diff --git a/paddle/operators/conv_cudnn_op.cc b/paddle/operators/conv_cudnn_op.cc
index 97f31bf22d7072d89bd043045045dcb5bb5518b8..4c65b60d2349d2989128f4b1da705ea18391b8a3 100644
--- a/paddle/operators/conv_cudnn_op.cc
+++ b/paddle/operators/conv_cudnn_op.cc
@@ -22,8 +22,6 @@ class CudnnConvOpMaker : public Conv2DOpMaker {
   CudnnConvOpMaker(framework::OpProto* proto,
                    framework::OpAttrChecker* op_checker)
       : Conv2DOpMaker(proto, op_checker) {
-    AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
-        .SetDefault(std::vector<int>{1, 1});
     AddAttr<int>("workspace_size_MB",
                  "workspace size for cudnn, in MB, "
                  "workspace is a section of GPU memory which will be "
diff --git a/paddle/operators/conv_op.cc b/paddle/operators/conv_op.cc
index a6f65f10165929316f971d195f3790fd9e7ed376..852ac2ae37ca5d59215415787a80df95305bd4c0 100644
--- a/paddle/operators/conv_op.cc
+++ b/paddle/operators/conv_op.cc
@@ -30,6 +30,7 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
   std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
   std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
   int groups = ctx->Attrs().Get<int>("groups");
+  std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
   int input_channels = in_dims[1];
   int output_channels = filter_dims[0];
 
@@ -54,7 +55,8 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
   std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
   for (size_t i = 0; i < paddings.size(); ++i) {
     output_shape.push_back(OutputSize(in_dims[i + 2], filter_dims[i + 2],
-                                      paddings[i], strides[i]));
+                                      dilations[i], paddings[i], paddings[i],
+                                      strides[i]));
   }
   ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
 }
@@ -90,6 +92,10 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
       "first half of the input channels, while the second half of the filters "
       "is only connected to the second half of the input channels.")
       .SetDefault(1);
+  AddAttr<std::vector<int>>("dilations",
+                            "(vector default:{1, 1}), the dilations of "
+                            "convolution operator.")
+      .SetDefault(std::vector<int>{1, 1});
   AddComment(R"DOC(
 Convolution Operator.
 
@@ -151,6 +157,11 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
       "first half of the input channels, while the second half of the filters "
       "is only connected to the second half of the input channels.")
       .SetDefault(1);
+  AddAttr<std::vector<int>>("dilations",
+                            "(vector default:{1, 1, 1}), the dilations of "
+                            "convolution operator. Currently, conv3d doesn't "
+                            "support dilation.")
+      .SetDefault(std::vector<int>{1, 1, 1});
 
   AddComment(R"DOC(
 Convolution3D Operator.
diff --git a/paddle/operators/conv_op.h b/paddle/operators/conv_op.h
index 7c1729213bf3f5f3987afbf2d51d5b5339ae521d..2459f03a1a92511a4dedcccb170174ede1671a08 100644
--- a/paddle/operators/conv_op.h
+++ b/paddle/operators/conv_op.h
@@ -27,9 +27,12 @@ using Tensor = framework::Tensor;
 
 // Base convolution operator definations for other conv
 // like operators to reuse the implementation.
-inline int OutputSize(int input_size, int filter_size, int padding,
-                      int stride) {
-  int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
+inline int OutputSize(int input_size, int filter_size, int dilation,
+                      int padding_up, int padding_down, int stride) {
+  int output_size = (input_size + padding_up + padding_down -
+                     (dilation * (filter_size - 1) + 1)) /
+                        stride +
+                    1;
   return output_size;
 }
 
@@ -76,6 +79,7 @@ class GemmConvKernel : public framework::OpKernel<T> {
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
     int groups = context.Attr<int>("groups");
+    std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
 
     const int batch_size = static_cast<int>(input->dims()[0]);
 
@@ -139,9 +143,9 @@ class GemmConvKernel : public framework::OpKernel<T> {
         if (filter_shape_vec.size() == 2) {
           // im2col
           math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
-          im2col(context.device_context(), in_slice, col, strides[0],
-                 strides[1], paddings[0], paddings[0], paddings[1],
-                 paddings[1]);
+          im2col(context.device_context(), in_slice, col, dilations[0],
+                 dilations[1], strides[0], strides[1], paddings[0], paddings[0],
+                 paddings[1], paddings[1]);
         } else if (filter_shape_vec.size() == 3) {
           // vol2col
           math::Vol2ColFunctor<Place, T> vol2col;
@@ -181,6 +185,7 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
     int groups = context.Attr<int>("groups");
+    std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
 
     const int batch_size = static_cast<int>(input->dims()[0]);
 
@@ -263,9 +268,9 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
 
           if (filter_shape_vec.size() == 2) {
             math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
-            col2im(context.device_context(), in_grad_slice, col, strides[0],
-                   strides[1], paddings[0], paddings[0], paddings[1],
-                   paddings[1]);
+            col2im(context.device_context(), in_grad_slice, col, dilations[0],
+                   dilations[1], strides[0], strides[1], paddings[0],
+                   paddings[0], paddings[1], paddings[1]);
 
           } else if (filter_shape_vec.size() == 3) {
             math::Col2VolFunctor<Place, T> col2vol;
@@ -295,9 +300,9 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
 
           if (filter_shape_vec.size() == 2) {
             math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
-            im2col(context.device_context(), in_slice, col, strides[0],
-                   strides[1], paddings[0], paddings[0], paddings[1],
-                   paddings[1]);
+            im2col(context.device_context(), in_slice, col, dilations[0],
+                   dilations[1], strides[0], strides[1], paddings[0],
+                   paddings[0], paddings[1], paddings[1]);
           } else if (filter_shape_vec.size() == 3) {
             math::Vol2ColFunctor<Place, T> vol2col;
             vol2col(context.device_context(), in_slice, col, strides[0],
diff --git a/paddle/operators/conv_transpose_op.h b/paddle/operators/conv_transpose_op.h
index 6c1a6220d784abf89ec789f94d9cff9e5414db04..cbfad88b3982af13601a92ca5366ea5eaf026854 100644
--- a/paddle/operators/conv_transpose_op.h
+++ b/paddle/operators/conv_transpose_op.h
@@ -69,6 +69,9 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
     // TODO(Zhuoyuan): Paddings can be added in future.
     // groups will alway be disabled in conv2dtranspose.
 
