Updating FasterRCNN to use Task API

keras_cv/models/object_detection/faster_rcnn/faster_rcnn.py

@@ -64,8 +64,7 @@ class FasterRCNN(Task):
     Args:
         backbone: `keras.Model`. Must implement the
             `pyramid_level_inputs` property with keys "P2", "P3", "P4", and "P5"
-            and layer names as values. If `None`, defaults to
-            `keras_cv.models.ResNet50Backbone()`.
+            and layer names as values.
         num_classes: the number of classes in your dataset excluding the
             background class. classes should be represented by integers in the
             range [0, num_classes).

keras_cv/models/object_detection/faster_rcnn/faster_rcnn_test.py

@@ -0,0 +1,104 @@
+# Copyright 2022 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 pytest
+import tensorflow as tf
+from absl.testing import parameterized
+
+from keras_cv.backend import keras
+from keras_cv.models import ResNet18V2Backbone
+from keras_cv.models.object_detection.__test_utils__ import (
+    _create_bounding_box_dataset,
+)
+from keras_cv.models.object_detection.faster_rcnn.faster_rcnn import FasterRCNN
+from keras_cv.tests.test_case import TestCase
+
+
+class FasterRCNNTest(TestCase):
+    # TODO(ianstenbit): Make FasterRCNN support shapes that are not multiples
+    # of 128, perhaps by adding a flag to the anchor generator for whether to
+    # include anchors centered outside of the image. (RetinaNet does use those,
+    # while FasterRCNN doesn't). For more context on why this is the case, see
+    # https://github.com/keras-team/keras-cv/pull/1882
+    @parameterized.parameters(
+        ((2, 640, 384, 3),),
+        ((2, 512, 512, 3),),
+        ((2, 128, 128, 3),),
+    )
+    def test_faster_rcnn_infer(self, batch_shape):
+        model = FasterRCNN(
+            num_classes=80,
+            bounding_box_format="xyxy",
+            backbone=ResNet18V2Backbone(),
+        )
+        images = tf.random.normal(batch_shape)
+        outputs = model(images, training=False)
+        # 1000 proposals in inference
+        self.assertAllEqual([2, 1000, 81], outputs[1].shape)
+        self.assertAllEqual([2, 1000, 4], outputs[0].shape)
+
+    @parameterized.parameters(
+        ((2, 640, 384, 3),),
+        ((2, 512, 512, 3),),
+        ((2, 128, 128, 3),),
+    )
+    def test_faster_rcnn_train(self, batch_shape):
+        model = FasterRCNN(
+            num_classes=80,
+            bounding_box_format="xyxy",
+            backbone=ResNet18V2Backbone(),
+        )
+        images = tf.random.normal(batch_shape)
+        outputs = model(images, training=True)
+        self.assertAllEqual([2, 1000, 81], outputs[1].shape)
+        self.assertAllEqual([2, 1000, 4], outputs[0].shape)
+
+    def test_invalid_compile(self):
+        model = FasterRCNN(
+            num_classes=80,
+            bounding_box_format="yxyx",
+            backbone=ResNet18V2Backbone(),
+        )
+        with self.assertRaisesRegex(ValueError, "only accepts"):
+            model.compile(rpn_box_loss="binary_crossentropy")
+        with self.assertRaisesRegex(ValueError, "only accepts"):
+            model.compile(
+                rpn_classification_loss=keras.losses.BinaryCrossentropy(
+                    from_logits=False
+                )
+            )
+
+    @pytest.mark.large  # Fit is slow, so mark these large.
+    def test_faster_rcnn_with_dictionary_input_format(self):
+        faster_rcnn = FasterRCNN(
+            num_classes=20,
+            bounding_box_format="xywh",
+            backbone=ResNet18V2Backbone(),
+        )
+
+        images, boxes = _create_bounding_box_dataset("xywh")
+        dataset = tf.data.Dataset.from_tensor_slices(
+            {"images": images, "bounding_boxes": boxes}
+        ).batch(5, drop_remainder=True)
+
+        faster_rcnn.compile(
+            optimizer=keras.optimizers.Adam(),
+            box_loss="Huber",
+            classification_loss="SparseCategoricalCrossentropy",
+            rpn_box_loss="Huber",
+            rpn_classification_loss="BinaryCrossentropy",
+        )
+
+        faster_rcnn.fit(dataset, epochs=1)
+        faster_rcnn.evaluate(dataset)