search

waymo-research / waymo-open-dataset

Waymo Open Dataset
https://www.waymo.com/open
Other
2.67k stars 608 forks source link

motion prediction dataset 1.2.0 - hd map feature description for tf_example #618

Closed kharitonov-ivan closed 1 year ago

kharitonov-ivan commented 1 year ago

Hello!

I attempted to parse the tf_example scenario from gcs://waymo_open_dataset_motion_v_1_2_0/uncompressed/tf_example using the provided code. However, it only works for state features and traffic lights. When I tried to access roadmap_features, I encountered an error. I suspect the format for roadgraph_features has been updated. If that's the case, could you please provide an updated description?

I use this feature description:

# Example field definition
roadgraph_features = {
    'roadgraph_samples/dir':
        tf.io.FixedLenFeature([20000, 3], tf.float32, default_value=None),
    'roadgraph_samples/id':
        tf.io.FixedLenFeature([20000, 1], tf.int64, default_value=None),
    'roadgraph_samples/type':
        tf.io.FixedLenFeature([20000, 1], tf.int64, default_value=None),
    'roadgraph_samples/valid':
        tf.io.FixedLenFeature([20000, 1], tf.int64, default_value=None),
    'roadgraph_samples/xyz':
        tf.io.FixedLenFeature([20000, 3], tf.float32, default_value=None),
}

# Features of other agents.
state_features = {
    'state/id':
        tf.io.FixedLenFeature([128], tf.float32, default_value=None),
    'state/type':
        tf.io.FixedLenFeature([128], tf.float32, default_value=None),
    'state/is_sdc':
        tf.io.FixedLenFeature([128], tf.int64, default_value=None),
    'state/tracks_to_predict':
        tf.io.FixedLenFeature([128], tf.int64, default_value=None),
    'state/current/bbox_yaw':
        tf.io.FixedLenFeature([128, 1], tf.float32, default_value=None),
    'state/current/height':
        tf.io.FixedLenFeature([128, 1], tf.float32, default_value=None),
    'state/current/length':
        tf.io.FixedLenFeature([128, 1], tf.float32, default_value=None),
    'state/current/timestamp_micros':
        tf.io.FixedLenFeature([128, 1], tf.int64, default_value=None),
    'state/current/valid':
        tf.io.FixedLenFeature([128, 1], tf.int64, default_value=None),
    'state/current/vel_yaw':
        tf.io.FixedLenFeature([128, 1], tf.float32, default_value=None),
    'state/current/velocity_x':
        tf.io.FixedLenFeature([128, 1], tf.float32, default_value=None),
    'state/current/velocity_y':
        tf.io.FixedLenFeature([128, 1], tf.float32, default_value=None),
    'state/current/width':
        tf.io.FixedLenFeature([128, 1], tf.float32, default_value=None),
    'state/current/x':
        tf.io.FixedLenFeature([128, 1], tf.float32, default_value=None),
    'state/current/y':
        tf.io.FixedLenFeature([128, 1], tf.float32, default_value=None),
    'state/current/z':
        tf.io.FixedLenFeature([128, 1], tf.float32, default_value=None),
    'state/future/bbox_yaw':
        tf.io.FixedLenFeature([128, 80], tf.float32, default_value=None),
    'state/future/height':
        tf.io.FixedLenFeature([128, 80], tf.float32, default_value=None),
    'state/future/length':
        tf.io.FixedLenFeature([128, 80], tf.float32, default_value=None),
    'state/future/timestamp_micros':
        tf.io.FixedLenFeature([128, 80], tf.int64, default_value=None),
    'state/future/valid':
        tf.io.FixedLenFeature([128, 80], tf.int64, default_value=None),
    'state/future/vel_yaw':
        tf.io.FixedLenFeature([128, 80], tf.float32, default_value=None),
    'state/future/velocity_x':
        tf.io.FixedLenFeature([128, 80], tf.float32, default_value=None),
    'state/future/velocity_y':
        tf.io.FixedLenFeature([128, 80], tf.float32, default_value=None),
    'state/future/width':
        tf.io.FixedLenFeature([128, 80], tf.float32, default_value=None),
    'state/future/x':
        tf.io.FixedLenFeature([128, 80], tf.float32, default_value=None),
    'state/future/y':
        tf.io.FixedLenFeature([128, 80], tf.float32, default_value=None),
    'state/future/z':
        tf.io.FixedLenFeature([128, 80], tf.float32, default_value=None),
    'state/past/bbox_yaw':
        tf.io.FixedLenFeature([128, 10], tf.float32, default_value=None),
    'state/past/height':
        tf.io.FixedLenFeature([128, 10], tf.float32, default_value=None),
    'state/past/length':
        tf.io.FixedLenFeature([128, 10], tf.float32, default_value=None),
    'state/past/timestamp_micros':
        tf.io.FixedLenFeature([128, 10], tf.int64, default_value=None),
    'state/past/valid':
        tf.io.FixedLenFeature([128, 10], tf.int64, default_value=None),
    'state/past/vel_yaw':
        tf.io.FixedLenFeature([128, 10], tf.float32, default_value=None),
    'state/past/velocity_x':
        tf.io.FixedLenFeature([128, 10], tf.float32, default_value=None),
    'state/past/velocity_y':
        tf.io.FixedLenFeature([128, 10], tf.float32, default_value=None),
    'state/past/width':
        tf.io.FixedLenFeature([128, 10], tf.float32, default_value=None),
    'state/past/x':
        tf.io.FixedLenFeature([128, 10], tf.float32, default_value=None),
    'state/past/y':
        tf.io.FixedLenFeature([128, 10], tf.float32, default_value=None),
    'state/past/z':
        tf.io.FixedLenFeature([128, 10], tf.float32, default_value=None),
}