+    int dilation_h = 1;
+    int dilation_w = 1;
+
     const int batch_size = static_cast<int>(input->dims()[0]);
 
     // input_shape_vec: {h, w} or {d, h, w}
@@ -140,8 +143,8 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
         // from (c * k_h * k_w, h * w) to (c, o_h, o_w)
         math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
 
-        col2im(context.device_context(), output_batch, col, strides[0],
-               strides[1], 0, 0, 0, 0);
+        col2im(context.device_context(), output_batch, col, dilation_h,
+               dilation_w, strides[0], strides[1], 0, 0, 0, 0);
       } else if (filter_shape_vec.size() == 3) {
         // col2vol: col_matrix -> dy
         // from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
@@ -174,6 +177,9 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
     // Actually, no paddings and groups allowed in conv transpose.
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
 
+    int dilation_h = 1;
+    int dilation_w = 1;
+
     const int batch_size = static_cast<int>(input->dims()[0]);
 
     // input_shape_vec: {h, w} or {d, h, w}
@@ -248,9 +254,9 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
           // im2col: dy -> col matrix
           // from (c, o_h, o_w) to (c * k_h * k_w, h * w)
           math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
-          im2col(context.device_context(), output_grad_batch, col, strides[0],
-                 strides[1], paddings[0], paddings[0], paddings[1],
-                 paddings[1]);
+          im2col(context.device_context(), output_grad_batch, col, dilation_h,
+                 dilation_w, strides[0], strides[1], paddings[0], paddings[0],
+                 paddings[1], paddings[1]);
         } else if (filter_shape_vec.size() == 3) {
           // vol2col: dy -> col_matrix
           // from (c, o_d, o_h, o_w) to (c * k_d * k_h * k_w, d * h * w)
diff --git a/paddle/operators/math/context_project.h b/paddle/operators/math/context_project.h
index e0283360414fbdfb3dae2e94b45c9c8daeed3c74..c67d84528fdd397d0ef29bb85a335c77eb7cb65f 100644
--- a/paddle/operators/math/context_project.h
+++ b/paddle/operators/math/context_project.h
@@ -95,6 +95,9 @@ class ContextProjectFunctor {
 
     math::Im2ColFunctor<math::ColFormat::kOCF, Place, float> im2col_ocf;
 
+    int dilation_h = 1;
+    int dilation_w = 1;
+
     int input_row_begin, input_row_end;
     int sequence_height, sequence_width;
     sequence_width = in.dims()[1];
@@ -124,7 +127,7 @@ class ContextProjectFunctor {
              sequence_width});  // input_channels, input_height, input_width
         in_t.Resize(framework::make_ddim(input_shape));
 
-        im2col_ocf(context, in_t, out_t,
+        im2col_ocf(context, in_t, out_t, dilation_h, dilation_w,
                    /*stride_height*/ context_stride, /*stride_width*/ 1, up_pad,
                    down_pad, 0, 0);
         out_t.Resize({sequence_height, context_length * sequence_width});
@@ -204,6 +207,9 @@ class ContextProjectGradFunctor {
 
     math::Col2ImFunctor<math::ColFormat::kOCF, Place, float> col2im_ocf;
 
+    int dilation_h = 1;
+    int dilation_w = 1;
+
     int input_row_begin, input_row_end;
     int sequence_height, sequence_width;
     sequence_width = in.dims()[1];
@@ -234,7 +240,7 @@ class ContextProjectGradFunctor {
                sequence_width});  // input_channels, input_height, input_width
           in_t.Resize(framework::make_ddim(input_shape));
 
-          col2im_ocf(context, in_t, out_t,
+          col2im_ocf(context, in_t, out_t, dilation_h, dilation_w,
                      /*stride_height*/ context_stride, /*stride_width*/ 1,
                      up_pad, down_pad, 0, 0);
           out_t.Resize({sequence_height, context_length * sequence_width});
diff --git a/paddle/operators/math/im2col.cc b/paddle/operators/math/im2col.cc
index 3b1b0bd71dd3768b932864e185af8dc839b4653e..b248863b4e96a90ce43cc36c78957d56bd2f9e63 100644
--- a/paddle/operators/math/im2col.cc
+++ b/paddle/operators/math/im2col.cc
@@ -29,35 +29,36 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
  public:
   void operator()(const platform::DeviceContext& context,
                   const framework::Tensor& im, framework::Tensor& col,
-                  int stride_height, int stride_width, int padding_up,
-                  int padding_down, int padding_left, int padding_right) {
+                  int dilation_h, int dilation_w, int stride_height,
+                  int stride_width, int padding_up, int padding_down,
+                  int padding_left, int padding_right) {
     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 im_channels = im.dims()[0];
+    int im_height = im.dims()[1];
+    int im_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 col_height = col.dims()[3];
+    int col_width = col.dims()[4];
 
