Updating FasterRCNN to use Task API

.kokoro/github/ubuntu/gpu/build.sh

@@ -65,6 +65,7 @@ then
       keras_cv/models/object_detection/retinanet \
       keras_cv/models/object_detection/yolo_v8 \
       keras_cv/models/object_detection_3d \
+      keras_cv/models/object_detection/faster_rcnn \
       keras_cv/models/segmentation \
       keras_cv/models/stable_diffusion
 else
@@ -79,6 +80,7 @@ else
       keras_cv/models/object_detection/retinanet \
       keras_cv/models/object_detection/yolo_v8 \
       keras_cv/models/object_detection_3d \
+      keras_cv/models/object_detection/faster_rcnn \
       keras_cv/models/segmentation \
       keras_cv/models/stable_diffusion
 fi

demo.py

@@ -0,0 +1,76 @@
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+import keras
+
+import keras_cv
+from keras_cv.models import FasterRCNN
+
+batch_size = 1
+image_shape = (512, 512, 3)
+
+images = keras.ops.ones((batch_size,) + image_shape)
+labels = {
+    "boxes": keras.ops.array(
+        [
+            [
+                [0, 0, 100, 100],
+                [100, 100, 200, 200],
+                [300, 300, 100, 100],
+            ]
+        ],
+        dtype="float32",
+    ),
+    "classes": keras.ops.array([[1, 1, 1]], dtype="float32"),
+}
+
+# Initialize the model
+model = FasterRCNN(
+    batch_size=batch_size,
+    num_classes=2,
+    bounding_box_format="xywh",
+    backbone=keras_cv.models.ResNet50Backbone.from_preset(
+        "resnet50_imagenet",
+        input_shape=image_shape,
+    ),
+)
+
+# Call the model
+outputs = model(images)
+print("outputs")
+for key, value in outputs.items():
+    print(f"{key}: {value.shape}")
+
+# Compile the model
+model.compile(
+    optimizer=keras.optimizers.Adam(),
+    box_loss=keras.losses.Huber(),
+    classification_loss=keras.losses.CategoricalCrossentropy(),
+    rpn_box_loss=keras.losses.Huber(),
+    rpn_classification_loss=keras.losses.BinaryCrossentropy(from_logits=True),
+)
+
+# Compute Loss from the model
+loss = model.compute_loss(x=images, y=labels, y_pred=None, sample_weight=None)
+print(loss)
+
+# Train step
+xs = keras.ops.ones((1, 512, 512, 3), "float32")
+ys = {
+    "classes": keras.ops.array([[1, 1, 1]], dtype="float32"),
+    "boxes": keras.ops.array(
+        [
+            [
+                [0, 0, 100, 100],
+                [100, 100, 200, 200],
+                [300, 300, 100, 100],
+            ]
+        ],
+        dtype="float32",
+    ),
+}
+import tensorflow as tf
+ds = tf.data.Dataset.from_tensor_slices((xs, ys))
+ds = ds.batch(1, drop_remainder=True)
+model.fit(ds, epochs=1)

keras_cv/bounding_box/utils.py

@@ -141,7 +141,7 @@ def _clip_boxes(boxes, box_format, image_shape):
 
     if isinstance(image_shape, list) or isinstance(image_shape, tuple):
         height, width, _ = image_shape
-        max_length = [height, width, height, width]
+        max_length = ops.stack([height, width, height, width], axis=-1)
     else:
         image_shape = ops.cast(image_shape, dtype=boxes.dtype)
         height = image_shape[0]