Metrics changes + tests
This commit is contained in:
LinasKo
parent
b061f4fd84
commit
4c903cf7f2
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@ -0,0 +1,2 @@
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from supervision.metrics.core import UnsupportedMetricTargetError, Metric, MetricTarget
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from supervision.metrics.intersection_over_union import IntersectionOverUnion
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@ -1,9 +1,16 @@
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from enum import Enum
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from typing import Any
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from typing import Any, Dict, Iterator, Optional, Tuple, Union
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import numpy as np
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import numpy.typing as npt
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from supervision import config
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from supervision.detection.core import Detections
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"""Used by metrics module as class ID, when none is present"""
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CLASS_ID_NONE = -1
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class Metric(ABC):
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"""
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@ -36,14 +43,15 @@ class Metric(ABC):
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Compute the metric from the internal state and return the result.
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"""
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raise NotImplementedError
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@abstractmethod
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def to_pandas(self, *args, **kwargs) -> Any:
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"""
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Return a pandas DataFrame representation of the metric.
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"""
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self._ensure_pandas_installed()
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raise NotImplementedError
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# TODO: determine if this is necessary.
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# @abstractmethod
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# def to_pandas(self, *args, **kwargs) -> Any:
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# """
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# Return a pandas DataFrame representation of the metric.
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# """
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# self._ensure_pandas_installed()
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# raise NotImplementedError
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def _ensure_pandas_installed(self):
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try:
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@ -73,3 +81,113 @@ class UnsupportedMetricTargetError(Exception):
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def __init__(self, metric: Metric, target: MetricTarget):
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super().__init__(f"Metric {metric} does not support target {target}")
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class InternalMetricDataStore:
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"""
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Stores internal data of IntersectionOverUnion metric:
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* Stores the basic data: boxes, masks, or oriented bounding boxes
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* Validates data: ensures data types and shape are consistent
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* Provides iteration by class
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"""
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def __init__(
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self,
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metric_target: MetricTarget,
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class_agnostic: bool
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):
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self._metric_target = metric_target
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self._class_agnostic = class_agnostic
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self._data_1: Dict[int, npt.NDArray]
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self._data_2: Dict[int, npt.NDArray]
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self._datapoint_shape: Optional[Tuple[int, ...]]
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self.reset()
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def reset(self) -> None:
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self._data_1 = {}
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self._data_2 = {}
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if self._metric_target == MetricTarget.BOXES:
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self._datapoint_shape = (4,)
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elif self._metric_target == MetricTarget.MASKS:
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# Determined when adding data
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self._datapoint_shape = None
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elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
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self._datapoint_shape = (8,)
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def update(
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self,
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data_1: Union[npt.NDArray, Detections],
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data_2: Union[npt.NDArray, Detections],
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) -> None:
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content_1 = self._get_content(data_1)
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content_2 = self._get_content(data_2)
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class_ids_1 = self._get_class_ids(data_1)
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class_ids_2 = self._get_class_ids(data_2)
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self._validate_class_ids(class_ids_1)
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self._validate_class_ids(class_ids_2)
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if content_1 is not None and len(content_1) > 0:
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assert len(content_1) == len(class_ids_1)
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for class_id in set(class_ids_1):
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content_of_class = content_1[class_ids_1 == class_id]
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if class_id not in self._data_1:
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self._data_1[class_id] = content_of_class
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continue
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self._data_1[class_id] = np.vstack((self._data_1[class_id], content_of_class))
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if content_2 is not None and len(content_2) > 0:
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assert len(content_2) == len(class_ids_2)
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for class_id in set(class_ids_2):
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content_of_class = content_2[class_ids_2 == class_id]
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if class_id not in self._data_2:
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self._data_2[class_id] = content_of_class
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continue
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self._data_2[class_id] = np.vstack((self._data_2[class_id], content_of_class))
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def __iter__(self) -> Iterator[Tuple[int, Optional[npt.NDArray], Optional[npt.NDArray]]]:
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class_ids = sorted(set.union(
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set(self._data_1.keys()),
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set(self._data_2.keys())
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))
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for class_id in class_ids:
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yield class_id, self._data_1.get(class_id, None), self._data_2.get(class_id, None)
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def _get_content(self, data: Union[npt.NDArray, Detections]) -> Optional[npt.NDArray]:
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"""Return boxes, masks or oriented bounding boxes from the data."""