-    PADDLE_ENFORCE_EQ(
-        (input_height + padding_up + padding_down - filter_height) /
-                stride_height +
-            1,
-        output_height,
-        "Output_height and padding(padding_up, padding_down) are "
-        "inconsistent.");
-    PADDLE_ENFORCE_EQ(
-        (input_width + padding_left + padding_right - filter_width) /
-                stride_width +
-            1,
-        output_width,
-        "output_width and padding(padding_left, padding_right) are "
-        "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_height + padding_up + padding_down -
+                       ((dilation_h * (filter_height - 1) + 1))) /
+                              stride_height +
+                          1,
+                      col_height,
+                      "Output_height and padding(padding_up, padding_down) are "
+                      "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_width + padding_left + padding_right -
+                       ((dilation_w * (filter_width - 1) + 1))) /
+                              stride_width +
+                          1,
+                      col_width,
+                      "col_width and padding(padding_left, padding_right) are "
+                      "inconsistent.");
 
-    int channels_col = input_channels * filter_height * filter_width;
+    int channels_col = im_channels * filter_height * filter_width;
 
     const T* im_data = im.data<T>();
     T* col_data = col.data<T>();
@@ -66,19 +67,19 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
       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 - padding_up;
-          int im_col_idx = w * stride_width + w_offset - padding_left;
+      for (int h = 0; h < col_height; ++h) {
+        for (int w = 0; w < col_width; ++w) {
+          int im_row_idx =
+              h * stride_height - padding_up + h_offset * dilation_h;
+          int im_col_idx =
+              w * stride_width - padding_left + w_offset * dilation_w;
 
-          if (im_row_idx < 0 || im_row_idx >= input_height || im_col_idx < 0 ||
-              im_col_idx >= input_width) {
-            col_data[(c * output_height + h) * output_width + w] = T(0);
-          } else {
-            im_row_idx += c_im * input_height;
-            col_data[(c * output_height + h) * output_width + w] =
-                im_data[im_row_idx * input_width + im_col_idx];
-          }
+          col_data[(c * col_height + h) * col_width + w] =
+              (im_row_idx < 0 || im_row_idx >= im_height || im_col_idx < 0 ||
+               im_col_idx >= im_width)
+                  ? static_cast<T>(0)
+                  : im_data[(im_row_idx + c_im * im_height) * im_width +
+                            im_col_idx];
         }
       }
     }
@@ -95,35 +96,35 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
                     platform::CPUPlace, T> {
  public:
   void operator()(const platform::DeviceContext& context, framework::Tensor& im,
-                  const framework::Tensor& col, int stride_height,
-                  int stride_width, int padding_up, int padding_down,
-                  int padding_left, int padding_right) {
+                  const framework::Tensor& col, int dilation_h, int dilation_w,
+                  int stride_height, int stride_width, int padding_up,
+                  int padding_down, int padding_left, int padding_right) {
     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 im_channels = im.dims()[0];
+    int im_height = im.dims()[1];
+    int im_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 col_height = col.dims()[3];
+    int col_width = col.dims()[4];
 
-    PADDLE_ENFORCE_EQ(
-        (input_height + padding_up + padding_down - filter_height) /
-                stride_height +
-            1,
-        output_height,
-        "Output_height and padding(padding_up, padding_down) are "
-        "inconsistent.");
-    PADDLE_ENFORCE_EQ(
-        (input_width + padding_left + padding_right - filter_width) /
-                stride_width +
-            1,
-        output_width,
-        "output_width and padding(padding_left, padding_right) are "
-        "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_height + padding_up + padding_down -
+                       ((dilation_h * (filter_height - 1) + 1))) /
+                              stride_height +
+                          1,
+                      col_height,
+                      "Output_height and padding(padding_up, padding_down) are "
+                      "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_width + padding_left + padding_right -
+                       ((dilation_w * (filter_width - 1) + 1))) /
+                              stride_width +
+                          1,
+                      col_width,
+                      "col_width and padding(padding_left, padding_right) are "
+                      "inconsistent.");
 
-    int channels_col = input_channels * filter_height * filter_width;
+    int channels_col = im_channels * filter_height * filter_width;
 
     T* im_data = im.data<T>();
     const T* col_data = col.data<T>();
@@ -132,16 +133,18 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
       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 - padding_up;
-          int im_col_idx = w * stride_width + w_offset - padding_left;
+      for (int h = 0; h < col_height; ++h) {
+        for (int w = 0; w < col_width; ++w) {
+          int im_row_idx =
+              h * stride_height - padding_up + h_offset * dilation_h;
+          int im_col_idx =
+              w * stride_width - padding_left + w_offset * dilation_w;
 
-          if ((im_row_idx) >= 0 && (im_row_idx) < input_height &&
-              (im_col_idx) >= 0 && (im_col_idx) < input_width) {
-            im_row_idx += c_im * input_height;
-            im_data[im_row_idx * input_width + im_col_idx] +=
-                col_data[(c * output_height + h) * output_width + w];
+          if ((im_row_idx) >= 0 && (im_row_idx) < im_height &&
+              (im_col_idx) >= 0 && (im_col_idx) < im_width) {
+            im_row_idx += c_im * im_height;
+            im_data[im_row_idx * im_width + im_col_idx] +=
+                col_data[(c * col_height + h) * col_width + w];
           }
         }
       }
@@ -169,39 +172,38 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
  public:
   void operator()(const platform::DeviceContext& context,
                   const framework::Tensor& im, framework::Tensor& col,
-                  int stride_height, int stride_width, int padding_up,
-                  int padding_down, int padding_left, int padding_right) {
+                  int dilation_h, int dilation_w, int stride_height,
+                  int stride_width, int padding_up, int padding_down,
+                  int padding_left, int padding_right) {
     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 im_channels = im.dims()[0];
+    int im_height = im.dims()[1];
+    int im_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];
+    int col_height = col.dims()[0];
+    int col_width = col.dims()[1];
 
