
+
+    def test_cpu_gpu_compare(self):
+        self.compare_grad(self.op, self.inputs)
+
+
+if __name__ == '__main__':
+    unittest.main()