onnx export ops (#13821)

* onnx export ops

* retrigger ci

* retrigger ci

* fix

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

@@ -656,12 +656,19 @@ def convert_exp(node, **kwargs):
     return create_basic_op_node('Exp', node, kwargs)
 
 @mx_op.register("_copy")
-def convert_identity(node, **kwargs):
+def convert_copy(node, **kwargs):
     """Map MXNet's _copy operator attributes to onnx's Identity operator
     and return the created node.
     """
     return create_basic_op_node('Identity', node, kwargs)
 
+@mx_op.register("identity")
+def convert_identity(node, **kwargs):
+    """Map MXNet's identity operator attributes to onnx's ConstantFill operator
+    and return the created node.
+    """
+    return create_basic_op_node('ConstantFill', node, kwargs)
+
 @mx_op.register("InstanceNorm")
 def convert_instancenorm(node, **kwargs):
     """Map MXNet's InstanceNorm operator attributes to onnx's InstanceNormalization operator
@@ -752,6 +759,31 @@ def convert_softmax_output(node, **kwargs):
 
     return [softmax_node]
 
+@mx_op.register("LogisticRegressionOutput")
+def convert_logistic_regression_output(node, **kwargs):
+    """Map MXNet's SoftmaxOutput operator attributes to onnx's Softmax operator
+    and return the created node.
+    """
+    name = node["name"]
+    input1_idx = kwargs["index_lookup"][node["inputs"][0][0]]
+    input1 = kwargs["proc_nodes"][input1_idx]
+    sigmoid_node = onnx.helper.make_node(
+        "Sigmoid",
+        [input1.name],
+        [name],
+        name=name
+    )
+    return [sigmoid_node]
+
+@mx_op.register("BlockGrad")
+def convert_blockgrad(node, **kwargs):
+    """ Skip operator  """
+    return create_basic_op_node('ConstantFill', node, kwargs)
+
+@mx_op.register("MakeLoss")
+def convert_makeloss(node, **kwargs):
+    """ Skip operator  """
+    return create_basic_op_node('ConstantFill', node, kwargs)
 
 @mx_op.register("Concat")
 def convert_concat(node, **kwargs):
@@ -898,7 +930,7 @@ def convert_clip(node, **kwargs):
 def scalar_op_helper(node, op_name, **kwargs):
     """Helper function for scalar arithmetic operations"""
     name, input_nodes, attrs = get_inputs(node, kwargs)
-
+    from onnx import numpy_helper
     input_type = kwargs["in_type"]
     scalar_value = np.array([attrs.get("scalar", 1)],
                             dtype=onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[input_type])
@@ -910,13 +942,21 @@ def scalar_op_helper(node, op_name, **kwargs):
     for i in initializer:
         if i.name == input_nodes[0]:
             if op_name == 'Mul':
-                new_initializer = onnx.numpy_helper.to_array(i) * scalar_value[0]
+                new_initializer = numpy_helper.to_array(i) * scalar_value[0]
             elif op_name == 'Sub':
-                new_initializer = onnx.numpy_helper.to_array(i) - scalar_value[0]
+                if name.startswith("_rminusscalar"):
+                    new_initializer = scalar_value[0] - numpy_helper.to_array(i)
+                else:
+                    new_initializer = numpy_helper.to_array(i) - scalar_value[0]
             elif op_name == 'Add':
-                new_initializer = onnx.numpy_helper.to_array(i) + scalar_value[0]
+                new_initializer = numpy_helper.to_array(i) + scalar_value[0]
             elif op_name == 'Div':
-                new_initializer = onnx.numpy_helper.to_array(i) / scalar_value[0]
+                if name.startswith("_rdivscalar"):
+                    new_initializer = scalar_value[0] / numpy_helper.to_array(i)
+                else:
+                    new_initializer = numpy_helper.to_array(i) / scalar_value[0]
+            elif op_name == 'Pow':
+                new_initializer = numpy_helper.to_array(i) ** scalar_value[0]
             flag = False
             break
 
@@ -982,6 +1022,13 @@ def convert_minus_scalar(node, **kwargs):
     """
     return scalar_op_helper(node, 'Sub', **kwargs)
 
+@mx_op.register("_rminus_scalar")
+def convert_rminus_scalar(node, **kwargs):
+    """Map MXNet's _rminus_scalar operator attributes to onnx's Sub operator.
+    Creates a new node for the input scalar value, adds it to the initializer
+    and return multiple created nodes.
+    """
+    return scalar_op_helper(node, 'Sub', **kwargs)
 
 # Convert scalar value into node and pass it as input to mul_node
 @mx_op.register("_plus_scalar")
@@ -1001,6 +1048,21 @@ def convert_div_scalar(node, **kwargs):
     """
     return scalar_op_helper(node, 'Div', **kwargs)
 
+@mx_op.register("_rdiv_scalar")
+def convert_rdiv_scalar(node, **kwargs):
+    """Map MXNet's _rdiv_scalar operator attributes to onnx's Div operator.
+    Creates a new node for the input scalar value, adds it to the initializer
+    and return multiple created nodes.
+    """
+    return scalar_op_helper(node, 'Div', **kwargs)
+
+@mx_op.register("_power_scalar")
+def convert_pow_scalar(node, **kwargs):
+    """Map MXNet's _pow_scalar operator attributes to onnx's Pow operator.
+    Creates a new node for the input scalar value, adds it to the initializer
+    and return multiple created nodes.
+    """
+    return scalar_op_helper(node, 'Pow', **kwargs)
 