-    PADDLE_ENFORCE_EQ(
-        (input_height + padding_up + padding_down - filter_height) /
-                stride_height +
-            1,
-        output_height,
-        "Output_height and padding(padding_up, padding_down) are "
-        "inconsistent.");
-    PADDLE_ENFORCE_EQ(
-        (input_width + padding_left + padding_right - filter_width) /
-                stride_width +
-            1,
-        output_width,
-        "output_width and padding(padding_left, padding_right) are "
-        "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_height + padding_up + padding_down - filter_height) /
+                              stride_height +
+                          1,
+                      col_height,
+                      "Output_height and padding(padding_up, padding_down) are "
+                      "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_width + padding_left + padding_right - filter_width) /
+                              stride_width +
+                          1,
+                      col_width,
+                      "col_width and padding(padding_left, padding_right) are "
+                      "inconsistent.");
 
     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 col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
+      for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
+        for (int channel = 0; channel < im_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;
@@ -210,22 +212,21 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
                   col_row_idx * stride_height + filter_row_idx - padding_up;
               int im_col_offset =
                   col_col_idx * stride_width + filter_col_idx - padding_left;
-              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 im_offset =
-                    (channel * input_height + im_row_offset) * input_width +
-                    im_col_offset;
-                col_data[col_offset] = im_data[im_offset];
-              }
+              int col_offset =
+                  ((((col_row_idx)*col_width + col_col_idx) * im_channels +
+                    channel) *
+                       filter_height +
+                   filter_row_idx) *
+                      filter_width +
+                  filter_col_idx;
+
+              int im_offset = (channel * im_height + im_row_offset) * im_width +
+                              im_col_offset;
+              col_data[col_offset] =
+                  (im_row_offset < 0 || im_row_offset >= im_height ||
+                   im_col_offset < 0 || im_col_offset >= im_width)
+                      ? static_cast<T>(0)
+                      : im_data[im_offset];
             }
           }
         }
@@ -244,40 +245,38 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
                     platform::CPUPlace, T> {
  public:
   void operator()(const platform::DeviceContext& context, framework::Tensor& im,
-                  const framework::Tensor& col, int stride_height,
-                  int stride_width, int padding_up, int padding_down,
-                  int padding_left, int padding_right) {
+                  const framework::Tensor& col, int dilation_h, int dilation_w,
+                  int stride_height, int stride_width, int padding_up,
+                  int padding_down, int padding_left, int padding_right) {
     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 im_channels = im.dims()[0];
+    int im_height = im.dims()[1];
+    int im_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];
+    int col_height = col.dims()[0];
+    int col_width = col.dims()[1];
 
-    PADDLE_ENFORCE_EQ(
-        (input_height + padding_up + padding_down - filter_height) /
-                stride_height +
-            1,
-        output_height,
-        "Output_height and padding(padding_up, padding_down) are "
-        "inconsistent.");
-    PADDLE_ENFORCE_EQ(
-        (input_width + padding_left + padding_right - filter_width) /
-                stride_width +
-            1,
-        output_width,
-        "output_width and padding(padding_left, padding_right) are "
-        "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_height + padding_up + padding_down - filter_height) /
+                              stride_height +
+                          1,
+                      col_height,
+                      "Output_height and padding(padding_up, padding_down) are "
+                      "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_width + padding_left + padding_right - filter_width) /
+                              stride_width +
+                          1,
+                      col_width,
+                      "col_width and padding(padding_left, padding_right) are "
+                      "inconsistent.");
 
     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 col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
+      for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
+        for (int channel = 0; channel < im_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;
@@ -286,17 +285,17 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
                   col_row_idx * stride_height + filter_row_idx - padding_up;
               int im_col_offset =
                   col_col_idx * stride_width + filter_col_idx - padding_left;
-              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 col_offset =
+                  (((col_row_idx * col_width + col_col_idx) * im_channels +
+                    channel) *
+                       filter_height +
+                   filter_row_idx) *
+                      filter_width +
+                  filter_col_idx;
+              if (im_row_offset >= 0 && im_row_offset < im_height &&
+                  im_col_offset >= 0 && im_col_offset < im_width) {
                 int im_offset =
-                    (channel * input_height + im_row_offset) * input_width +
+                    (channel * im_height + im_row_offset) * im_width +
                     im_col_offset;
                 im_data[im_offset] += col_data[col_offset];
               }
diff --git a/paddle/operators/math/im2col.cu b/paddle/operators/math/im2col.cu
index 7b201fdbf3c5dd7d336d359e00b7323cecc0231a..69e2abee03b34ee9191fa594dddbeb6909e6d793 100644
--- a/paddle/operators/math/im2col.cu
+++ b/paddle/operators/math/im2col.cu
@@ -20,36 +20,32 @@ namespace operators {
 namespace math {
 
 template <class T>
-__global__ void im2col(const T* data_im, int num_outs, int height, int width,
+__global__ void im2col(const T* data_im, int num_outs, int im_height,
+                       int im_width, int dilation_h, int dilation_w,
                        int filter_height, int filter_width, int stride_height,
                        int stride_width, int padding_height, int padding_width,
-                       int output_height, int output_width, T* data_col) {
-  int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
+                       int col_height, int col_width, T* data_col) {
+  const int index =
+      (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
   if (index < num_outs) {
-    int w_out = index % output_width;
-    index /= output_width;
-    int h_out = index % output_height;
-    int channel_in = index / output_height;
+    int w_out = index % col_width;
+    int h_out = (index / col_width) % col_height;
+    int channel_in = index / col_width / col_height;
     int channel_out = channel_in * filter_height * filter_width;
-    int h_in = h_out * stride_height;
-    int w_in = w_out * stride_width;
+    int h_in = h_out * stride_height - padding_height;
+    int w_in = w_out * stride_width - padding_width;
 
