add: video swin backbone test

keras_cv/models/backbones/video_swin/video_swin_backbone_test.py

@@ -0,0 +1,60 @@
+# Copyright 2023 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+import numpy as np
+import pytest
+
+from keras_cv.backend import keras
+from keras_cv.backend import ops
+from keras_cv.models.backbones.video_swin.video_swin_aliases import VideoSwinSBackbone
+from keras_cv.tests.test_case import TestCase
+
+class TestViTDetBackbone(TestCase):
+    @pytest.mark.large
+    def test_call(self):
+        model = VideoSwinSBackbone()
+        x = np.ones((1, 32, 224, 224, 3))
+        x_out = ops.convert_to_numpy(model(x))
+        num_parameters = sum(
+            np.prod(tuple(x.shape)) for x in model.trainable_variables
+        )
+        self.assertEqual(x_out.shape, (1, 16, 7, 7, 768))
+        self.assertEqual(num_parameters, 49_509_078)
+
+    @pytest.mark.extra_large
+    def teat_save(self):
+        # saving test
+        model = VideoSwinSBackbone()
+        x = np.ones((1, 32, 224, 224, 3))
+        x_out = ops.convert_to_numpy(model(x))
+        path = os.path.join(self.get_temp_dir(), "model.keras")
+        model.save(path)
+        loaded_model = keras.saving.load_model(path)
+        x_out_loaded = ops.convert_to_numpy(loaded_model(x))
+        self.assertAllClose(x_out, x_out_loaded)
+
+    @pytest.mark.extra_large
+    def test_fit(self):
+        model = VideoSwinSBackbone()
+        x = np.ones((1, 32, 224, 224, 3))
+        y = np.zeros((1, 16, 7, 7, 768))
+        model.compile(optimizer="adam", loss="mse", metrics=["mse"])
+        model.fit(x, y, epochs=1)
+
+    def test_pyramid_level_inputs_error(self):
+        model = VideoSwinSBackbone()
+        with self.assertRaises(NotImplementedError, msg="doesn't compute"):
+            model.pyramid_level_inputs