Speedup the slow tests of detection models (#3929)

* Speed up keypoint and retina.

* Update keypoint expected file

* Speed up fasterrcnn_resnet50_fpn.

* Speed up maskrcnn_resnet50_fpn.

* Updating params to resolve flakiness.

* limit runs to those 4 tests

* Relaxing precision to resolve flakiness

* Undo test filtering

test/common_utils.py

@@ -311,7 +311,7 @@ class TestCase(unittest.TestCase):
             results = m(*args)
         with freeze_rng_state():
             results_from_imported = m_import(*args)
-        self.assertEqual(results, results_from_imported, prec=3e-5)
+        self.assertEqual(results, results_from_imported, prec=3e-4)
 
 
 @contextlib.contextmanager

test/test_models.py

@@ -81,7 +81,30 @@ _model_params = {
         'input_shape': (1, 3, 299, 299)
     },
     'retinanet_resnet50_fpn': {
+        'num_classes': 20,
         'score_thresh': 0.01,
+        'min_size': 224,
+        'max_size': 224,
+        'input_shape': (3, 224, 224),
+    },
+    'keypointrcnn_resnet50_fpn': {
+        'num_classes': 2,
+        'min_size': 224,
+        'max_size': 224,
+        'box_score_thresh': 0.15,
+        'input_shape': (3, 224, 224),
+    },
+    'fasterrcnn_resnet50_fpn': {
+        'num_classes': 20,
+        'min_size': 224,
+        'max_size': 224,
+        'input_shape': (3, 224, 224),
+    },
+    'maskrcnn_resnet50_fpn': {
+        'num_classes': 10,
+        'min_size': 224,
+        'max_size': 224,
+        'input_shape': (3, 224, 224),
     },
     'fasterrcnn_mobilenet_v3_large_fpn': {
         'box_score_thresh': 0.02076,