-    data_col += (channel_out * output_height + h_out) * output_width + w_out;
+    data_col += (channel_out * col_height + h_out) * col_width + w_out;
+    data_im += (channel_in * im_height + h_in) * im_width + w_in;
     for (int i = 0; i < filter_height; ++i) {
       for (int j = 0; j < filter_width; ++j) {
-        int rIdx = int(h_in + i);
-        int cIdx = int(w_in + j);
-        if ((rIdx - (int)padding_height) >= (int)height ||
-            (rIdx - (int)padding_height) < 0 ||
-            (cIdx - (int)padding_width) >= (int)width ||
-            (cIdx - (int)padding_width) < 0) {
-          *data_col = 0;
-        } else {
-          rIdx = rIdx + channel_in * height - padding_height;
-          cIdx = cIdx - padding_width;
-          *data_col = data_im[rIdx * width + cIdx];
-        }
-        data_col += output_height * output_width;
+        int rIdx = h_in + i * dilation_h;
+        int cIdx = w_in + j * dilation_w;
+        *data_col =
+            (rIdx >= im_height || rIdx < 0 || cIdx >= im_width || cIdx < 0)
+                ? 0
+                : data_im[i * dilation_h * im_width + j * dilation_w];
+        data_col += col_height * col_width;
       }
     }
   }
@@ -66,29 +62,36 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
  public:
   void operator()(const platform::DeviceContext& context,
                   const framework::Tensor& im, framework::Tensor& col,
-                  int stride_height, int stride_width, int padding_up,
-                  int padding_down, int padding_left, int padding_right) {
+                  int dilation_h, int dilation_w, int stride_height,
+                  int stride_width, int padding_up, int padding_down,
+                  int padding_left, int padding_right) {
     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 im_channels = im.dims()[0];
+    int im_height = im.dims()[1];
+    int im_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];
-
-    PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
-                           stride_height +
-                       1 ==
-                   output_height);
-    PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
-                           stride_width +
-                       1 ==
-                   output_width);
-
-    int num_outputs = input_channels * output_height * output_width;
+    int col_height = col.dims()[3];
+    int col_width = col.dims()[4];
+
+    PADDLE_ENFORCE_EQ((im_height + padding_up + padding_down -
+                       (dilation_h * (filter_height - 1) + 1)) /
+                              stride_height +
+                          1,
+                      col_height,
+                      "Output_height and padding(padding_up, padding_down) are "
+                      "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_width + padding_left + padding_right -
+                       (dilation_w * (filter_width - 1) + 1)) /
+                              stride_width +
+                          1,
+                      col_width,
+                      "col_width and padding(padding_left, padding_right) are "
+                      "inconsistent.");
+
+    int num_outputs = im_channels * col_height * col_width;
     int blocks = (num_outputs + 1024 - 1) / 1024;
     int block_x = 512;
     int block_y = (blocks + 512 - 1) / 512;
@@ -97,56 +100,56 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
     im2col<T><<<grid, threads, 0,
                 reinterpret_cast<const platform::CUDADeviceContext&>(context)
                     .stream()>>>(
-        im.data<T>(), num_outputs, input_height, input_width, filter_height,
-        filter_width, stride_height, stride_width, padding_up, padding_left,
-        output_height, output_width, col.data<T>());
+        im.data<T>(), num_outputs, im_height, im_width, dilation_h, dilation_w,
+        filter_height, filter_width, stride_height, stride_width, padding_up,
+        padding_left, col_height, col_width, col.data<T>());
   }
 };
 
