diff --git a/nucleus/metrics/__init__.py b/nucleus/metrics/__init__.py
index 1fd038a2..cb19d8a8 100644
--- a/nucleus/metrics/__init__.py
+++ b/nucleus/metrics/__init__.py
@@ -1,5 +1,6 @@
from .base import Metric, ScalarResult
from .categorization_metrics import CategorizationF1
+from .cuboid_metrics import CuboidIOU, CuboidPrecision, CuboidRecall
from .polygon_metrics import (
PolygonAveragePrecision,
PolygonIOU,
diff --git a/nucleus/metrics/cuboid_metrics.py b/nucleus/metrics/cuboid_metrics.py
new file mode 100644
index 00000000..62e1f09f
--- /dev/null
+++ b/nucleus/metrics/cuboid_metrics.py
@@ -0,0 +1,194 @@
+import sys
+from abc import abstractmethod
+from typing import List
+
+from nucleus.annotation import AnnotationList, CuboidAnnotation
+from nucleus.prediction import CuboidPrediction, PredictionList
+
+from .base import Metric, ScalarResult
+from .cuboid_utils import detection_iou, label_match_wrapper, recall_precision
+from .filters import confidence_filter
+
+
+class CuboidMetric(Metric):
+ """Abstract class for metrics of cuboids.
+
+ The CuboidMetric class automatically filters incoming annotations and
+ predictions for only cuboid annotations. It also filters
+ predictions whose confidence is less than the provided confidence_threshold.
+ Finally, it provides support for enforcing matching labels. If
+ `enforce_label_match` is set to True, then annotations and predictions will
+ only be matched if they have the same label.
+
+ To create a new concrete CuboidMetric, override the `eval` function
+ with logic to define a metric between cuboid annotations and predictions.
+ """
+
+ def __init__(
+ self,
+ enforce_label_match: bool = False,
+ confidence_threshold: float = 0.0,
+ ):
+ """Initializes CuboidMetric abstract object.
+
+ Args:
+ enforce_label_match: whether to enforce that annotation and prediction labels must match. Default False
+ confidence_threshold: minimum confidence threshold for predictions. Must be in [0, 1]. Default 0.0
+ """
+ self.enforce_label_match = enforce_label_match
+ assert 0 <= confidence_threshold <= 1
+ self.confidence_threshold = confidence_threshold
+
+ @abstractmethod
+ def eval(
+ self,
+ annotations: List[CuboidAnnotation],
+ predictions: List[CuboidPrediction],
+ ) -> ScalarResult:
+ # Main evaluation function that subclasses must override.
+ pass
+
+ def aggregate_score(self, results: List[ScalarResult]) -> ScalarResult: # type: ignore[override]
+ return ScalarResult.aggregate(results)
+
+ def __call__(
+ self, annotations: AnnotationList, predictions: PredictionList
+ ) -> ScalarResult:
+ if self.confidence_threshold > 0:
+ predictions = confidence_filter(
+ predictions, self.confidence_threshold
+ )
+ cuboid_annotations: List[CuboidAnnotation] = []
+ cuboid_annotations.extend(annotations.cuboid_annotations)
+ cuboid_predictions: List[CuboidPrediction] = []
+ cuboid_predictions.extend(predictions.cuboid_predictions)
+
+ eval_fn = label_match_wrapper(self.eval)
+ result = eval_fn(
+ cuboid_annotations,
+ cuboid_predictions,
+ enforce_label_match=self.enforce_label_match,
+ )
+ return result
+
+
+class CuboidIOU(CuboidMetric):
+ """Calculates the average IOU between cuboid annotations and predictions."""
+
+ # TODO: Remove defaults once these are surfaced more cleanly to users.
+ def __init__(
+ self,
+ enforce_label_match: bool = True,
+ iou_threshold: float = 0.0,
+ confidence_threshold: float = 0.0,
+ iou_2d: bool = False,
+ ):
+ """Initializes CuboidIOU object.
+
+ Args:
+ enforce_label_match: whether to enforce that annotation and prediction labels must match. Defaults to True
+ iou_threshold: IOU threshold to consider detection as valid. Must be in [0, 1]. Default 0.0
+ birds_eye_view: whether to return the BEV 2D IOU if true, or the 3D IOU if false.
