[v1.x] Add onnx export support for one_hot and random_uniform_like and unit tests for one_hot.

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

@@ -4035,6 +4035,47 @@ def convert_argsort(node, **kwargs):
     return nodes
 
 
+@mx_op.register('one_hot')
+def convert_one_hot(node, **kwargs):
+    """Map MXNet's one_hot operator attributes to onnx's OneHot operator
+    """
+    from onnx.helper import make_node
+    name, input_nodes, attrs = get_inputs(node, kwargs)
+
+    depth = int(attrs.get('depth'))
+    on_value = float(attrs.get('on_value', 1.))
+    off_value = float(attrs.get('off_value', 0.))
+    dtype = attrs.get('dtype', 'float32')
+
+    create_tensor([off_value, on_value], name+'_values', kwargs['initializer'], dtype=np.dtype(dtype))
+    create_tensor([depth], name+'_depth', kwargs['initializer'])
+    nodes = [
+        make_node('OneHot', [input_nodes[0], name+'_depth', name+'_values'], [name], name=name)
+    ]
+
+    return nodes
+
+
+@mx_op.register('_random_uniform_like')
+def convert_random_uniform_like(node, **kwargs):
+    """Map MXNet's random_uniform_like operator attributes to onnx's RandomUniformLike operator
+    """
+    from onnx.helper import make_node
+    name, input_nodes, attrs = get_inputs(node, kwargs)
+
+    low = float(attrs.get('low', 0.))
+    high = float(attrs.get('high', 1.))
+    dtype = attrs.get('dtype', 'float32')
+
+    nodes = [
+        make_node('RandomUniformLike', [input_nodes[0]], [name], name=name,
+                  dtype=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)],
+                  low=low, high=high)
+    ]
+
+    return nodes
+
+
 @mx_op.register('SequenceReverse')
 def convert_sequence_reverse(node, **kwargs):
     """Map MXNet's SequenceReverse op

tests/python-pytest/onnx/test_operators.py

@@ -1183,6 +1183,16 @@ def test_onnx_export_take_raise(tmp_path, dtype, axis):
     op_export_test('take', M, [x, y], tmp_path)
 
 
+# onnxruntime currently does not support int32
+@pytest.mark.parametrize("dtype", ["float16", "float32", "int64"])
+@pytest.mark.parametrize("depth", [1, 3, 5, 10])
+@pytest.mark.parametrize("shape", [(1,1), (1,5), (5,5), (3,4,5)])
+def test_onnx_export_one_hot(tmp_path, dtype, depth, shape):
+    M = def_model('one_hot', depth=depth, dtype=dtype)
+    x = mx.random.randint(0, 10, shape).astype('int64')
+    op_export_test('one_hot', M, [x], tmp_path)
+
+
 @pytest.mark.parametrize('dtype', ['int32', 'int64', 'float16', 'float32', 'float64'])
 @pytest.mark.parametrize('params', [((6, 5, 4), [1, 2, 4, 5, 6]),
                                      ((7, 3, 5), [1, 7, 4]),