ONNX ops: norm exported and lpnormalization imported (apache#13806)

* ReduceL1, l2 export, lpnormalization import added

* fix

* fix

* fix

* fix

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

@@ -1716,6 +1716,40 @@ def convert_logsoftmax(node, **kwargs):
     )
     return [node]
 
+@mx_op.register("norm")
+def convert_norm(node, **kwargs):
+    """Map MXNet's norm operator attributes to onnx's ReduceL1 and ReduceL2 operators
+    and return the created node.
+    """
+    name, input_nodes, attrs = get_inputs(node, kwargs)
+
+    mx_axis = attrs.get("axis", None)
+    axes = convert_string_to_list(str(mx_axis)) if mx_axis else None
+
+    keepdims = get_boolean_attribute_value(attrs, "keepdims")
+    ord = int(attrs.get("ord", 2))
+
+    onnx_op_name = "ReduceL1" if ord == 1 else "ReduceL2"
+
+    if axes:
+        reduce_node = onnx.helper.make_node(
+            onnx_op_name,
+            input_nodes,
+            [name],
+            axes=axes,
+            keepdims=keepdims,
+            name=name
+        )
+        return [reduce_node]
+    else:
+        reduce_node = onnx.helper.make_node(
+            onnx_op_name,
+            input_nodes,
+            [name],
+            keepdims=keepdims,
+            name=name
+        )
+        return [reduce_node]
 
 @mx_op.register("_sample_multinomial")
 def convert_multinomial(node, **kwargs):

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

@@ -37,7 +37,7 @@
 from ._op_translations import reduce_sum_square, reduce_l1, reduce_l2, max_roi_pooling
 from ._op_translations import log_softmax, softsign, lesser, greater, equal
 from ._op_translations import logical_and, logical_or, logical_xor, logical_not
-from ._op_translations import mean, depthtospace, spacetodepth
+from ._op_translations import mean, depthtospace, spacetodepth, lpnormalization
 
 # convert_map defines maps of ONNX operator names to converter functor(callable)
 # defined in the op_translations module.
@@ -146,5 +146,6 @@
     'LpPool'            : lp_pooling,
     'DepthToSpace'      : depthtospace,
     'SpaceToDepth'      : spacetodepth,
-    'Hardmax'           : hardmax
+    'Hardmax'           : hardmax,
+    'LpNormalization'   : lpnormalization
 }

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

@@ -755,3 +755,12 @@ def hardmax(attrs, inputs, proto_obj):
     one_hot = symbol.one_hot(amax, depth=new_shape[-1])
     hardmax_op = symbol.reshape(one_hot, input_shape)
     return hardmax_op, attrs, inputs
+
+def lpnormalization(attrs, inputs, proto_obj):
+    """ONNX does not have eps attribute, so cannot map it to L2normalization in MXNet
+     without that, it works as norm operator discussion in PR:
+     https://github.com/onnx/onnx/pull/1330"""
+    new_attrs = translation_utils._fix_attribute_names(attrs, {'p': 'ord'})
+    axis = int(attrs.get("axis", -1))
+    new_attrs.update(axis=axis)
+    return 'norm', new_attrs, inputs

tests/python-pytest/onnx/test_cases.py

@@ -76,11 +76,11 @@
              'test_selu_default',
              'test_elu',
              'test_max_',
-             'test_softplus'
+             'test_softplus',
+             'test_reduce_'
              ],
     'import': ['test_gather',
                'test_softsign',
-               'test_reduce_',
                'test_mean',
                'test_averagepool_1d',
                'test_averagepool_2d_pads_count_include_pad',

tests/python-pytest/onnx/test_node.py

@@ -106,35 +106,35 @@ def forward_pass(sym, arg, aux, data_names, input_data):
     return mod.get_outputs()[0].asnumpy()
 
 
-class TestNode(unittest.TestCase):
-    """ Tests for models.
-    Tests are dynamically added.
-    Therefore edit test_models to add more tests.
-    """
+def get_input_tensors(input_data):
+    input_tensor = []
+    input_names = []
+    input_sym = []
+    for idx, ip in enumerate(input_data):
+        name = "input" + str(idx + 1)
+        input_sym.append(mx.sym.Variable(name))
+        input_names.append(name)
+        input_tensor.append(helper.make_tensor_value_info(name,
+                                                          TensorProto.FLOAT, shape=np.shape(ip)))
+    return input_names, input_tensor, input_sym
 
-    def test_import_export(self):
-        def get_input_tensors(input_data):
-            input_tensor = []
-            input_names = []
-            input_sym = []
-            for idx, ip in enumerate(input_data):
-                name = "input" + str(idx + 1)
-                input_sym.append(mx.sym.Variable(name))
-                input_names.append(name)
-                input_tensor.append(helper.make_tensor_value_info(name,
-                                                                  TensorProto.FLOAT, shape=np.shape(ip)))
-            return input_names, input_tensor, input_sym
 
-        def get_onnx_graph(testname, input_names, inputs, output_name, output_shape, attr):
-            outputs = [helper.make_tensor_value_info("output", TensorProto.FLOAT, shape=output_shape)]
+def get_onnx_graph(testname, input_names, inputs, output_name, output_shape, attr):
+    outputs = [helper.make_tensor_value_info("output", TensorProto.FLOAT, shape=output_shape)]
 
-            nodes = [helper.make_node(output_name, input_names, ["output"], **attr)]
+    nodes = [helper.make_node(output_name, input_names, ["output"], **attr)]
 
-            graph = helper.make_graph(nodes, testname, inputs, outputs)
+    graph = helper.make_graph(nodes, testname, inputs, outputs)
 
-            model = helper.make_model(graph)
-            return model
+    model = helper.make_model(graph)
+    return model
 
+class TestNode(unittest.TestCase):
+    """ Tests for models.
+    Tests are dynamically added.
+    Therefore edit test_models to add more tests.
+    """
+    def test_import_export(self):
         for test in test_cases:
             test_name, mxnet_op, onnx_name, inputs, attrs, mxnet_specific, fix_attrs, check_value, check_shape = test
             with self.subTest(test_name):
@@ -161,6 +161,18 @@ def get_onnx_graph(testname, input_names, inputs, output_name, output_shape, att
                 if check_shape:
                     npt.assert_equal(output[0].shape, outputshape)
 
+    def test_imports(self):
+        for test in import_test_cases:
+            test_name, onnx_name, inputs, np_op, attrs = test
+            with self.subTest(test_name):
+                names, input_tensors, inputsym = get_input_tensors(inputs)
+                np_out = [np_op(*inputs, **attrs)]
+                output_shape = np.shape(np_out)
+                onnx_model = get_onnx_graph(test_name, names, input_tensors, onnx_name, output_shape, attrs)
+                bkd_rep = backend.prepare(onnx_model, operation='import')
+                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},
@@ -211,5 +223,13 @@ def get_onnx_graph(testname, input_names, inputs, output_name, output_shape, att
      {'shape': (10,)}, False, {'modify': {'shape': 'sample_size'}}, False, True)
 ]
 
+# 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})
+]
+
 if __name__ == '__main__':
     unittest.main()