 template <class T>
-__global__ void col2im(size_t n, const T* data_col, size_t height, size_t width,
-                       size_t channels, size_t filter_height,
-                       size_t filter_width, size_t stride_height,
-                       size_t stride_width, size_t padding_height,
-                       size_t padding_width, size_t output_height,
-                       size_t output_width, T* data_im) {
-  size_t index =
+__global__ void col2im(int n, const T* data_col, int im_height, int im_width,
+                       int dilation_h, int dilation_w, int filter_height,
+                       int filter_width, int stride_height, int stride_width,
+                       int padding_height, int padding_width, int col_height,
+                       int col_width, T* data_im) {
+  const int index =
       (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
+
+  const int d_filter_height = dilation_h * (filter_height - 1) + 1;
+  const int d_filter_width = dilation_w * (filter_width - 1) + 1;
+
   if (index < n) {
     T val = 0;
-    int w = int(index % width);
-    int h = int((index / width) % height);
-    int c = int(index / (width * height));
-    if ((w - (int)padding_width) >= 0 &&
-        (w - (int)padding_width) < (width - 2 * padding_width) &&
-        (h - (int)padding_height) >= 0 &&
-        (h - padding_height) < (height - 2 * padding_height)) {
-      // compute the start and end of the output
-      int w_col_start = (w < (int)filter_width)
-                            ? 0
-                            : (w - int(filter_width)) / (int)stride_width + 1;
-      int w_col_end =
-          min((int)(w / (int)stride_width + 1), (int)(output_width));
-      int h_col_start = (h < (int)filter_height)
-                            ? 0
-                            : (h - (int)filter_height) / (int)stride_height + 1;
-      int h_col_end = min(int(h / stride_height + 1), int(output_height));
-      for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
-        for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
-          // the col location: [c * width * height + h_out, w_out]
-          int c_col = int(c * filter_height * filter_width) +
-                      (h - h_col * (int)stride_height) * (int)filter_width +
-                      (w - w_col * (int)stride_width);
-          val +=
-              data_col[(c_col * output_height + h_col) * output_width + w_col];
+    int w = index % im_width;
+    int h = (index / im_width) % im_height;
+    int c = index / (im_width * im_height);
+
+    // compute the start and end of the output
+    int w_col_start =
+        (w < d_filter_width) ? 0 : (w - d_filter_width) / stride_width + 1;
+    int w_col_end = min(w / stride_width + 1, col_width);
+    int h_col_start =
+        (h < d_filter_height) ? 0 : (h - d_filter_height) / stride_height + 1;
+    int h_col_end = min(h / stride_height + 1, col_height);
+
+    for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
+      for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
+        int h_off = (h - h_col * stride_height);
+        int w_off = (w - w_col * stride_width);
+        if (h_off % dilation_h == 0 && w_off % dilation_w == 0) {
+          h_off /= dilation_h;
+          w_off /= dilation_w;
+          int data_col_index =
+              (((c * filter_height + h_off) * filter_width + w_off) *
+                   col_height +
+               h_col) *
+                  col_width +
+              w_col;
+          val += data_col[data_col_index];
         }
       }
-      h -= padding_height;
-      w -= padding_width;
-      data_im[c * ((width - 2 * padding_width) *
-                   (height - 2 * padding_height)) +
-              h * (width - 2 * padding_width) + w] += val;
     }
+    data_im[index] = val;
   }
 }
 
@@ -160,32 +163,36 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
                     platform::GPUPlace, T> {
  public:
   void operator()(const platform::DeviceContext& context, framework::Tensor& im,
-                  const framework::Tensor& col, int stride_height,
-                  int stride_width, int padding_up, int padding_down,
-                  int padding_left, int padding_right) {
+                  const framework::Tensor& col, int dilation_h, int dilation_w,
+                  int stride_height, int stride_width, int padding_up,
+                  int padding_down, int padding_left, int padding_right) {
     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 im_channels = im.dims()[0];
+    int im_height = im.dims()[1];
+    int im_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];
-
-    PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
-                           stride_height +
-                       1 ==
-                   output_height);
-    PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
-                           stride_width +
-                       1 ==
-                   output_width);
-
-    size_t num_kernels = input_channels *
-                         (input_height + padding_up + padding_down) *
-                         (input_width + padding_left + padding_right);
+    int col_height = col.dims()[3];
+    int col_width = col.dims()[4];
+
+    PADDLE_ENFORCE_EQ((im_height + padding_up + padding_down -
+                       (dilation_h * (filter_height - 1) + 1)) /
+                              stride_height +
+                          1,
+                      col_height,
+                      "Output_height and padding(padding_up, padding_down) are "
+                      "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_width + padding_left + padding_right -
+                       (dilation_w * (filter_width - 1) + 1)) /
+                              stride_width +
+                          1,
+                      col_width,
+                      "col_width and padding(padding_left, padding_right) are "
+                      "inconsistent.");
+
+    size_t num_kernels = im_channels * im_height * im_width;
 
     size_t blocks = (num_kernels + 1024 - 1) / 1024;
     size_t block_x = 512;
@@ -198,10 +205,9 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
     col2im<T><<<grid, threads, 0,
                 reinterpret_cast<const platform::CUDADeviceContext&>(context)
                     .stream()>>>(
-        num_kernels, col.data<T>(), input_height + padding_up + padding_down,
-        input_width + padding_left + padding_left, input_channels,
+        num_kernels, col.data<T>(), im_height, im_width, dilation_h, dilation_w,
         filter_height, filter_width, stride_height, stride_width, padding_up,
-        padding_left, output_height, output_width, im.data<T>());
+        padding_left, col_height, col_width, im.data<T>());
   }
 };
 
@@ -215,33 +221,32 @@ template class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
                              platform::GPUPlace, double>;
 
 template <class T>
-__global__ void im2colOCF(const T* im_data, T* col_data, int input_channels,
-                          int input_height, int input_width, int filter_height,
+__global__ void im2colOCF(const T* im_data, T* col_data, int im_channels,
+                          int im_height, int im_width, int filter_height,
                           int filter_width, int stride_height, int stride_width,
-                          int padding_height, int padding_width,
-                          int output_height, int output_width) {
+                          int padding_height, int padding_width, int col_height,
+                          int col_width) {
   int swid = blockIdx.x;
   int shid = blockIdx.y;
-  for (int channelid = threadIdx.z; channelid < input_channels;
+  for (int channelid = threadIdx.z; channelid < im_channels;
        channelid += blockDim.z) {
     for (int idy = threadIdx.y; idy < filter_height; idy += blockDim.y) {
       for (int idx = threadIdx.x; idx < filter_width; idx += blockDim.x) {
         int width_offset = idx + swid * stride_width - padding_width;
         int height_offset = idy + shid * stride_height - padding_height;
-        int im_offset = width_offset + height_offset * input_width +
-                        channelid * input_height * input_width;
+        int im_offset = width_offset + height_offset * im_width +
+                        channelid * im_height * im_width;
 