+ confidence_threshold: minimum confidence threshold for predictions. Must be in [0, 1]. Default 0.0
+ """
+ assert (
+ 0 <= iou_threshold <= 1
+ ), "IoU threshold must be between 0 and 1."
+ self.iou_threshold = iou_threshold
+ self.iou_2d = iou_2d
+ super().__init__(enforce_label_match, confidence_threshold)
+
+ def eval(
+ self,
+ annotations: List[CuboidAnnotation],
+ predictions: List[CuboidPrediction],
+ ) -> ScalarResult:
+ iou_3d_metric, iou_2d_metric = detection_iou(
+ predictions,
+ annotations,
+ threshold_in_overlap_ratio=self.iou_threshold,
+ )
+
+ weight = max(len(annotations), len(predictions))
+ if self.iou_2d:
+ avg_iou = iou_2d_metric.sum() / max(weight, sys.float_info.epsilon)
+ else:
+ avg_iou = iou_3d_metric.sum() / max(weight, sys.float_info.epsilon)
+
+ return ScalarResult(avg_iou, weight)
+
+
+class CuboidPrecision(CuboidMetric):
+ """Calculates the average precision between cuboid annotations and predictions."""
+
+ # TODO: Remove defaults once these are surfaced more cleanly to users.
+ def __init__(
+ self,
+ enforce_label_match: bool = True,
+ iou_threshold: float = 0.0,
+ confidence_threshold: float = 0.0,
+ ):
+ """Initializes CuboidIOU object.
+
+ Args:
+ enforce_label_match: whether to enforce that annotation and prediction labels must match. Defaults to True
+ iou_threshold: IOU threshold to consider detection as valid. Must be in [0, 1]. Default 0.0
+ confidence_threshold: minimum confidence threshold for predictions. Must be in [0, 1]. Default 0.0
+ """
+ assert (
+ 0 <= iou_threshold <= 1
+ ), "IoU threshold must be between 0 and 1."
+ self.iou_threshold = iou_threshold
+ super().__init__(enforce_label_match, confidence_threshold)
+
+ def eval(
+ self,
+ annotations: List[CuboidAnnotation],
+ predictions: List[CuboidPrediction],
+ ) -> ScalarResult:
+ stats = recall_precision(
+ predictions,
+ annotations,
+ threshold_in_overlap_ratio=self.iou_threshold,
+ )
+ weight = stats["tp_sum"] + stats["fp_sum"]
+ precision = stats["tp_sum"] / max(weight, sys.float_info.epsilon)
+ return ScalarResult(precision, weight)
+
+
+class CuboidRecall(CuboidMetric):
+ """Calculates the average recall between cuboid annotations and predictions."""
+
+ # TODO: Remove defaults once these are surfaced more cleanly to users.
+ def __init__(
+ self,
+ enforce_label_match: bool = True,
+ iou_threshold: float = 0.0,
+ confidence_threshold: float = 0.0,
+ ):
+ """Initializes CuboidIOU object.
+
+ Args:
+ enforce_label_match: whether to enforce that annotation and prediction labels must match. Defaults to True
+ iou_threshold: IOU threshold to consider detection as valid. Must be in [0, 1]. Default 0.0
+ confidence_threshold: minimum confidence threshold for predictions. Must be in [0, 1]. Default 0.0
+ """
+ assert (
+ 0 <= iou_threshold <= 1
+ ), "IoU threshold must be between 0 and 1."
