diff --git a/paddle/operators/conv_op.cc b/paddle/operators/conv_op.cc
index 852ac2ae37ca5d59215415787a80df95305bd4c0..a848b9b49cd2fe8ccf2f2dfeb4e99c26f4462f4d 100644
--- a/paddle/operators/conv_op.cc
+++ b/paddle/operators/conv_op.cc
@@ -54,6 +54,12 @@ 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) {
+    PADDLE_ENFORCE(in_dims[i + 2] + 2 * paddings[i] -
+                           (dilations[i] * (filter_dims[i + 2] - 1) + 1) >
+                       0,
+                   "Due to the settings of paddings, filter_dims and "
+                   "dilations, the output size is less than 0, please check "
+                   "again.");
     output_shape.push_back(OutputSize(in_dims[i + 2], filter_dims[i + 2],
                                       dilations[i], paddings[i], paddings[i],
                                       strides[i]));
@@ -100,11 +106,11 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
 Convolution Operator.
 
 The convolution operation calculates the output based on the input, filter
-and strides, paddings, groups parameters. The size of each dimension of the
+and strides, paddings, groups, dilations parameters. The size of each dimension of the
 parameters is checked in the infer-shape.
 Input(Input, Filter) and output(Output) are in NCHW format. Where N is batch
 size, C is the number of channels, H is the height of the feature, and W is
-the width of the feature. Parameters(ksize, strides, paddings) are two elements.
+the width of the feature. Parameters(ksize, strides, paddings, dilations) are two elements.
 These two elements represent height and width, respectively.
 The input(X) size and output(Out) size may be different.
 
@@ -115,8 +121,8 @@ Example:
   Output:
        Output shape: (N, C_out, H_out, W_out)
   where
-       H_out = (H_in - filter_size[0] + 2 * paddings[0]) / strides[0] + 1;
-       W_out = (W_in - filter_size[1] + 2 * paddings[1]) / strides[1] + 1;
+       H_out = (H_in + 2 * paddings[0] - (dilations[0]*(filter_size[0] - 1) + 1)) / strides[0] + 1;
+       W_out = (W_in + 2 * paddings[1] - (dilations[1]*(filter_size[1] - 1) + 1)) / strides[1] + 1;
 )DOC");
 }
 
diff --git a/python/paddle/v2/framework/tests/test_conv2d_op.py b/python/paddle/v2/framework/tests/test_conv2d_op.py
index 04ae7f294c27fdceaaff2e9a7ed854213e643945..f3f3930dab00a7fe23c2d9f445b0dbea19087adc 100644
--- a/python/paddle/v2/framework/tests/test_conv2d_op.py
+++ b/python/paddle/v2/framework/tests/test_conv2d_op.py
@@ -39,6 +39,7 @@ class TestConv2dOp(OpTest):
     def setUp(self):
         self.init_op_type()
         self.init_group()
+        self.init_dilation()
         self.init_test_case()
 
         conv2d_param = {'stride': self.stride, 'pad': self.pad}
@@ -80,12 +81,14 @@ class TestConv2dOp(OpTest):
     def init_test_case(self):
         self.pad = [0, 0]
         self.stride = [1, 1]
-        self.dilations = [1, 1]
         self.input_size = [2, 3, 5, 5]  # NCHW
         assert np.mod(self.input_size[1], self.groups) == 0
         f_c = self.input_size[1] / self.groups
         self.filter_size = [6, f_c, 3, 3]
 
+    def init_dilation(self):
+        self.dilations = [1, 1]
+
     def init_group(self):
         self.groups = 1