diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt
index ed51d416ed9497eee45ba826ad672b8fb1ad3678..f31281ebac0910326e6783a5244d52e6305f2dc5 100644
--- a/paddle/operators/math/CMakeLists.txt
+++ b/paddle/operators/math/CMakeLists.txt
@@ -1,8 +1,8 @@
 
 if(WITH_GPU)
-    nv_library(math_function SRCS math_function.cc math_function.cu DEPS cblas device_context)
+    nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc DEPS cblas device_context)
 else()
-    cc_library(math_function SRCS math_function.cc DEPS cblas device_context)
+    cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context)
 endif()
 
 nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
diff --git a/paddle/operators/math/im2col.cc b/paddle/operators/math/im2col.cc
index dafb21b335f734362ab98f97fafe2fc0778cf0d0..8124e322cbf305b772f15c683c6904c673fcdee0 100644
--- a/paddle/operators/math/im2col.cc
+++ b/paddle/operators/math/im2col.cc
@@ -12,48 +12,54 @@ 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 "Im2Col.h"
+#include "paddle/operators/math/im2col.h"
 
 namespace paddle {
 
 /*
- * imShape = [inputChannels, inputHeight, inputWidth]
- * colShape =
- *   [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth]
+ * im = [input_channels, input_height, input_width]
+ * col =
+ *   [input_channels, filter_height, filter_width, output_height, output_width]
  */
 template <class T>
-class Im2ColFunctor<kCFO, DEVICE_TYPE_CPU, T> {
+class Im2ColFunctor<kCFO, platform::CPUPlace, T> {
  public:
-  void operator()(const T* imData, const TensorShape& imShape, T* colData,
-                  const TensorShape& colShape, int strideHeight,
-                  int strideWidth, int paddingHeight, int paddingWidth) {
-    int inputChannels = imShape[0];
-    int inputHeight = imShape[1];
-    int inputWidth = imShape[2];
-    int filterHeight = colShape[1];
-    int filterWidth = colShape[2];
-    int outputHeight = colShape[3];
-    int outputWidth = colShape[4];
-    int channelsCol = inputChannels * filterHeight * filterWidth;
-
-    for (int c = 0; c < channelsCol; ++c) {
-      int wOffset = c % filterWidth;
-      int hOffset = (c / filterWidth) % filterHeight;
-      int c_im = c / filterWidth / filterHeight;
-      for (int h = 0; h < outputHeight; ++h) {
-        for (int w = 0; w < outputWidth; ++w) {
-          int imRowIdx = h * strideHeight + hOffset;
-          int imColIdx = w * strideWidth + wOffset;
-          if ((imRowIdx - paddingHeight) < 0 ||
-              (imRowIdx - paddingHeight) >= inputHeight ||
-              (imColIdx - paddingWidth) < 0 ||
-              (imColIdx - paddingWidth) >= inputWidth) {
-            colData[(c * outputHeight + h) * outputWidth + w] = T(0);
+  void operator()(const framework::Tensor& im, framework::Tensor& col,
+                  int stride_height, int stride_width, int padding_height,
+                  int padding_width) {
+    PADDLE_ENFORCE(im.dims().size() == 3);
+    PADDLE_ENFORCE(col.dims().size() == 5);
+
+    int input_channels = im.dims()[0];
+    int input_height = im.dims()[1];
+    int input_width = im.dims()[2];
+    int filter_height = col.dims()[1];
+    int filter_width = col.dims()[2];
+    int output_height = col.dims()[3];
+    int output_width = col.dims()[4];
+    int channels_col = input_channels * filter_height * filter_width;
+
+    const T* im_data = im.data<T>();
+    T* col_data = col.data<T>();
+
+    for (int c = 0; c < channels_col; ++c) {
+      int w_offset = c % filter_width;
+      int h_offset = (c / filter_width) % filter_height;
+      int c_im = c / filter_width / filter_height;
+      for (int h = 0; h < output_height; ++h) {
+        for (int w = 0; w < output_width; ++w) {
+          int im_row_idx = h * stride_height + h_offset;
+          int im_col_idx = w * stride_width + w_offset;
+          if ((im_row_idx - padding_height) < 0 ||
+              (im_row_idx - padding_height) >= input_height ||
+              (im_col_idx - padding_width) < 0 ||
+              (im_col_idx - padding_width) >= input_width) {
+            col_data[(c * output_height + h) * output_width + w] = T(0);
           } else {
-            imRowIdx += c_im * inputHeight - paddingHeight;
-            imColIdx -= paddingWidth;
-            colData[(c * outputHeight + h) * outputWidth + w] =
-                imData[imRowIdx * inputWidth + imColIdx];
+            im_row_idx += c_im * input_height - padding_height;
+            im_col_idx -= padding_width;
+            col_data[(c * output_height + h) * output_width + w] =
+                im_data[im_row_idx * input_width + im_col_idx];
           }
         }
       }
@@ -62,41 +68,46 @@ class Im2ColFunctor<kCFO, DEVICE_TYPE_CPU, T> {
 };
 
