Apparently, ONNX Conv with no 'pads' defaults to zero padding (apache…

…#5548)

python/tvm/relay/frontend/onnx.py

@@ -325,7 +325,6 @@ class Conv(OnnxOpConverter):
     def _impl_v1(cls, inputs, attr, params):
         # Use shape of input to determine convolution type.
         input_shape = infer_shape(inputs[0])
-
         if 'auto_pad' in attr:
             attr['auto_pad'] = attr['auto_pad'].decode('utf-8')
             if attr['auto_pad'] in ('SAME_UPPER', 'SAME_LOWER'):
@@ -350,7 +349,10 @@ def _impl_v1(cls, inputs, attr, params):
             attr.pop('auto_pad')
         elif len(attr['kernel_shape']) == 2:
             sym_pad = True
-            padding = attr['pads']
+            if 'pads' in attr:
+                padding = attr['pads']
+            else:
+                padding = [0, 0, 0, 0]
             for i in range(0, len(padding), 2):
                 sym_pad = sym_pad and padding[i] == padding[i + 1]
 

tests/python/frontend/onnx/test_forward.py

@@ -2028,8 +2028,18 @@ def test_or():
     verify_or(indata=[x, y], dtype=bool)
 
 
-def verify_conv(x_shape, w_shape, y_shape, padding, kernel_shape, strides, dilations, auto_pad="NOTSET"):
-    if padding is None:
+def verify_conv(x_shape, w_shape, y_shape, padding, kernel_shape, strides, dilations, auto_pad="NOTSET", unset_pad=False):
+    if unset_pad:
+        node = helper.make_node('Conv',
+                                inputs=['x', 'W'],
+                                outputs=['y'],
+                                kernel_shape=kernel_shape,
+                                # Default values for other attributes:
+                                strides=strides,
+                                dilations=dilations,
+                                # groups=1
+                                )
+    elif padding is None:
         node = helper.make_node('Conv',
                                 inputs=['x', 'W'],
                                 outputs=['y'],
@@ -2095,6 +2105,15 @@ def repeat(N, D):
                     repeat(1, D),
                     repeat(1, D),
                     auto_pad="SAME_UPPER")
+        # Convolution with unset padding
+        verify_conv((1, 1) + repeat(5, D),
+                    (1, 1) + repeat(3, D),
+                    (1, 1) + repeat(3, D),
+                    2 * repeat(0, D),
+                    repeat(3, D),
+                    repeat(1, D),
+                    repeat(1, D),
+                    True)
         # Convolution with non uniform stride
         verify_conv((1, 1) + repeat(5, D),
                     (1, 1) + repeat(3, D),