cleanup of deprecated test methods

PiperOrigin-RevId: 725124395

Author: tensorflower-gardener

research/object_detection/anchor_generators/flexible_grid_anchor_generator_test.py

@@ -74,7 +74,7 @@ def test_construct_normalized_anchors_fails_with_unit_dimensions(self):
     anchor_generator = fg.FlexibleGridAnchorGenerator(
         [(32.0,)], [(1.0,)], [(32, 32),], [(16, 16),],
         normalize_coordinates=True)
-    with self.assertRaisesRegexp(ValueError, 'Normalized coordinates'):
+    with self.assertRaisesRegex(ValueError, 'Normalized coordinates'):
       anchor_generator.generate(
           feature_map_shape_list=[(2, 2)], im_height=1, im_width=1)
 

research/object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py

@@ -78,7 +78,7 @@ def test_construct_normalized_anchors_fails_with_unit_dimensions(self):
     anchor_generator = mg.MultiscaleGridAnchorGenerator(
         min_level=5, max_level=5, anchor_scale=1.0, aspect_ratios=[1.0],
         scales_per_octave=1, normalize_coordinates=True)
-    with self.assertRaisesRegexp(ValueError, 'Normalized coordinates'):
+    with self.assertRaisesRegex(ValueError, 'Normalized coordinates'):
       anchor_generator.generate(
           feature_map_shape_list=[(2, 2)], im_height=1, im_width=1)
 

research/object_detection/core/prefetcher_tf1_test.py

@@ -60,9 +60,9 @@ def test_prefetch_tensors_with_fully_defined_shapes(self):
       with slim.queues.QueueRunners(sess):
         for _ in range(num_batches):
           results = sess.run(tensor_dict)
-          self.assertEquals(results['image'].shape,
+          self.assertEqual(results['image'].shape,
                             (batch_size, image_size, image_size, 3))
-          self.assertEquals(results['label'].shape, (batch_size, 1))
+          self.assertEqual(results['label'].shape, (batch_size, 1))
         with self.assertRaises(tf.errors.OutOfRangeError):
           sess.run(tensor_dict)
 
@@ -98,9 +98,9 @@ def test_prefetch_tensors_with_partially_defined_shapes(self):
       with slim.queues.QueueRunners(sess):
         for _ in range(num_batches):
           results = sess.run(tensor_dict)
-          self.assertEquals(results['image'].shape,
+          self.assertEqual(results['image'].shape,
                             (batch_size, image_size, image_size, 3))
-          self.assertEquals(results['label'].shape, (batch_size, 1))
+          self.assertEqual(results['label'].shape, (batch_size, 1))
         with self.assertRaises(tf.errors.OutOfRangeError):
           sess.run(tensor_dict)
 

research/object_detection/core/target_assigner_test.py

@@ -462,7 +462,7 @@ def test_raises_error_on_incompatible_groundtruth_boxes_and_labels(self):
     groundtruth_labels = tf.constant([[0, 1, 0, 0, 0, 0, 0],
                                       [0, 0, 0, 0, 0, 1, 0],
                                       [0, 0, 0, 1, 0, 0, 0]], tf.float32)
-    with self.assertRaisesRegexp(ValueError, 'Unequal shapes'):
+    with self.assertRaisesRegex(ValueError, 'Unequal shapes'):
       target_assigner.assign(
           priors,
           boxes,

research/object_detection/exporter_tf1_test.py

@@ -665,7 +665,7 @@ def test_raise_runtime_error_on_images_with_different_sizes(self):
       keypoints = inference_graph.get_tensor_by_name('detection_keypoints:0')
       masks = inference_graph.get_tensor_by_name('detection_masks:0')
       num_detections = inference_graph.get_tensor_by_name('num_detections:0')
-      with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
+      with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
                                    'TensorArray.*shape'):
         sess.run(
             [boxes, scores, classes, keypoints, masks, num_detections],

research/object_detection/model_lib_tf1_test.py

@@ -226,7 +226,7 @@ def test_model_fn_in_train_mode_freeze_all_variables(self):
     """Tests model_fn TRAIN mode with all variables frozen."""
     configs = _get_configs_for_model(MODEL_NAME_FOR_TEST)
     configs['train_config'].freeze_variables.append('.*')
-    with self.assertRaisesRegexp(ValueError, 'No variables to optimize'):
+    with self.assertRaisesRegex(ValueError, 'No variables to optimize'):
       self._assert_model_fn_for_train_eval(configs, 'train')
 
   def test_model_fn_in_train_mode_freeze_all_included_variables(self):
@@ -235,7 +235,7 @@ def test_model_fn_in_train_mode_freeze_all_included_variables(self):
     train_config = configs['train_config']
     train_config.update_trainable_variables.append('FeatureExtractor')
     train_config.freeze_variables.append('.*')
-    with self.assertRaisesRegexp(ValueError, 'No variables to optimize'):
+    with self.assertRaisesRegex(ValueError, 'No variables to optimize'):
       self._assert_model_fn_for_train_eval(configs, 'train')
 
   def test_model_fn_in_train_mode_freeze_box_predictor(self):

research/object_detection/utils/config_util_test.py

@@ -866,22 +866,22 @@ def testCheckAndParseInputConfigKey(self):
     self.assertEqual(input_name, None)
     self.assertEqual(field_name, "label_map_path")
 