 /*
- * imShape = [inputChannels, inputHeight, inputWidth]
- * colShape =
- *   [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth]
+ * im = [input_channels, input_height, input_width]
+ * col =
+ *   [input_channels, filter_height, filter_width, output_height, output_width]
  */
 template <class T>
-class Col2ImFunctor<kCFO, DEVICE_TYPE_CPU, T> {
+class Col2ImFunctor<kCFO, platform::CPUPlace, T> {
  public:
-  void operator()(T* imData, const TensorShape& imShape, const T* colData,
-                  const TensorShape& colShape, int strideHeight,
-                  int strideWidth, int paddingHeight, int paddingWidth) {
-    int inputChannels = imShape[0];
-    int inputHeight = imShape[1];
-    int inputWidth = imShape[2];
-    int filterHeight = colShape[1];
-    int filterWidth = colShape[2];
-    int outputHeight = colShape[3];
-    int outputWidth = colShape[4];
-    int channelsCol = inputChannels * filterHeight * filterWidth;
-
-    for (int c = 0; c < channelsCol; ++c) {
-      int wOffset = c % filterWidth;
-      int hOffset = (c / filterWidth) % filterHeight;
-      int c_im = c / filterWidth / filterHeight;
-      for (int h = 0; h < outputHeight; ++h) {
-        for (int w = 0; w < outputWidth; ++w) {
-          int imRowIdx = h * strideHeight + hOffset;
-          int imColIdx = w * strideWidth + wOffset;
-          if ((imRowIdx - paddingHeight) >= 0 &&
-              (imRowIdx - paddingHeight) < inputHeight &&
-              (imColIdx - paddingWidth) >= 0 &&
-              (imColIdx - paddingWidth) < inputWidth) {
-            imRowIdx += c_im * inputHeight - paddingHeight;
-            imColIdx -= paddingWidth;
-            imData[imRowIdx * inputWidth + imColIdx] +=
-                colData[(c * outputHeight + h) * outputWidth + w];
+  void operator()(framework::Tensor& im, const framework::Tensor& col,
+                  int stride_height, int stride_width, int padding_height,
+                  int padding_width) {
+    PADDLE_ENFORCE(im.dims().size() == 3);
+    PADDLE_ENFORCE(col.dims().size() == 5);
+    int input_channels = im.dims()[0];
+    int input_height = im.dims()[1];
+    int input_width = im.dims()[2];
+    int filter_height = col.dims()[1];
+    int filter_width = col.dims()[2];
+    int output_height = col.dims()[3];
+    int output_width = col.dims()[4];
+    int channels_col = input_channels * filter_height * filter_width;
+
+    T* im_data = im.data<T>();
+    const T* col_data = col.data<T>();
+
+    for (int c = 0; c < channels_col; ++c) {
+      int w_offset = c % filter_width;
+      int h_offset = (c / filter_width) % filter_height;
+      int c_im = c / filter_width / filter_height;
+      for (int h = 0; h < output_height; ++h) {
+        for (int w = 0; w < output_width; ++w) {
+          int im_row_idx = h * stride_height + h_offset;
+          int im_col_idx = w * stride_width + w_offset;
+          if ((im_row_idx - padding_height) >= 0 &&
+              (im_row_idx - padding_height) < input_height &&
+              (im_col_idx - padding_width) >= 0 &&
+              (im_col_idx - padding_width) < input_width) {
+            im_row_idx += c_im * input_height - padding_height;
+            im_col_idx -= padding_width;
+            im_data[im_row_idx * input_width + im_col_idx] +=
+                col_data[(c * output_height + h) * output_width + w];
           }
         }
       }
@@ -104,52 +115,61 @@ class Col2ImFunctor<kCFO, DEVICE_TYPE_CPU, T> {
   }
 };
 
