[v1.x] Add onnx export operator for minimum_scalar.

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

@@ -3293,6 +3293,28 @@ def convert_maximum_scalar(node, **kwargs):
 
     return nodes
 
+@mx_op.register('_minimum_scalar')
+def convert_minimum_scalar(node, **kwargs):
+    """Map MXNet's _minimum_scalar
+    """
+    from onnx.helper import make_node
+    name, input_nodes, attrs = get_inputs(node, kwargs)
+
+    input_type = int(kwargs['in_type'])
+    dtype = onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[input_type]
+
+    scalar = None
+    if 'float' in str(dtype):
+        scalar = float(attrs.get('scalar', '0'))
+    else:
+        scalar = int(attrs.get('scalar', '0'))
+
+    nodes = [
+        create_tensor([scalar], name+'_scalar', kwargs['initializer'], dtype=dtype),
+        make_node('Min', [input_nodes[0], name+'_scalar'], [name], name=name)
+    ]
+
+    return nodes
 
 @mx_op.register("_contrib_box_decode")
 def convert_contrib_box_decode(node, **kwargs):

tests/python-pytest/onnx/test_operators.py

@@ -609,6 +609,16 @@ def test_onnx_maximum_scalar(tmp_path, dtype, shape):
     op_export_test('_maximum_scalar', M, [x], tmp_path)
 
 
+# opset 8 Min only supports float types
+# opset 12 and up suppots float and int
+@pytest.mark.parametrize('dtype', ['float16', 'float32', 'float64'])
+@pytest.mark.parametrize('shape', [(3, 4, 5), (1, 4, 1, 7)])
+def test_onnx_minimum_scalar(tmp_path, dtype, shape):
+    x = mx.random.uniform(0, 10, shape).astype(dtype)
+    M = def_model('minimum', right=5)
+    op_export_test('_minimum_scalar', M, [x], tmp_path)
+
+
 @pytest.mark.parametrize('dtype', ['float16', 'float32'])
 @pytest.mark.parametrize('fmt', ['corner', 'center'])
 @pytest.mark.parametrize('clip', [-1., 0., .5, 5.])