         int col_offset = idx + idy * filter_width +
                          channelid * filter_height * filter_width +
-                         (shid * output_width + swid) *
-                             (input_channels * filter_height * filter_width);
-
-        if (height_offset >= input_height || height_offset < 0 ||
-            width_offset >= input_width || width_offset < 0) {
-          col_data[col_offset] = T(0);
-        } else {
-          col_data[col_offset] = im_data[im_offset];
-        }
+                         (shid * col_width + swid) *
+                             (im_channels * filter_height * filter_width);
+
+        col_data[col_offset] =
+            (height_offset >= im_height || height_offset < 0 ||
+             width_offset >= im_width || width_offset < 0)
+                ? T(0)
+                : im_data[im_offset];
       }
     }
   }
@@ -258,26 +263,33 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
  public:
   void operator()(const platform::DeviceContext& context,
                   const framework::Tensor& im, framework::Tensor& col,
-                  int stride_height, int stride_width, int padding_up,
-                  int padding_down, int padding_left, int padding_right) {
+                  int dilation_h, int dilation_w, int stride_height,
+                  int stride_width, int padding_up, int padding_down,
+                  int padding_left, int padding_right) {
     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 im_channels = im.dims()[0];
+    int im_height = im.dims()[1];
+    int im_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];
-
-    PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
-                           stride_height +
-                       1 ==
-                   output_height);
-    PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
-                           stride_width +
-                       1 ==
-                   output_width);
+    int col_height = col.dims()[0];
+    int col_width = col.dims()[1];
+
+    PADDLE_ENFORCE_EQ((im_height + padding_up + padding_down -
+                       (dilation_h * (filter_height - 1) + 1)) /
+                              stride_height +
+                          1,
+                      col_height,
+                      "Output_height and padding(padding_up, padding_down) are "
+                      "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_width + padding_left + padding_right -
+                       (dilation_w * (filter_width - 1) + 1)) /
+                              stride_width +
+                          1,
+                      col_width,
+                      "col_width and padding(padding_left, padding_right) are "
+                      "inconsistent.");
 
     int block_dim_x = 0;
     int block_dim_y = 0;
@@ -296,42 +308,41 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
     }
 
     int block_dim_z = 1024 / block_dim_x / block_dim_y;
-    dim3 threads(block_dim_x, block_dim_y,
-                 std::min(block_dim_z, input_channels));
-    dim3 grid(output_width, output_height);
+    dim3 threads(block_dim_x, block_dim_y, std::min(block_dim_z, im_channels));
+    dim3 grid(col_width, col_height);
     im2colOCF<T><<<grid, threads, 0,
                    reinterpret_cast<const platform::CUDADeviceContext&>(context)
                        .stream()>>>(
-        im.data<T>(), col.data<T>(), input_channels, input_height, input_width,
+        im.data<T>(), col.data<T>(), im_channels, im_height, im_width,
         filter_height, filter_width, stride_height, stride_width, padding_up,
-        padding_left, output_height, output_width);
+        padding_left, col_height, col_width);
   }
 };
 
 template <class T>
-__global__ void col2imOCF(T* im_data, const T* col_data, int input_channels,
-                          int input_height, int input_width, int filter_height,
+__global__ void col2imOCF(T* im_data, const T* col_data, int im_channels,
+                          int im_height, int im_width, int filter_height,
                           int filter_width, int stride_height, int stride_width,
-                          int padding_height, int padding_width,
-                          int output_height, int output_width) {
+                          int padding_height, int padding_width, int col_height,
+                          int col_width) {
   int swid = blockIdx.x;
   int shid = blockIdx.y;
-  for (int channelid = threadIdx.z; channelid < input_channels;
+  for (int channelid = threadIdx.z; channelid < im_channels;
        channelid += blockDim.z) {
     for (int idy = threadIdx.y; idy < filter_height; idy += blockDim.y) {
       for (int idx = threadIdx.x; idx < filter_width; idx += blockDim.x) {
         int width_offset = idx + swid * stride_width - padding_width;
         int height_offset = idy + shid * stride_height - padding_height;
-        int im_offset = width_offset + height_offset * input_width +
-                        channelid * input_height * input_width;
+        int im_offset = width_offset + height_offset * im_width +
+                        channelid * im_height * im_width;
 
         int col_offset = idx + idy * filter_width +
                          channelid * filter_height * filter_width +
-                         (shid * output_width + swid) *
-                             (input_channels * filter_height * filter_width);
+                         (shid * col_width + swid) *
+                             (im_channels * filter_height * filter_width);
 
-        if (height_offset >= 0 && height_offset < input_height &&
-            width_offset >= 0 && width_offset < input_width) {
+        if (height_offset >= 0 && height_offset < im_height &&
+            width_offset >= 0 && width_offset < im_width) {
           paddle::platform::CudaAtomicAdd(im_data + im_offset,
                                           col_data[col_offset]);
         }
@@ -350,27 +361,33 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
                     platform::GPUPlace, T> {
  public:
   void operator()(const platform::DeviceContext& context, framework::Tensor& im,
-                  const framework::Tensor& col, int stride_height,
-                  int stride_width, int padding_up, int padding_down,
-                  int padding_left, int padding_right) {
+                  const framework::Tensor& col, int dilation_h, int dilation_w,
+                  int stride_height, int stride_width, int padding_up,
+                  int padding_down, int padding_left, int padding_right) {
     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 im_channels = im.dims()[0];
+    int im_height = im.dims()[1];
+    int im_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];
-
-    PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
-                           stride_height +
-                       1 ==
-                   output_height);
-    PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
-                           stride_width +
-                       1 ==
-                   output_width);
+    int col_height = col.dims()[0];
+    int col_width = col.dims()[1];
+
+    PADDLE_ENFORCE_EQ((im_height + padding_up + padding_down -
+                       (dilation_h * (filter_height - 1) + 1)) /
+                              stride_height +
+                          1,
+                      col_height,
+                      "Output_height and padding(padding_up, padding_down) are "
+                      "inconsistent.");
+    PADDLE_ENFORCE_EQ((im_width + padding_left + padding_right -
+                       (dilation_w * (filter_width - 1) + 1)) /
+                              stride_width +
+                          1,
+                      col_width,
+                      "col_width and padding(padding_left, padding_right) are "
+                      "inconsistent.");
 