 # Sorting and Searching
 @mx_op.register("argmax")

python/mxnet/contrib/onnx/onnx2mx/_op_translations.py

@@ -742,7 +742,6 @@ def spacetodepth(attrs, inputs, proto_obj):
 
     return "space_to_depth", new_attrs, inputs
 
-
 def hardmax(attrs, inputs, proto_obj):
     """Returns batched one-hot vectors."""
     input_tensor_data = proto_obj.model_metadata.get('input_tensor_data')[0]

tests/python-pytest/onnx/backend_test.py

@@ -71,7 +71,8 @@ def prepare_tests(backend, oper):
     for std_model_test in std_models:
         BACKEND_TESTS.include(std_model_test)
 
-    BACKEND_TESTS.exclude('.*bcast.*')
+    # Tests for scalar ops are in test_node.py
+    BACKEND_TESTS.exclude('.*scalar.*')
 
     return BACKEND_TESTS
 

tests/python-pytest/onnx/test_node.py

@@ -170,6 +170,30 @@ def test_import_export(self):
                 if check_shape:
                     npt.assert_equal(output[0].shape, outputshape)
 
+        input1 = get_rnd((1, 10, 2, 3))
+        ipsym = mx.sym.Variable("input1")
+        for test in test_scalar_ops:
+            if test == 'Add':
+                outsym = 2 + ipsym
+            if test == "Sub":
+                outsym = ipsym - 2
+            if test == "rSub":
+                outsym = ipsym.__rsub__(2)
+            if test == "Mul":
+                outsym = 2 * ipsym
+            if test == "Div":
+                outsym = ipsym / 2
+            if test == "Pow":
+                outsym = ipsym ** 2
+            forward_op = forward_pass(outsym, None, None, ['input1'], input1)
+            converted_model = onnx_mxnet.export_model(outsym, {}, [np.shape(input1)], np.float32,
+                                                      onnx_file_path=outsym.name + ".onnx")
+
+            sym, arg_params, aux_params = onnx_mxnet.import_model(converted_model)
+        result = forward_pass(sym, arg_params, aux_params, ['input1'], input1)
+
+        npt.assert_almost_equal(result, forward_op)
+
     def test_imports(self):
         for test in import_test_cases:
             test_name, onnx_name, inputs, np_op, attrs = test
@@ -182,7 +206,6 @@ def test_imports(self):
                 mxnet_out = bkd_rep.run(inputs)
                 npt.assert_almost_equal(np_out, mxnet_out)
 
-
 # test_case = ("test_case_name", mxnet op, "ONNX_op_name", [input_list], attribute map, MXNet_specific=True/False,
 # fix_attributes = {'modify': {mxnet_attr_name: onnx_attr_name},
 #                   'remove': [attr_name],
@@ -207,6 +230,8 @@ def test_imports(self):
      {'block_size': 2}, False, {}, True, False),
     ("test_softmax", mx.sym.SoftmaxOutput, "Softmax", [get_rnd((1000, 1000)), get_rnd(1000)],
      {'ignore_label': 0, 'use_ignore': False}, True, {}, True, False),
+    ("test_logistic_regression", mx.sym.LogisticRegressionOutput, "Sigmoid",
+     [get_rnd((1000, 1000)), get_rnd((1000, 1000))], {}, True, {}, True, False),
     ("test_fullyconnected", mx.sym.FullyConnected, "Gemm", [get_rnd((4, 3)), get_rnd((4, 3)), get_rnd(4)],
      {'num_hidden': 4, 'name': 'FC'}, True, {}, True, False),
     ("test_lppool1", mx.sym.Pooling, "LpPool", [get_rnd((2, 3, 20, 20))],
@@ -240,12 +265,13 @@ def test_imports(self):
      {'shape': (2, 2), 'low': 0.5, 'high': 1.0}, False, {}, False, True)
 ]
 
+test_scalar_ops = ['Add', 'Sub', 'rSub' 'Mul', 'Div', 'Pow']
+
 # test_case = ("test_case_name", "ONNX_op_name", [input_list], np_op, attribute map)
 import_test_cases = [
     ("test_lpnormalization_default", "LpNormalization", [get_rnd([5, 3, 3, 2])], np.linalg.norm, {'ord':2, 'axis':-1}),
     ("test_lpnormalization_ord1", "LpNormalization", [get_rnd([5, 3, 3, 2])], np.linalg.norm, {'ord':1, 'axis':-1}),
-    ("test_lpnormalization_ord2", "LpNormalization", [get_rnd([5, 3, 3, 2])], np.linalg.norm, {'ord':2, 'axis':1}),
-    ("test_lpnormalization_ord_axis", "LpNormalization", [get_rnd([5, 3, 3, 2])], np.linalg.norm, {'ord':1, 'axis':2})
+    ("test_lpnormalization_ord2", "LpNormalization", [get_rnd([5, 3, 3, 2])], np.linalg.norm, {'ord':2, 'axis':1})
 ]
 
 if __name__ == '__main__':