-    with self.assertRaisesRegexp(ValueError,
+    with self.assertRaisesRegex(ValueError,
                                  "Invalid key format when overriding configs."):
       config_util.check_and_parse_input_config_key(
           configs, "train_input_config:shuffle")
 
-    with self.assertRaisesRegexp(
+    with self.assertRaisesRegex(
         ValueError, "Invalid key_name when overriding input config."):
       config_util.check_and_parse_input_config_key(
           configs, "invalid_key_name:train_name:shuffle")
 
-    with self.assertRaisesRegexp(
+    with self.assertRaisesRegex(
         ValueError, "Invalid input_name when overriding input config."):
       config_util.check_and_parse_input_config_key(
           configs, "eval_input_configs:unknown_eval_name:shuffle")
 
-    with self.assertRaisesRegexp(
+    with self.assertRaisesRegex(
         ValueError, "Invalid field_name when overriding input config."):
       config_util.check_and_parse_input_config_key(
           configs, "eval_input_configs:eval_2:unknown_field_name")
@@ -919,7 +919,7 @@ def testUpdateInputReaderConfigErrors(self):
     _write_config(pipeline_config, pipeline_config_path)
     configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
 
-    with self.assertRaisesRegexp(ValueError,
+    with self.assertRaisesRegex(ValueError,
                                  "Duplicate input name found when overriding."):
       config_util.update_input_reader_config(
           configs,
@@ -928,7 +928,7 @@ def testUpdateInputReaderConfigErrors(self):
           field_name="shuffle",
           value=False)
 
-    with self.assertRaisesRegexp(
+    with self.assertRaisesRegex(
         ValueError, "Input name name_not_exist not found when overriding."):
       config_util.update_input_reader_config(
           configs,
@@ -937,7 +937,7 @@ def testUpdateInputReaderConfigErrors(self):
           field_name="shuffle",
           value=False)
 
-    with self.assertRaisesRegexp(ValueError,
+    with self.assertRaisesRegex(ValueError,
                                  "Unknown input config overriding."):
       config_util.update_input_reader_config(
           configs,

research/object_detection/utils/metrics_test.py

@@ -91,8 +91,8 @@ def test_compute_precision_recall_and_ap_no_groundtruth(self):
     expected_precision = None
     expected_recall = None
     precision, recall = metrics.compute_precision_recall(scores, labels, num_gt)
-    self.assertEquals(precision, expected_precision)
-    self.assertEquals(recall, expected_recall)
+    self.assertEqual(precision, expected_precision)
+    self.assertEqual(recall, expected_recall)
     ap = metrics.compute_average_precision(precision, recall)
     self.assertTrue(np.isnan(ap))
 
@@ -137,10 +137,10 @@ def test_compute_median_rank_at_k(self):
     median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
     median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
 
-    self.assertEquals(median_ranks_1, 0)
-    self.assertEquals(median_ranks_3, 0.5)
-    self.assertEquals(median_ranks_3_bool, 0.5)
-    self.assertEquals(median_ranks_5, 0.5)
+    self.assertEqual(median_ranks_1, 0)
+    self.assertEqual(median_ranks_3, 0.5)
+    self.assertEqual(median_ranks_3_bool, 0.5)
+    self.assertEqual(median_ranks_5, 0.5)
 
 
 if __name__ == '__main__':

research/object_detection/utils/np_box_list_test.py

@@ -132,7 +132,7 @@ def test_num_boxes(self):
     boxes = np.array([[0., 0., 100., 100.], [10., 30., 50., 70.]], dtype=float)
     boxlist = np_box_list.BoxList(boxes)
     expected_num_boxes = 2
-    self.assertEquals(boxlist.num_boxes(), expected_num_boxes)
+    self.assertEqual(boxlist.num_boxes(), expected_num_boxes)
 
 
 if __name__ == '__main__':

research/object_detection/utils/np_box_mask_list_test.py

@@ -179,7 +179,7 @@ def test_num_boxes(self):
     box_mask_list = np_box_mask_list.BoxMaskList(
         box_data=boxes, mask_data=masks)
     expected_num_boxes = 2
-    self.assertEquals(box_mask_list.num_boxes(), expected_num_boxes)
+    self.assertEqual(box_mask_list.num_boxes(), expected_num_boxes)
 
 
 if __name__ == '__main__':