-template class Im2ColFunctor<kCFO, DEVICE_TYPE_CPU, float>;
-template class Im2ColFunctor<kCFO, DEVICE_TYPE_CPU, double>;
-template class Col2ImFunctor<kCFO, DEVICE_TYPE_CPU, float>;
-template class Col2ImFunctor<kCFO, DEVICE_TYPE_CPU, double>;
+template class Im2ColFunctor<kCFO, platform::CPUPlace, float>;
+template class Im2ColFunctor<kCFO, platform::CPUPlace, double>;
+template class Col2ImFunctor<kCFO, platform::CPUPlace, float>;
+template class Col2ImFunctor<kCFO, platform::CPUPlace, double>;
 
 /*
- * imShape = [inputChannels, inputHeight, inputWidth]
- * colShape =
- *   [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth]
+ * im = [input_channels, input_height, input_width]
+ * col =
+ *   [output_height, output_width, input_channels, filter_height, filter_width]
  */
 template <class T>
-class Im2ColFunctor<kOCF, DEVICE_TYPE_CPU, T> {
+class Im2ColFunctor<kOCF, platform::CPUPlace, T> {
  public:
-  void operator()(const T* imData, const TensorShape& imShape, T* colData,
-                  const TensorShape& colShape, int strideHeight,
-                  int strideWidth, int paddingHeight, int paddingWidth) {
-    int inputChannels = imShape[0];
-    int inputHeight = imShape[1];
-    int inputWidth = imShape[2];
-    int filterHeight = colShape[3];
-    int filterWidth = colShape[4];
-    int outputHeight = colShape[0];
-    int outputWidth = colShape[1];
-    for (int outputH = 0; outputH < outputHeight; ++outputH) {
-      for (int outputW = 0; outputW < outputWidth; ++outputW) {
-        for (int channel = 0; channel < inputChannels; ++channel) {
-          for (int filterH = 0; filterH < filterHeight; ++filterH) {
-            for (int filterW = 0; filterW < filterWidth; ++filterW) {
-              int imRowOffset =
-                  outputH * strideHeight + filterH - paddingHeight;
-              int imColOffset = outputW * strideWidth + filterW - paddingWidth;
-              int colDataOffset =
-                  (((outputH * outputWidth + outputW) * inputChannels +
-                    channel) *
-                       filterHeight +
-                   filterH) *
-                      filterWidth +
-                  filterW;
-              if (imRowOffset < 0 || imRowOffset >= inputHeight ||
-                  imColOffset < 0 || imColOffset >= inputWidth) {
-                colData[colDataOffset] = float(0);
+  void operator()(const framework::Tensor& im, framework::Tensor& col,
+                  int stride_height, int stride_width, int padding_height,
+                  int padding_width) {
+    PADDLE_ENFORCE(im.dims().size() == 3);
+    PADDLE_ENFORCE(col.dims().size() == 5);
+    int input_channels = im.dims()[0];
+    int input_height = im.dims()[1];
+    int input_width = im.dims()[2];
+    int filter_height = col.dims()[3];
+    int filter_width = col.dims()[4];
+    int output_height = col.dims()[0];
+    int output_width = col.dims()[1];
+
+    const T* im_data = im.data<T>();
+    T* col_data = col.data<T>();
+
+    for (int col_row_idx = 0; col_row_idx < output_height; ++col_row_idx) {
+      for (int col_col_idx = 0; col_col_idx < output_width; ++col_col_idx) {
+        for (int channel = 0; channel < input_channels; ++channel) {
+          for (int filter_row_idx = 0; filter_row_idx < filter_height;
+               ++filter_row_idx) {
+            for (int filter_col_idx = 0; filter_col_idx < filter_width;
+                 ++filter_col_idx) {
+              int im_row_offset =
+                  col_row_idx * stride_height + filter_row_idx - padding_height;
+              int im_col_offset =
+                  col_col_idx * stride_width + filter_col_idx - padding_width;
+              int col_offset = (((col_row_idx * output_width + col_col_idx) *
+                                     input_channels +
+                                 channel) *
+                                    filter_height +
+                                filter_row_idx) *
+                                   filter_width +
+                               filter_col_idx;
+              if (im_row_offset < 0 || im_row_offset >= input_height ||
+                  im_col_offset < 0 || im_col_offset >= input_width) {
+                col_data[col_offset] = T(0);
               } else {
-                int imDataOffset =
-                    (channel * inputHeight + imRowOffset) * inputWidth +
-                    imColOffset;
-                colData[colDataOffset] = imData[imDataOffset];
+                int im_offset =
+                    (channel * input_height + im_row_offset) * input_width +
+                    im_col_offset;
+                col_data[col_offset] = im_data[im_offset];
               }
             }
           }
@@ -160,44 +180,53 @@ class Im2ColFunctor<kOCF, DEVICE_TYPE_CPU, T> {
 };
 