+ self.iou_threshold = iou_threshold
+ super().__init__(enforce_label_match, confidence_threshold)
+
+ def eval(
+ self,
+ annotations: List[CuboidAnnotation],
+ predictions: List[CuboidPrediction],
+ ) -> ScalarResult:
+ stats = recall_precision(
+ predictions,
+ annotations,
+ threshold_in_overlap_ratio=self.iou_threshold,
+ )
+ weight = stats["tp_sum"] + stats["fn_sum"]
+ recall = stats["tp_sum"] / max(weight, sys.float_info.epsilon)
+ return ScalarResult(recall, weight)
diff --git a/nucleus/metrics/cuboid_utils.py b/nucleus/metrics/cuboid_utils.py
new file mode 100644
index 00000000..0ebf0716
--- /dev/null
+++ b/nucleus/metrics/cuboid_utils.py
@@ -0,0 +1,355 @@
+from functools import wraps
+from typing import Dict, List, Tuple
+
+import numpy as np
+from shapely.geometry import Polygon
+
+from nucleus.annotation import CuboidAnnotation
+from nucleus.prediction import CuboidPrediction
+
+from .base import ScalarResult
+
+
+def group_cuboids_by_label(
+ annotations: List[CuboidAnnotation],
+ predictions: List[CuboidPrediction],
+) -> Dict[str, Tuple[List[CuboidAnnotation], List[CuboidPrediction]]]:
+ """Groups input annotations and predictions by label.
+
+ Args:
+ annotations: list of input cuboid annotations
+ predictions: list of input cuboid predictions
+
+ Returns:
+ Mapping from each label to (annotations, predictions) tuple
+ """
+ labels = set(annotation.label for annotation in annotations)
+ labels |= set(prediction.label for prediction in predictions)
+ grouped: Dict[
+ str, Tuple[List[CuboidAnnotation], List[CuboidPrediction]]
+ ] = {label: ([], []) for label in labels}
+ for annotation in annotations:
+ grouped[annotation.label][0].append(annotation)
+ for prediction in predictions:
+ grouped[prediction.label][1].append(prediction)
+ return grouped
+
+
+def label_match_wrapper(metric_fn):
+ """Decorator to add the ability to only apply metric to annotations and
+ predictions with matching labels.
+
+ Args:
+ metric_fn: Metric function that takes a list of annotations, a list
+ of predictions, and optional args and kwargs.
+
+ Returns:
+ Metric function which can optionally enforce matching labels.
+ """
+
+ @wraps(metric_fn)
+ def wrapper(
+ annotations: List[CuboidAnnotation],
+ predictions: List[CuboidPrediction],
+ *args,
+ enforce_label_match: bool = False,
+ **kwargs,
+ ) -> ScalarResult:
+ # Simply return the metric if we are not enforcing label matches.
+ if not enforce_label_match:
+ return metric_fn(annotations, predictions, *args, **kwargs)
+
+ # For each bin of annotations/predictions, compute the metric applied
+ # only to that bin. Then aggregate results across all bins.
+ grouped_inputs = group_cuboids_by_label(annotations, predictions)
+ metric_results = []
+ for binned_annotations, binned_predictions in grouped_inputs.values():
+ metric_result = metric_fn(
+ binned_annotations, binned_predictions, *args, **kwargs
+ )
+ metric_results.append(metric_result)
+ assert all(
+ isinstance(r, ScalarResult) for r in metric_results
+ ), "Expected every result to be a ScalarResult"
+ return ScalarResult.aggregate(metric_results)
+
+ return wrapper
+
+
+def process_dataitem(dataitem):
+ processed_item = {}
+ processed_item["xyz"] = np.array(
