diff --git a/src/dialect/onnx/onnx_ops.cpp b/src/dialect/onnx/onnx_ops.cpp
index 1b89893..5474856 100644
--- a/src/dialect/onnx/onnx_ops.cpp
+++ b/src/dialect/onnx/onnx_ops.cpp
@@ -1045,39 +1045,41 @@ void ONNXMaxPoolSingleOutOp::inferShapes() {
 
 //===----------------------------------------------------------------------===//
 
+static Type padShapeInferenceHelper(Value data, ArrayAttr padsOpt) {
+  // Cannot infer shape if no shape exists.
+  if (!data.getType().isa<RankedTensorType>())
+    return (Type)NULL;
+  auto dataTy = data.getType().cast<RankedTensorType>();
+  auto dataShape = dataTy.getShape();
+  auto dataRank = dataShape.size();
+  SmallVector<int64_t, 4> outputShape(dataShape.begin(), dataShape.end());
+  if (padsOpt) {
+    auto padsArray = padsOpt.getValue();
+    // Pads consists of two values for each axis of data.
+    // The two values specify the number of elements padded before and after respectively.
+    for (int i = 0; i < dataRank; ++i) {
+      int64_t p1 = (padsArray[2*i]).cast<IntegerAttr>().getInt();
+      int64_t p2 = (padsArray[2*i+1]).cast<IntegerAttr>().getInt();
+      //Have to non-negative constant
+      if (p1 < 0 || p2 <0) 
+        return (Type)NULL;
+      outputShape[i] += p1+p2;
+    }
+
+    return (RankedTensorType::get(outputShape, dataTy.getElementType()));
+  } else {
+    return (Type)NULL;
+  }
+}
+
 // PadConstantPad
 
 void ONNXPadConstantPadOp::inferShapes(){
-  // Cannot infer shape if no shape exists.
-  if (!data().getType().isa<RankedTensorType>())
-    return;
-
-  // 1) get shape of input "data"
-  auto dataTy = data().getType().cast<RankedTensorType>();
-  auto dataShape = dataTy.getShape();
-  auto dataRank = dataShape.size();
-
-  SmallVector<int64_t, 4> outputShape(dataShape.begin(), dataShape.end());
-  auto padsOpt = pads();
-  if (padsOpt) {
-    auto padsArray = padsOpt.getValue();
-    // pads consists of two entries for each spatial axis.
-    if (padsArray.size() != 2 * dataRank)
-      emitError("pads rank is not twice the spatial rank.");
-    // fill in the actual values
-    for (int i = 0; i < dataRank; ++i) {
-      int64_t p1 = (padsArray[2*i]).cast<IntegerAttr>().getInt();
-      if (p1 < 0) 
-        emitError("pads value must be nonnegative.");
-      int64_t p2 = (padsArray[2*i+1]).cast<IntegerAttr>().getInt();
-      if (p2 < 0) 
-        emitError("pads value must be nonnegative.");
-      outputShape[i] += p1+p2;
-    }
-    getResult().setType(RankedTensorType::get(outputShape, dataTy.getElementType()));
-  } else {
-    emitError("pads attribute is not available.");
-  }
+  auto outputType = padShapeInferenceHelper(data(), pads());
+  if (outputType) {
+    getResult().setType(outputType);
+  } 
+  return;
 }
 
 //===----------------------------------------------------------------------===//
@@ -1085,36 +1087,11 @@ void ONNXPadConstantPadOp::inferShapes(){
 // PadConstantValuePad
 
 void ONNXPadConstantValuePadOp::inferShapes(){
-  // Cannot infer shape if no shape exists.
-  if (!data().getType().isa<RankedTensorType>())
-    return;
-
-  // 1) get shape of input "data"
-  auto dataTy = data().getType().cast<RankedTensorType>();
-  auto dataShape = dataTy.getShape();
-  auto dataRank = dataShape.size();
-
-  SmallVector<int64_t, 4> outputShape(dataShape.begin(), dataShape.end());
-  auto padsOpt = pads();
-  if (padsOpt) {
-    auto padsArray = padsOpt.getValue();
-    // pads consists of two entries for each spatial axis.
-    if (padsArray.size() != 2 * dataRank)
-      emitError("pads rank is not twice the spatial rank.");
-    // fill in the actual values
-    for (int i = 0; i < dataRank; ++i) {
-      int64_t p1 = (padsArray[2*i]).cast<IntegerAttr>().getInt();
-      if (p1 < 0) 
-        emitError("pads value must be nonnegative.");
-      int64_t p2 = (padsArray[2*i+1]).cast<IntegerAttr>().getInt();
-      if (p2 < 0) 
-        emitError("pads value must be nonnegative.");
-      outputShape[i] += p1+p2;
-    }
-    getResult().setType(RankedTensorType::get(outputShape, dataTy.getElementType()));
-  } else {
-    emitError("pads attribute is not available.");
-  }
+  auto outputType = padShapeInferenceHelper(data(), pads());
+  if (outputType) {
+    getResult().setType(outputType);
+  } 
+  return;
 }
 
 //===----------------------------------------------------------------------===//