diff --git a/src/dialect/onnx/onnx_ops.cpp b/src/dialect/onnx/onnx_ops.cpp
index 687a431..265842b 100644
--- a/src/dialect/onnx/onnx_ops.cpp
+++ b/src/dialect/onnx/onnx_ops.cpp
@@ -520,6 +520,26 @@ void ONNXConvNoBiasOp::inferShapes() {
       kernelDims.emplace_back(weightShape[i + 2]);
   }
 
+  // Check if dilations attribute is present.
+  // If it is then compute new kernel size that includes the receptive field.
+  // In this calculation we assume that the receptive field pixels must all be
+  // within the bounds of the image. In this case the new kernel size is given
+  // by:
+  //
+  // ( K + 1 ) * d - 1
+  // where K is a kernel dimension and d is the dilation along that axis.
+  //
+  // From a dimensionality perspective the kernel size becomes the dilated
+  // kernel size.
+  if (auto dilations = getAttrOfType<ArrayAttr>(
+          ONNXConvOp::getDilationsAttrName())) {
+    if (dilations.getValue().size() != nDims)
+      emitError("dilations length incompatible with spatial dimensions.");
+    for (int i = 0; i < nDims; ++i)
+      kernelDims[i] = (kernelDims[i] + 1) *
+          (dilations.getValue()[i]).cast<IntegerAttr>().getInt() + 1;
+  }
+
   // Subtract kernel dimensions from input data dimensions.
   for (int i = 0; i < nDims; ++i)
     outSpatialDims[i] -= kernelDims[i];
@@ -545,13 +565,14 @@ void ONNXConvNoBiasOp::inferShapes() {
     }
   } else if (autoPad == "SAME_UPPER" || autoPad == "SAME_LOWER") {
     // Pad input so that output size matches input size.
-    // Each spatial dimension needs to be padded by:
+    // Each spatial dimension needs to be padded by a total of:
     //
-    // ( K - 1 ) / 2
+    // K - 1
     //
     // where K is a kernel spatial dimension.
+    // Pad as if stride is 1.
     for (int i = 0; i < nDims; ++i)
-      outSpatialDims[i] += floor((kernelDims[i] - 1) / 2);
+      outSpatialDims[i] += kernelDims[i] - 1;
   } else if (autoPad == "VALID") {
     // No padding
   } else {
diff --git a/test/mlir/onnx/onnx_shape_inference.mlir b/test/mlir/onnx/onnx_shape_inference.mlir
index aaa08a7..5b7530f 100644
--- a/test/mlir/onnx/onnx_shape_inference.mlir
+++ b/test/mlir/onnx/onnx_shape_inference.mlir
@@ -1,7 +1,10 @@
 // RUN: onnf-opt --shape-inference %s -split-input-file | FileCheck %s
 
+//===----------------------------------------------------------------------===//
 /// Test the default behavior of transpose when no information for the
-/// permutation of the axes is provided.
+/// permutation of the axes is provided and when a permutation is provided.
+//===----------------------------------------------------------------------===//
+
 func @test_default_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
   %0 = "onnx.Transpose"(%arg0) : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
@@ -12,6 +15,7 @@ func @test_default_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
 // CHECK: return [[RES]] : tensor<32x1x5x5xf32>
 
 /// Test shape inference for transposition when perm attribute is specified.
+
 func @test_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
   %0 = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
@@ -19,4 +23,118 @@ func @test_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
 
 // CHECK-LABEL: test_transpose
 // CHECK: [[RES_ATTR:%.+]] = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<1x5x32x5xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x32x5xf32>
\ No newline at end of file
+// CHECK: return [[RES_ATTR]] : tensor<1x5x32x5xf32>
+
+//===----------------------------------------------------------------------===//
+/// Test shape inference for ConvNoBias operation and all its attributes.
+//===----------------------------------------------------------------------===//
+
+/// Default and required attributes.
+
+func @test_conv_no_bias_1(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
+  %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_conv_no_bias_1
+// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32>
+
+/// kernel_shape attribute.
+
+func @test_conv_no_bias_2(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
+  %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_conv_no_bias_2
+// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32>
+
+/// pads attribute.
+/// Use pads to make output size equal to input size by adding K - 1 to the result.
+
+func @test_conv_no_bias_3(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
+  %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_conv_no_bias_3
+// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
+
+/// auto_pad set to SAME_UPPER and SAME_LOWER.
+
+func @test_conv_no_bias_4(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
+  %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_conv_no_bias_4
+// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
+
+func @test_conv_no_bias_5(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
+  %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_conv_no_bias_5
+// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
+
+/// auto_pad set to VALID.
+
+func @test_conv_no_bias_6(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
+  %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_conv_no_bias_6
+// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32>
+
+/// With strides attribute.
+
+func @test_conv_no_bias_7(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
+  %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_conv_no_bias_7
+// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32>
+
+/// auto_pad set to SAME_UPPER with strides attribute.
+/// The auto_pad will pas as if stride is equal to 1.
+
+func @test_conv_no_bias_8(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
+  %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_conv_no_bias_8
+// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x16x22xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xf32>
+
+/// dilations attribute.
+
+func @test_conv_no_bias_9(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
+  %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_conv_no_bias_9
+// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x18x40xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x18x40xf32>
+
+/// dilations attribute with stride.
+
+func @test_conv_no_bias_10(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
+  %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, dilations = [2, 3], strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_conv_no_bias_10
+// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i32, strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x9x20xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x9x20xf32>