 /*
- * imShape = [inputChannels, inputHeight, inputWidth]
- * colShape =
- *   [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth]
+ * im = [input_channels, input_height, input_width]
+ * col =
+ *   [output_height, output_width, input_channels, filter_height, filter_width]
  */
 template <class T>
-class Col2ImFunctor<kOCF, DEVICE_TYPE_CPU, T> {
+class Col2ImFunctor<kOCF, platform::CPUPlace, T> {
  public:
-  void operator()(T* imData, const TensorShape& imShape, const T* colData,
-                  const TensorShape& colShape, int strideHeight,
-                  int strideWidth, int paddingHeight, int paddingWidth) {
-    int inputChannels = imShape[0];
-    int inputHeight = imShape[1];
-    int inputWidth = imShape[2];
-    int filterHeight = colShape[3];
-    int filterWidth = colShape[4];
-    int outputHeight = colShape[0];
-    int outputWidth = colShape[1];
-    for (int outputH = 0; outputH < outputHeight; ++outputH) {
-      for (int outputW = 0; outputW < outputWidth; ++outputW) {
-        for (int channel = 0; channel < inputChannels; ++channel) {
-          for (int filterH = 0; filterH < filterHeight; ++filterH) {
-            for (int filterW = 0; filterW < filterWidth; ++filterW) {
-              int imRowOffset =
-                  outputH * strideHeight + filterH - paddingHeight;
-              int imColOffset = outputW * strideWidth + filterW - paddingWidth;
-              int colDataOffset =
-                  (((outputH * outputWidth + outputW) * inputChannels +
-                    channel) *
-                       filterHeight +
-                   filterH) *
-                      filterWidth +
-                  filterW;
-              if (imRowOffset >= 0 && imRowOffset < inputHeight &&
-                  imColOffset >= 0 && imColOffset < inputWidth) {
-                int imDataOffset =
-                    (channel * inputHeight + imRowOffset) * inputWidth +
-                    imColOffset;
-                imData[imDataOffset] += colData[colDataOffset];
+  void operator()(framework::Tensor& im, const framework::Tensor& col,
+                  int stride_height, int stride_width, int padding_height,
+                  int padding_width) {
+    PADDLE_ENFORCE(im.dims().size() == 3);
+    PADDLE_ENFORCE(col.dims().size() == 5);
+    int input_channels = im.dims()[0];
+    int input_height = im.dims()[1];
+    int input_width = im.dims()[2];
+    int filter_height = col.dims()[3];
+    int filter_width = col.dims()[4];
+    int output_height = col.dims()[0];
+    int output_width = col.dims()[1];
+
+    T* im_data = im.data<T>();
+    const T* col_data = col.data<T>();
+
+    for (int col_row_idx = 0; col_row_idx < output_height; ++col_row_idx) {
+      for (int col_col_idx = 0; col_col_idx < output_width; ++col_col_idx) {
+        for (int channel = 0; channel < input_channels; ++channel) {
+          for (int filter_row_idx = 0; filter_row_idx < filter_height;
+               ++filter_row_idx) {
+            for (int filter_col_idx = 0; filter_col_idx < filter_width;
+                 ++filter_col_idx) {
+              int im_row_offset =
+                  col_row_idx * stride_height + filter_row_idx - padding_height;
+              int im_col_offset =
+                  col_col_idx * stride_width + filter_col_idx - padding_width;
+              int col_offset = (((col_row_idx * output_width + col_col_idx) *
+                                     input_channels +
+                                 channel) *
+                                    filter_height +
+                                filter_row_idx) *
+                                   filter_width +
+                               filter_col_idx;
+              if (im_row_offset >= 0 && im_row_offset < input_height &&
+                  im_col_offset >= 0 && im_col_offset < input_width) {
+                int im_offset =
+                    (channel * input_height + im_row_offset) * input_width +
+                    im_col_offset;
+                im_data[im_offset] += col_data[col_offset];
               }
             }
           }
@@ -207,9 +236,9 @@ class Col2ImFunctor<kOCF, DEVICE_TYPE_CPU, T> {
   }
 };
 