+ [[ann.position.x, ann.position.y, ann.position.z] for ann in dataitem]
+ )
+ processed_item["wlh"] = np.array(
+ [
+ [ann.dimensions.x, ann.dimensions.y, ann.dimensions.z]
+ for ann in dataitem
+ ]
+ )
+ processed_item["yaw"] = np.array([ann.yaw for ann in dataitem])
+ return processed_item
+
+
+def compute_outer_iou(
+ xyz_0: np.ndarray,
+ wlh_0: np.ndarray,
+ yaw_0: np.ndarray,
+ xyz_1: np.ndarray,
+ wlh_1: np.ndarray,
+ yaw_1: np.ndarray,
+ scale_convention: bool = True,
+ distance_threshold=25,
+) -> Tuple[np.ndarray, np.ndarray]:
+ """
+ Computes outer 3D and 2D IoU
+ :param xyz_0: (n, 3)
+ :param wlh_0: (n, 3)
+ :param yaw_0: (n,)
+ :param xyz_1: (m, 3)
+ :param wlh_1: (m, 3)
+ :param yaw_1: (m,)
+ :param scale_convention: flag whether the internal Scale convention is used (have to be adjusted by pi/2)
+ :param distance_threshold: computes iou only within this distance (~3x speedup)
+ :return: (n, m) 3D IoU, (n, m) 2D IoU
+ """
+
+ bottom_z = np.maximum.outer(
+ xyz_0[:, 2] - (wlh_0[:, 2] / 2), xyz_1[:, 2] - (wlh_1[:, 2] / 2)
+ )
+ top_z = np.minimum.outer(
+ xyz_0[:, 2] + (wlh_0[:, 2] / 2), xyz_1[:, 2] + (wlh_1[:, 2] / 2)
+ )
+ height_intersection = np.maximum(0, top_z - bottom_z)
+
+ cuboid_corners_0 = get_batch_cuboid_corners(
+ xyz_0, wlh_0, yaw_0, scale_convention=scale_convention
+ )
+ cuboid_corners_1 = get_batch_cuboid_corners(
+ xyz_1, wlh_1, yaw_1, scale_convention=scale_convention
+ )
+ polygons_1 = [
+ Polygon(corners_1[[1, 0, 4, 5, 1], :2])
+ for corners_1 in cuboid_corners_1
+ ]
+ area_intersection = np.zeros(
+ (cuboid_corners_0.shape[0], cuboid_corners_1.shape[0]),
+ dtype=np.float32,
+ )
+
+ if cuboid_corners_0.shape[0] != 0 and cuboid_corners_1.shape[0] != 0:
+ distance_mask = (
+ np.linalg.norm(
+ xyz_0[:, np.newaxis, :] - xyz_1[np.newaxis, :, :], axis=2
+ )
+ < distance_threshold
+ )
+
+ for i, corners_0 in enumerate(cuboid_corners_0):
+ for j, polygon_1 in enumerate(polygons_1):
+ if distance_mask[i, j]:
+ area_intersection[i, j] = (
+ Polygon(corners_0[[1, 0, 4, 5, 1], :2])
+ .intersection(polygon_1)
+ .area
+ )
+
+ intersection = height_intersection * area_intersection
+ area_0 = wlh_0[:, 0] * wlh_0[:, 1]
+ area_1 = wlh_1[:, 0] * wlh_1[:, 1]
+ union_2d = np.add.outer(area_0, area_1) - area_intersection
+
+ volume_0 = area_0 * wlh_0[:, 2]
+ volume_1 = area_1 * wlh_1[:, 2]
+ union = np.add.outer(volume_0, volume_1) - intersection
+ return intersection / union, area_intersection / union_2d
+
+
+def get_batch_cuboid_corners(
+ xyz: np.ndarray,
+ wlh: np.ndarray,
+ yaw: np.ndarray,
+ pitch: np.ndarray = None,
+ roll: np.ndarray = None,
+ scale_convention: bool = True,
+) -> np.ndarray:
+ """
+ Vectorized batch version of get_cuboid_corners
+ :param xyz: (n, 3)
+ :param wlh: (n, 3)
+ :param yaw: (n,)
+ :param pitch: (n,)
+ :param roll: (n,)
+ :param scale_convention: flag whether the internal Scale convention is used (have to be adjusted by pi/2)
+ :return: (n, 8, 3)
+ """
+ if scale_convention:
+ yaw = yaw.copy() + np.pi / 2
+
+ w, l, h = wlh[:, 0, None], wlh[:, 1, None], wlh[:, 2, None]
+
+ x_corners = l / 2 * np.array([1, 1, 1, 1, -1, -1, -1, -1])
+ y_corners = w / 2 * np.array([1, -1, -1, 1, 1, -1, -1, 1])
+ z_corners = h / 2 * np.array([1, 1, -1, -1, 1, 1, -1, -1])