     int block_dim_x = 0;
     int block_dim_y = 0;
@@ -389,15 +406,14 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
     }
 
     int block_dim_z = 1024 / block_dim_x / block_dim_y;
-    dim3 threads(block_dim_x, block_dim_y,
-                 std::min(block_dim_z, input_channels));
-    dim3 grid(output_width, output_height);
+    dim3 threads(block_dim_x, block_dim_y, std::min(block_dim_z, im_channels));
+    dim3 grid(col_width, col_height);
     col2imOCF<T><<<grid, threads, 0,
                    reinterpret_cast<const platform::CUDADeviceContext&>(context)
                        .stream()>>>(
-        im.data<T>(), col.data<T>(), input_channels, input_height, input_width,
+        im.data<T>(), col.data<T>(), im_channels, im_height, im_width,
         filter_height, filter_width, stride_height, stride_width, padding_up,
-        padding_left, output_height, output_width);
+        padding_left, col_height, col_width);
   }
 };
 
diff --git a/paddle/operators/math/im2col.h b/paddle/operators/math/im2col.h
index c736d4fa523c2af3e3dd7a11114d7f84021bc5c1..d1c9595a328d349809daa32e3a3c2bf36b87af3d 100644
--- a/paddle/operators/math/im2col.h
+++ b/paddle/operators/math/im2col.h
@@ -74,17 +74,18 @@ class Im2ColFunctor {
  public:
   void operator()(const platform::DeviceContext& context,
                   const framework::Tensor& im, framework::Tensor& col,
-                  int stride_height, int stride_width, int padding_up,
-                  int padding_down, int padding_left, int padding_right);
+                  int dilation_h, int dilation_w, int stride_height,
+                  int stride_width, int padding_up, int padding_down,
+                  int padding_left, int padding_right);
 };
 
 template <ColFormat Format, typename Place, typename T>
 class Col2ImFunctor {
  public:
   void operator()(const platform::DeviceContext& context, framework::Tensor& im,
-                  const framework::Tensor& col, int stride_height,
-                  int stride_width, int padding_up, int padding_down,
-                  int padding_left, int padding_right);
+                  const framework::Tensor& col, int dilation_h, int dilation_w,
+                  int stride_height, int stride_width, int padding_up,
+                  int padding_down, int padding_left, int padding_right);
 };
 
 }  // namespace math
diff --git a/paddle/operators/math/im2col_test.cc b/paddle/operators/math/im2col_test.cc
index 5763782c4edec87f44dabef2ccffe3097eeb2421..3385fe8721cb41dbb294763677dd54e9218b229a 100644
--- a/paddle/operators/math/im2col_test.cc
+++ b/paddle/operators/math/im2col_test.cc
@@ -47,6 +47,8 @@ void testIm2col() {
   int filter_size = 2;
   int stride = 1;
   int padding = 0;
+  int dilation_h = 1;
+  int dilation_w = 1;
   int output_height = (input_height - filter_size + 2 * padding) / stride + 1;
   int output_width = (input_width - filter_size + 2 * padding) / stride + 1;
   float* input_ptr = input_tmp.mutable_data<float>(
@@ -85,10 +87,10 @@ void testIm2col() {
       paddle::operators::math::ColFormat::kOCF, Place, float>
       im2col_ocf;
 
-  im2col(*context, input, output_cfo, stride, stride, padding, padding, padding,
-         padding);
-  im2col_ocf(*context, input, output_ocf, stride, stride, padding, padding,
-             padding, padding);
+  im2col(*context, input, output_cfo, dilation_h, dilation_w, stride, stride,
+         padding, padding, padding, padding);
+  im2col_ocf(*context, input, output_ocf, dilation_h, dilation_w, stride,
+             stride, padding, padding, padding, padding);
 
   float out_cfo_data[] = {0, 1, 1, 2, 3, 4, 4, 5};
   float out_ocf_data[] = {0, 1, 3, 4, 1, 2, 4, 5};
@@ -131,8 +133,8 @@ void testIm2col() {
     input.CopyFrom(input_tmp, *place, *context);
   }
 
-  col2im(*context, input, output_cfo, stride, stride, padding, padding, padding,
-         padding);
+  col2im(*context, input, output_cfo, dilation_h, dilation_w, stride, stride,
+         padding, padding, padding, padding);
 
   float* in_ptr;
   if (paddle::platform::is_cpu_place(*place)) {
@@ -153,8 +155,8 @@ void testIm2col() {
     input.CopyFrom(input_tmp, *place, *context);
   }
 
-  col2im_ocf(*context, input, output_ocf, stride, stride, padding, padding,
-             padding, padding);
+  col2im_ocf(*context, input, output_ocf, dilation_h, dilation_w, stride,
+             stride, padding, padding, padding, padding);
 
   if (paddle::platform::is_cpu_place(*place)) {
     in_ptr = input.data<float>();