Tutorial error: Image augmentation layers are expecting inputs to be rank 3 (HWC) or 4D (NHWC) tensors. Got shape: <unknown>

[apiszcz]

The images and bounding boxes display properly. When I attempt to any augment function the following stack trace is produced: I must have something setup incorrectly, however the visualize dataset function works. How do i determine where the the issue/problem is?

Following this documentation: https://keras.io/guides/keras_cv/object_detection_keras_cv/#training-our-model

    Image augmentation layers are expecting inputs to be rank 3 (HWC) or 4D (NHWC) tensors. Got shape: <unknown>

    Arguments received by AutoContrast.call():
      • inputs={'images': "<tf.Tensor 'args_2:0' shape=<unknown> dtype=float32>", 'bounding_boxes': {'classes': 'tf.RaggedTensor(values=Tensor("RaggedFromVariant_1/RaggedTensorFromVariant:1", shape=(None,), dtype=float32), row_splits=Tensor("RaggedFromVariant_1/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))', 'boxes': 'tf.RaggedTensor(values=tf.RaggedTensor(values=Tensor("Cast:0", shape=(None,), dtype=float32), row_splits=Tensor("RaggedFromVariant/RaggedTensorFromVariant:1", shape=(None,), dtype=int64)), row_splits=Tensor("RaggedFromVariant/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))'}}

def load_image(image_path): image = tf.io.read_file(image_path) image = tf.image.decode_image(image, channels=3) return image

def load_dataset(image_path, classes, bbox):

Read Image

image = load_image(image_path)
bounding_boxes = {
    "classes": tf.cast(classes, dtype=tf.float32),
    "boxes": bbox,
}
return {"images": tf.cast(image, tf.float32), "bounding_boxes": bounding_boxes}

augmenters = [ keras_cv.layers.AutoContrast((0, 255)), ]

def create_augmenter_fn(augmenters): def augmenter_fn(inputs): for augmenter in augmenters: inputs = augmenter(inputs) return inputs

return augmenter_fn

augmenter_fn = create_augmenter_fn(augmenters)

train_ds = train_data.map(load_dataset, num_parallel_calls=tf.data.AUTOTUNE) train_ds = train_ds.shuffle(BATCH_SIZE * 4) train_ds = train_ds.ragged_batch(BATCH_SIZE, drop_remainder=True) train_ds = train_ds.map(augmenter_fn, num_parallel_calls=tf_data.AUTOTUNE)