-template class Im2ColFunctor<kOCF, DEVICE_TYPE_CPU, float>;
-template class Im2ColFunctor<kOCF, DEVICE_TYPE_CPU, double>;
-template class Col2ImFunctor<kOCF, DEVICE_TYPE_CPU, float>;
-template class Col2ImFunctor<kOCF, DEVICE_TYPE_CPU, double>;
+template class Im2ColFunctor<kOCF, platform::CPUPlace, float>;
+template class Im2ColFunctor<kOCF, platform::CPUPlace, double>;
+template class Col2ImFunctor<kOCF, platform::CPUPlace, float>;
+template class Col2ImFunctor<kOCF, platform::CPUPlace, double>;
 
 }  // namespace paddle
diff --git a/paddle/operators/math/im2col.h b/paddle/operators/math/im2col.h
index 4568ca2fd11d91696cea26c22e4cabd022b9e9fc..f2f982b6875553af242c56559c4a0fcd49eb73ba 100644
--- a/paddle/operators/math/im2col.h
+++ b/paddle/operators/math/im2col.h
@@ -14,8 +14,8 @@ limitations under the License. */
 
 #pragma once
 
-#include "TensorShape.h"
-#include "TensorType.h"
+#include "paddle/framework/tensor.h"
+#include "paddle/platform/device_context.h"
 
 namespace paddle {
 
@@ -67,20 +67,20 @@ enum ColFormat { kCFO = 0, kOCF = 1 };
  * \note The caller needs to ensure that imShape.inputChannels is equal to
  *       colShape.inputChannels.
  */
-template <ColFormat Format, DeviceType Device, class T>
+template <ColFormat Format, typename Place, typename T>
 class Im2ColFunctor {
  public:
-  void operator()(const T* imData, const TensorShape& imShape, T* colData,
-                  const TensorShape& colShape, int strideHeight,
-                  int strideWidth, int paddingHeight, int paddingWidth);
+  void operator()(const framework::Tensor& im, framework::Tensor& col,
+                  int stride_height, int stride_width, int padding_height,
+                  int padding_width);
 };
 
-template <ColFormat Format, DeviceType Device, class T>
+template <ColFormat Format, typename Place, typename T>
 class Col2ImFunctor {
  public:
-  void operator()(T* imData, const TensorShape& imShape, const T* colData,
-                  const TensorShape& colShape, int strideHeight,
-                  int strideWidth, int paddingHeight, int paddingWidth);
+  void operator()(framework::Tensor& im, const framework::Tensor& col,
+                  int stride_height, int stride_width, int padding_height,
+                  int padding_width);
 };
 
 }  // namespace paddle