[v1.x] ONNX Supoort for MXNet _contrib_BilinearResize2D op (#19733)

* initial

* resize

* _contrib_BilinearResize2D

* restore sanity

python/mxnet/contrib/onnx/mx2onnx/_op_translations.py

@@ -2698,6 +2698,63 @@ def convert_arange_like(node, **kwargs):
 
     return nodes
 
+
+@mx_op.register("_contrib_BilinearResize2D")
+def convert_contrib_BilinearResize2D(node, **kwargs):
+    """Map MXNet's contrib_BilinearResize2D operator attributes to onnx.
+    """
+    from onnx.helper import make_node
+    from onnx import TensorProto
+    name, input_nodes, attrs = get_inputs(node, kwargs)
+
+    opset_version = kwargs['opset_version']
+    if opset_version < 11:
+        raise AttributeError("ONNX opset 11 or greater is required to export this operator")
+
+    height = int(attrs.get('height', 0))
+    width = int(attrs.get('width', 0))
+
+    scale_height = float(attrs.get('scale_height', 0))
+    scale_width = float(attrs.get('scale_width', 0))
+
+    if height * width == 0 and scale_height * scale_width == 0:
+        raise AttributeError('height, width or scale_height, scale_width cannot be 0')
+
+    mode = attrs.get('mode', 'size')
+    if mode != 'size':
+        raise NotImplementedError('contrib_BilinearResize2D with mode other than "size" is \
+                                   not supported')
+
+    nodes = [
+        create_tensor([], name+'_roi', kwargs['initializer'], dtype='float32'),
+        ]
+
+    if scale_height == 0:
+        nodes += [
+            create_tensor([0], name+'_0', kwargs['initializer']),
+            create_tensor([2], name+'_2', kwargs['initializer']),
+            create_tensor([height, width], name+'_h_w', kwargs['initializer'], dtype='int64'),
+            make_node('Shape', [input_nodes[0]], [name+'_shape']),
+            make_node('Slice', [name+'_shape', name+'_0', name+'_2'], [name+'_shape_01']),
+            make_node('Concat', [name+'_shape_01', name+'_h_w'], [name+'_new_shape'], axis=0),
+            make_node('Cast', [name+'_shape'], [name+'_shape_f'], to=int(TensorProto.FLOAT)),
+            make_node('Cast', [name+'_new_shape'], [name+'_new_shape_f'],
+                      to=int(TensorProto.FLOAT)),
+            make_node('Div', [name+'_new_shape_f', name+'_shape_f'], [name+'_scales']),
+            make_node('Resize', [input_nodes[0], name+'_roi', name+'_scales'], [name],
+                      mode='linear', coordinate_transformation_mode='align_corners', name=name)
+            ]
+    else:
+        nodes += [
+            create_tensor([1, 1, scale_height, scale_width], name+'_scales', kwargs['initializer'],
+                          dtype='float32'),
+            make_node('Resize', [input_nodes[0], name+'_roi', name+'_scales'], [name],
+                      mode='linear', coordinate_transformation_mode='align_corners', name=name)
+            ]
+
+    return nodes
+
+
 @mx_op.register("_arange")
 def convert_arange(node, **kwargs):
     """Map MXNet's arange operator attributes to onnx's Range operator.

tests/python-pytest/onnx/test_operators.py

@@ -351,3 +351,22 @@ def test_onnx_export_softmax(tmp_path, dtype):
     M4 = def_model('softmax', use_length=True, axis=1)
     l4 = mx.nd.array([[2,0,3,1],[0,1,0,0]], dtype=int)
     op_export_test('softmax_4', M4, [x, l4], tmp_path)
+
+
+@pytest.mark.parametrize('dtype', ['float16', 'float32', 'float64', 'int32', 'int64'])
+@pytest.mark.parametrize('params', [{'height': 7, 'width': 13},
+                                    {'height': 10, 'width': 16},
+                                    {'height': 3, 'width': 5},
+                                    {'height': 2, 'width': 4},
+                                    {'scale_height': 3, 'scale_width': 2},
+                                    {'scale_height': 1.7, 'scale_width': 2.3},
+                                    {'scale_height': 0.5, 'scale_width': 0.6},
+                                    {'scale_height': 0.8, 'scale_width': 0.1},
+                                    {'scale_height': 2.5, 'scale_width': 0.5},
+                                    {'scale_height': 3, 'scale_width': 0.00001},
+                                    ])
+def test_onnx_export_contrib_BilinearResize2D(tmp_path, dtype, params):
+    x = mx.nd.arange(0, 160).reshape((2, 2, 5, 8))
+    M = def_model('contrib.BilinearResize2D', **params)
+    op_export_test('contrib_BilinearResize2D', M, [x], tmp_path)
+