Pylint

Signed-off-by: Tom Wildenhain <[email protected]>

Author: TomWildenhain-Microsoft

tests/backend_test_base.py

@@ -95,14 +95,14 @@ def run_onnxruntime(self, model_path, inputs, output_names, use_custom_ops=False
         results = m.run(output_names, inputs)
         return results
 
-    def run_backend(self, g, outputs, input_dict, large_model=False, postfix=""):
+    def run_backend(self, g, outputs, input_dict, large_model=False, postfix="", use_custom_ops=False):
         tensor_storage = ExternalTensorStorage() if large_model else None
         model_proto = g.make_model("test", external_tensor_storage=tensor_storage)
         model_path = self.save_onnx_model(model_proto, input_dict, external_tensor_storage=tensor_storage,
                                           postfix=postfix)
 
         if self.config.backend == "onnxruntime":
-            y = self.run_onnxruntime(model_path, input_dict, outputs)
+            y = self.run_onnxruntime(model_path, input_dict, outputs, use_custom_ops)
         elif self.config.backend == "caffe2":
             y = self.run_onnxcaffe2(model_proto, input_dict)
         else:
@@ -307,7 +307,8 @@ def get_dtype(info):
     def run_test_case(self, func, feed_dict, input_names_with_port, output_names_with_port,
                       rtol=1e-07, atol=1e-5, mtol=None, convert_var_to_const=True, constant_fold=True,
                       check_value=True, check_shape=True, check_dtype=True, process_args=None, onnx_feed_dict=None,
-                      graph_validator=None, as_session=False, large_model=False, premade_placeholders=False):
+                      graph_validator=None, as_session=False, large_model=False, premade_placeholders=False,
+                      use_custom_ops=False):
         test_tf = not self.config.skip_tf_tests
         test_tflite = not self.config.skip_tflite_tests
         run_tfl_consistency_test = test_tf and test_tflite and self.config.run_tfl_consistency_test
@@ -347,7 +348,8 @@ def run_test_case(self, func, feed_dict, input_names_with_port, output_names_wit
                                      initialized_tables=initialized_tables,
                                      **process_args)
                 g = optimizer.optimize_graph(g, catch_errors=False)
-                actual = self.run_backend(g, output_names_with_port, onnx_feed_dict, large_model)
+                actual = self.run_backend(g, output_names_with_port, onnx_feed_dict, large_model,
+                                          use_custom_ops=use_custom_ops)
 
             self.assert_results_equal(expected, actual, rtol, atol, mtol, check_value, check_shape, check_dtype)
             self.assert_shapes_correct(g, self.config.allow_missing_shapes, not self.config.skip_onnx_checker)
@@ -377,7 +379,8 @@ def run_test_case(self, func, feed_dict, input_names_with_port, output_names_wit
                                  **tfl_process_args)
             g = optimizer.optimize_graph(g)
             onnx_feed_dict_without_port = {k.split(':')[0]: v for k, v in onnx_feed_dict.items()}
-            onnx_tfl_res = self.run_backend(g, tfl_outputs, onnx_feed_dict_without_port, postfix="_from_tflite")
+            onnx_tfl_res = self.run_backend(g, tfl_outputs, onnx_feed_dict_without_port,
+                                            postfix="_from_tflite", use_custom_ops=use_custom_ops)
 
             self.assert_results_equal(tfl_res, onnx_tfl_res, rtol, atol, mtol, check_value, check_shape, check_dtype)
             self.assert_shapes_correct(g, self.config.allow_missing_shapes, not self.config.skip_onnx_checker)

tests/test_api.py

@@ -14,6 +14,7 @@
 
 import numpy as np
 import tensorflow as tf
+from onnx import helper
 
 from common import check_tf_min_version, unittest_main, requires_custom_ops
 from backend_test_base import Tf2OnnxBackendTestBase
@@ -82,19 +83,21 @@ def test_keras_api_large(self):
     @check_tf_min_version("2.0")
     def test_keras_hashtable(self):
 
-        featCols = [tf.feature_column.numeric_column("f_inp", dtype=tf.float32),
+        feature_cols = [
+            tf.feature_column.numeric_column("f_inp", dtype=tf.float32),
             tf.feature_column.indicator_column(
                 tf.feature_column.categorical_column_with_vocabulary_list("s_inp", ["a", "b", "z"], num_oov_buckets=1)
-            )]
-        featureLayer = tf.keras.layers.DenseFeatures(featCols)
+            )
+        ]
+        feature_layer = tf.keras.layers.DenseFeatures(feature_cols)
 
-        inputDict = {}
-        inputDict["f_inp"] = tf.keras.Input(name="f_inp", shape=(1,), dtype=tf.float32)
-        inputDict["s_inp"] = tf.keras.Input(name="s_inp", shape=(1,), dtype=tf.string)
+        input_dict = {}
+        input_dict["f_inp"] = tf.keras.Input(name="f_inp", shape=(1,), dtype=tf.float32)
+        input_dict["s_inp"] = tf.keras.Input(name="s_inp", shape=(1,), dtype=tf.string)
 
-        inputs = [input for input in inputDict.values()]
-        standardFeatures = featureLayer(inputDict)
-        hidden1 = tf.keras.layers.Dense(512, activation='relu')(standardFeatures)
+        inputs = list(input_dict.values())
+        standard_features = feature_layer(input_dict)
+        hidden1 = tf.keras.layers.Dense(512, activation='relu')(standard_features)
         output = tf.keras.layers.Dense(10, activation='softmax')(hidden1)
         model = tf.keras.Model(inputs=inputs, outputs=output)
         model.compile(optimizer='adam', loss=tf.keras.losses.mean_squared_error)
@@ -106,7 +109,6 @@ def test_keras_hashtable(self):
                 tf.TensorSpec((None, 1), tf.string, name="s_inp"))
         output_path = os.path.join(self.test_data_directory, "model.onnx")
 
-        from onnx import helper
         model_proto, _ = tf2onnx.convert.from_keras(
             model, input_signature=spec, opset=self.config.opset, output_path=output_path,
             extra_opset=[helper.make_opsetid("ai.onnx.contrib", 1)])

tests/test_string_ops.py

@@ -135,17 +135,8 @@ def func(text):
     def _run_test_case(self, func, output_names_with_port, feed_dict, **kwargs):
         extra_opset = [utils.make_opsetid(constants.CONTRIB_OPS_DOMAIN, 1)]
         process_args = {"extra_opset": extra_opset}
-        return self.run_test_case(func, feed_dict, [], output_names_with_port, process_args=process_args, **kwargs)
-
-    def run_onnxruntime(self, model_path, inputs, output_names):
-        """Run test against onnxruntime backend."""
-        from onnxruntime_customops import get_library_path
-        import onnxruntime as rt
-        opt = rt.SessionOptions()
-        opt.register_custom_ops_library(get_library_path())
-        m = rt.InferenceSession(model_path, opt)
-        results = m.run(output_names, inputs)
-        return results
+        return self.run_test_case(func, feed_dict, [], output_names_with_port,
+                                  use_custom_ops=True, process_args=process_args, **kwargs)
 
     @requires_custom_ops("WordpieceTokenizer")
     @check_tf_min_version("2.0", "tensorflow_text")