diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index 397ca90de55f..75ebb44cbb84 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -55,6 +55,17 @@ def revert_caffe2_pad(pads):
     return pads
 
 
+def get_pad_pair(input1d, kernel1d, stride1d):
+    """infer pad size"""
+    if input1d % stride1d == 0:
+        pad = max(kernel1d - stride1d, 0)
+    else:
+        pad = max(kernel1d - (input1d % stride1d), 0)
+    pad_before = pad // 2
+    pad_after = pad - pad_before
+    return [pad_before, pad_after]
+
+
 def onnx_storage_order2layout(storage_order):
     """converter of onnx storage order parameter to tvm storage order format"""
     if storage_order not in (0, 1):
@@ -182,14 +193,37 @@ class Conv(OnnxOpConverter):
 
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
-        out = AttrCvt(op_name=dimension_picker('conv'),
-                      transforms={
-                          'kernel_shape': 'kernel_size',
-                          'dilations': ('dilation', (0, 0)),
-                          'pads': ('padding', (0, 0), revert_caffe2_pad),
-                          'group': ('groups', 1)},
-                      ignores=['auto_pad'],
-                      custom_check=dimension_constraint())(inputs[:2], attr, params)
+        # infer pads for auto_pad
+        if 'auto_pad' in attr:
+            attr['auto_pad'] = attr['auto_pad'].decode('utf-8')
+            if attr['auto_pad'] in ('SAME_UPPER', 'SAME_LOWER'):
+                input_shape = infer_shape(inputs[0])
+                in_h, in_w = input_shape[2], input_shape[3]
+                stride_h, stride_w = attr['strides']
+                kernel_h, kernel_w = attr['kernel_shape']
+                dilation_h, dilation_w = attr['dilations']
+                dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
+                dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
+                pad_v = get_pad_pair(in_h, dilated_kernel_h, stride_h)
+                pad_h = get_pad_pair(in_w, dilated_kernel_w, stride_w)
+                attr['pads'] = (pad_v[0], pad_h[0], pad_v[1], pad_h[1])
+            elif attr['auto_pad'] == 'VALID':
+                attr['pads'] = (0, 0)
+            elif attr['auto_pad'] == 'NOTSET':
+                pass
+            else:
+                msg = 'Value {} in attribute "auto_pad" of operator Conv is invalid.'
+                raise tvm.error.OpAttributeInvalid(msg.format(attr['auto_pad']))
+            attr.pop('auto_pad')
+
+        out = AttrCvt(
+            op_name=dimension_picker('conv'),
+            transforms={
+                'kernel_shape': 'kernel_size',
+                'dilations': ('dilation', (0, 0)),
+                'pads': ('padding', (0, 0), revert_caffe2_pad),
+                'group': ('groups', 1)},
+            custom_check=dimension_constraint())(inputs[:2], attr, params)
         use_bias = len(inputs) == 3
         if use_bias:
             out = _op.nn.bias_add(out, inputs[2])
@@ -206,6 +240,29 @@ def _impl_v1(cls, inputs, attr, params):
         attr['channels'] = channels
         groups = attr.pop('group')
         attr['groups'] = groups
+        # infer pads for auto_pad
+        if 'auto_pad' in attr:
+            attr['auto_pad'] = attr['auto_pad'].decode('utf-8')
+            if attr['auto_pad'] in ('SAME_UPPER', 'SAME_LOWER'):
+                input_shape = infer_shape(inputs[0])
+                in_h, in_w = input_shape[2], input_shape[3]
+                stride_h, stride_w = attr['strides']
+                kernel_h, kernel_w = attr['kernel_shape']
+                dilation_h, dilation_w = attr['dilations']
+                dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
+                dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
+                pad_v = get_pad_pair(in_h, dilated_kernel_h, stride_h)
+                pad_h = get_pad_pair(in_w, dilated_kernel_w, stride_w)
+                attr['pads'] = (pad_v[0], pad_h[0], pad_v[1], pad_h[1])
+            elif attr['auto_pad'] == 'VALID':
+                attr['pads'] = (0, 0)
+            elif attr['auto_pad'] == 'NOTSET':
+                pass
+            else:
+                msg = 'Value {} in attribute "auto_pad" of operator Conv is invalid.'
+                raise tvm.error.OpAttributeInvalid(msg.format(attr['auto_pad']))
+            attr.pop('auto_pad')
+
         out = AttrCvt(
             op_name=dimension_picker('conv', '_transpose'),
             transforms={