+ corners = np.stack((x_corners, y_corners, z_corners), axis=1)
+
+ rot_mats = get_batch_rotation_matrices(yaw, pitch, roll)
+ corners = np.matmul(rot_mats, corners)
+
+ x, y, z = xyz[:, 0, None], xyz[:, 1, None], xyz[:, 2, None]
+ corners[:, 0, :] = corners[:, 0, :] + x
+ corners[:, 1, :] = corners[:, 1, :] + y
+ corners[:, 2, :] = corners[:, 2, :] + z
+ return corners.swapaxes(1, 2)
+
+
+def get_batch_rotation_matrices(
+ yaw: np.ndarray, pitch: np.ndarray = None, roll: np.ndarray = None
+) -> np.ndarray:
+ if pitch is None:
+ pitch = np.zeros_like(yaw)
+ if roll is None:
+ roll = np.zeros_like(yaw)
+ cy = np.cos(yaw)
+ sy = np.sin(yaw)
+ cp = np.cos(pitch)
+ sp = np.sin(pitch)
+ cr = np.cos(roll)
+ sr = np.sin(roll)
+ return np.stack(
+ (
+ np.stack(
+ (cy * cp, cy * sp * sr - sy * cr, cy * sp * cr + sy * sr), 1
+ ),
+ np.stack(
+ (sy * cp, sy * sp * sr + cy * cr, sy * sp * cr - cy * sr), 1
+ ),
+ np.stack((-sp, cp * sr, cp * cr), 1),
+ ),
+ 1,
+ )
+
+
+def associate_cuboids_on_iou(
+ xyz_0: np.ndarray,
+ wlh_0: np.ndarray,
+ yaw_0: np.ndarray,
+ xyz_1: np.ndarray,
+ wlh_1: np.ndarray,
+ yaw_1: np.ndarray,
+ threshold_in_overlap_ratio: float = 0.1,
+) -> List[Tuple[int, int]]:
+ if xyz_0.shape[0] < 1 or xyz_1.shape[0] < 1:
+ return []
+ iou_matrix, _ = compute_outer_iou(xyz_0, wlh_0, yaw_0, xyz_1, wlh_1, yaw_1)
+ mapping = []
+ for i, m in enumerate(iou_matrix.max(axis=1)):
+ if m >= threshold_in_overlap_ratio:
+ mapping.append((i, iou_matrix[i].argmax()))
+ return mapping
+
+
+def recall_precision(
+ prediction: List[CuboidPrediction],
+ groundtruth: List[CuboidAnnotation],
+ threshold_in_overlap_ratio: float,
+) -> Dict[str, float]:
+ """
+ Calculates the precision and recall of each lidar frame.
+
+ Args:
+ :param predictions: list of cuboid annotation predictions.
+ :param ground_truth: list of cuboid annotation groundtruths.
+ :param threshold: IOU threshold to consider detection as valid. Must be in [0, 1].
+ """
+
+ tp_sum = 0
+ fp_sum = 0
+ fn_sum = 0
+ num_predicted = 0
+ num_instances = 0
+
+ gt_items = process_dataitem(groundtruth)
+ pred_items = process_dataitem(prediction)
+
+ num_predicted += pred_items["xyz"].shape[0]
+ num_instances += gt_items["xyz"].shape[0]
+
+ tp = np.zeros(pred_items["xyz"].shape[0])
+ fp = np.ones(pred_items["xyz"].shape[0])
+ fn = np.ones(gt_items["xyz"].shape[0])
+
+ mapping = associate_cuboids_on_iou(
+ pred_items["xyz"],
+ pred_items["wlh"],
+ pred_items["yaw"] + np.pi / 2,
+ gt_items["xyz"],
+ gt_items["wlh"],
+ gt_items["yaw"] + np.pi / 2,
+ threshold_in_overlap_ratio=threshold_in_overlap_ratio,
+ )
+
+ for pred_id, gt_id in mapping:
+ if fn[gt_id] == 0:
+ continue
+ tp[pred_id] = 1
+ fp[pred_id] = 0
+ fn[gt_id] = 0
+
+ tp_sum += tp.sum()
+ fp_sum += fp.sum()
+ fn_sum += fn.sum()
+
+ return {
+ "tp_sum": tp_sum,
+ "fp_sum": fp_sum,
+ "fn_sum": fn_sum,
+ "precision": tp_sum / (tp_sum + fp_sum),
+ "recall": tp_sum / (tp_sum + fn_sum),
+ "num_predicted": num_predicted,
+ "num_instances": num_instances,
+ }
+
+
+def detection_iou(
+ prediction: List[CuboidPrediction],
+ groundtruth: List[CuboidAnnotation],
+ threshold_in_overlap_ratio: float,
+) -> Tuple[np.ndarray, np.ndarray]:
+ """
+ Calculates the 2D IOU and 3D IOU overlap between predictions and groundtruth.