traffic_light_features = {
    'traffic_light_state/current/state':
        tf.io.FixedLenFeature([1, 16], tf.int64, default_value=None),
    'traffic_light_state/current/valid':
        tf.io.FixedLenFeature([1, 16], tf.int64, default_value=None),
    'traffic_light_state/current/x':
        tf.io.FixedLenFeature([1, 16], tf.float32, default_value=None),
    'traffic_light_state/current/y':
        tf.io.FixedLenFeature([1, 16], tf.float32, default_value=None),
    'traffic_light_state/current/z':
        tf.io.FixedLenFeature([1, 16], tf.float32, default_value=None),
    'traffic_light_state/past/state':
        tf.io.FixedLenFeature([10, 16], tf.int64, default_value=None),
    'traffic_light_state/past/valid':
        tf.io.FixedLenFeature([10, 16], tf.int64, default_value=None),
    'traffic_light_state/past/x':
        tf.io.FixedLenFeature([10, 16], tf.float32, default_value=None),
    'traffic_light_state/past/y':
        tf.io.FixedLenFeature([10, 16], tf.float32, default_value=None),
    'traffic_light_state/past/z':
        tf.io.FixedLenFeature([10, 16], tf.float32, default_value=None),
}

And get this error:

InvalidArgumentError: {{function_node __wrapped__ParseExampleV2_Tdense_55_num_sparse_0_ragged_split_types_0_ragged_value_types_0_sparse_types_0_device_/job:localhost/replica:0/task:0/device:CPU:0}} Key: roadgraph_samples/xyz.  Can't parse serialized Example. [Op:ParseExampleV2]
scott-ettinger commented 1 year ago

The number of roadgraph samples has increased to 30000. We will update the tutorial shortly. As a workaround for now, you can change the 20000 values in the roadgraph field definitions to 30000.

kharitonov-ivan commented 1 year ago

Thank you for the quick answer. This information exists in updates of the dataset, I hadn't noticed it.

image

Now it works:

roadgraph_features = {
            "roadgraph_samples/dir": tf.io.FixedLenFeature(
                [30000, 3], tf.float32, default_value=None
            ),
            "roadgraph_samples/id": tf.io.FixedLenFeature(
                [30000, 1], tf.int64, default_value=None
            ),
            "roadgraph_samples/type": tf.io.FixedLenFeature(
                [30000, 1], tf.int64, default_value=None
            ),
            "roadgraph_samples/valid": tf.io.FixedLenFeature(
                [30000, 1], tf.int64, default_value=None
            ),
            "roadgraph_samples/xyz": tf.io.FixedLenFeature(
                [30000, 3], tf.float32, default_value=None
            ),
        }

I believe the thread can be closed now. Thanks.