+ Uses linear sum assignment to associate cuboids.
+
+ Args:
+ :param predictions: list of cuboid annotation predictions.
+ :param ground_truth: list of cuboid annotation groundtruths.
+ :param threshold: IOU threshold to consider detection as valid. Must be in [0, 1].
+ """
+
+ gt_items = process_dataitem(groundtruth)
+ pred_items = process_dataitem(prediction)
+
+ meter_2d = []
+ meter_3d = []
+
+ if gt_items["xyz"].shape[0] == 0 or pred_items["xyz"].shape[0] == 0:
+ return np.array([0.0]), np.array([0.0])
+
+ iou_3d, iou_2d = compute_outer_iou(
+ gt_items["xyz"],
+ gt_items["wlh"],
+ gt_items["yaw"],
+ pred_items["xyz"],
+ pred_items["wlh"],
+ pred_items["yaw"],
+ )
+
+ for i, m in enumerate(iou_3d.max(axis=1)):
+ if m >= threshold_in_overlap_ratio:
+ j = iou_3d[i].argmax()
+ meter_3d.append(iou_3d[i, j])
+ meter_2d.append(iou_2d[i, j])
+
+ meter_3d = np.array(meter_3d)
+ meter_2d = np.array(meter_2d)
+ return meter_3d, meter_2d
diff --git a/poetry.lock b/poetry.lock
index 55dc71a2..59bda3e5 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1669,6 +1669,19 @@ nativelib = ["pyobjc-framework-cocoa", "pywin32"]
objc = ["pyobjc-framework-cocoa"]
win32 = ["pywin32"]
+[[package]]
+name = "shapely"
+version = "1.8.1.post1"
+description = "Geometric objects, predicates, and operations"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+all = ["pytest", "pytest-cov", "numpy"]
+test = ["pytest", "pytest-cov"]
+vectorized = ["numpy"]
+
[[package]]
name = "shellingham"
version = "1.4.0"
@@ -2064,7 +2077,7 @@ testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytes
[metadata]
lock-version = "1.1"
python-versions = "^3.6.2"
-content-hash = "8d6269a2d5d30685e0d1e9b38187b6105826042e868acfb5bdb5a3e7188560f8"
+content-hash = "4830dd3f9f593703fbbae85efdbd6feca5b0513285ad87132d4f90934fca1098"
[metadata.files]
absl-py = [
@@ -3206,32 +3219,24 @@ pyzmq = [
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{file = "pyzmq-22.3.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:ea12133df25e3a6918718fbb9a510c6ee5d3fdd5a346320421aac3882f4feeea"},
{file = "pyzmq-22.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76c532fd68b93998aab92356be280deec5de8f8fe59cd28763d2cc8a58747b7f"},
- {file = "pyzmq-22.3.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:f907c7359ce8bf7f7e63c82f75ad0223384105f5126f313400b7e8004d9b33c3"},
- {file = "pyzmq-22.3.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:902319cfe23366595d3fa769b5b751e6ee6750a0a64c5d9f757d624b2ac3519e"},
{file = "pyzmq-22.3.0-cp310-cp310-win32.whl", hash = "sha256:67db33bea0a29d03e6eeec55a8190e033318cee3cbc732ba8fd939617cbf762d"},
{file = "pyzmq-22.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:7661fc1d5cb73481cf710a1418a4e1e301ed7d5d924f91c67ba84b2a1b89defd"},
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@@ -3407,6 +3410,34 @@ send2trash = [
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]
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diff --git a/pyproject.toml b/pyproject.toml
index a3a47502..dafcd587 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,6 +45,7 @@ click = ">=7.1.2,<9.0" # NOTE: COLAB has 7.1.2 and has problems updating
rich = "^10.15.2"
shellingham = "^1.4.0"
scikit-learn = ">=0.24.0"
+Shapely = "^1.8.1"
[tool.poetry.dev-dependencies]
poetry = "^1.1.5"