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if isinstance(data, np.ndarray):
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return data
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assert isinstance(data, Detections)
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if self._metric_target == MetricTarget.BOXES:
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return data.xyxy
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if self._metric_target == MetricTarget.MASKS:
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return data.mask
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if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
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obb = data.data.get(config.ORIENTED_BOX_COORDINATES, None)
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if isinstance(obb, list):
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obb = np.array(obb, dtype=np.float32)
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return obb
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raise ValueError(f"Invalid metric target: {self._metric_target}")
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def _get_class_ids(self, data: Union[npt.NDArray, Detections]) -> npt.NDArray[np.int_]:
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if self._class_agnostic or isinstance(data, np.ndarray):
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return np.array([CLASS_ID_NONE] * len(data), dtype=int)
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assert isinstance(data, Detections)
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if data.class_id is None:
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return np.array([CLASS_ID_NONE] * len(data), dtype=int)
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return data.class_id
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def _validate_class_ids(self, class_id: npt.NDArray[np.int_]) -> None:
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class_set = set(class_id)
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if len(class_set) >= 2 and -1 in class_set:
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raise ValueError("Metrics store received results with partially defined classes.")
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def _validate_shape(self, data: npt.NDArray) -> None:
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if self._datapoint_shape is None:
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assert self._metric_target == MetricTarget.MASKS
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self._datapoint_shape = data.shape[1:]
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return
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if data.shape[1:] != self._datapoint_shape:
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raise ValueError(
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f"Invalid data shape: {data.shape}. Expected: (N, {self._datapoint_shape})"
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)
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@ -1,194 +1,16 @@
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from typing import TYPE_CHECKING, Dict, Iterator, Optional, Tuple, Union
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from typing import Dict, Union, TYPE_CHECKING
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import numpy as np
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import numpy.typing as npt
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import supervision.config as config
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from supervision.detection.core import Detections
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from supervision.metrics.core import Metric, MetricTarget
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from supervision.metrics.core import InternalMetricDataStore, Metric, MetricTarget
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if TYPE_CHECKING:
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import pandas as pd
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CLASS_ID_NONE = -1
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Data = Union[npt.NDArray, Detections]
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class InternalMetricDataStore:
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"""
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Stores internal data of IntersectionOverUnion metric:
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* Stores the basic data: boxes, masks, or oriented bounding boxes
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* Validates data: ensures data types and shape are consistent
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* Provides iteration by class
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"""
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def __init__(self, metric_target: MetricTarget, class_agnostic: bool):
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self._metric_target = metric_target
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self._class_agnostic = class_agnostic
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self._data_1: Dict[int, npt.NDArray]
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self._data_2: Dict[int, npt.NDArray]
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self._datapoint_shape: Optional[Tuple[int]]
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self.reset()
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def reset(self) -> None:
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self._data_1 = {}
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self._data_2 = {}
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if self._metric_target == MetricTarget.BOXES:
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self._datapoint_shape = (4,)
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elif self._metric_target == MetricTarget.MASKS:
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# Determined when adding data
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self._datapoint_shape = None
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elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
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self._datapoint_shape = (8,)
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# def update(
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# self,
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# data_1: Union[npt.NDArray, Detections],
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# data_2: Union[npt.NDArray, Detections],
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# ) -> None:
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# # This class dispatches to helper self._add_to_list, which does more validation
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# # Then calls self._vstack which generates the final result
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# if len(data_1) == 0 and len(data_2) == 0:
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# return
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# if type(data_1) != type(data_2):
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# raise ValueError(
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# f"Data types must match. Got {type(data_1)=} and {type(data_2)=}."
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# )
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# if isinstance(data_1, npt.NDArray):
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# assert isinstance(data_2, npt.NDArray)
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# self._update(data_1, class_id=CLASS_ID_NONE, data_id=1)
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# self._update(data_2, class_id=CLASS_ID_NONE, data_id=2)
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# return
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# assert isinstance(data_1, Detections)
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# assert isinstance(data_2, Detections)
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# if self._class_agnostic:
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# self._update(self._get_detections_content(data_1), class_id=CLASS_ID_NONE, data_id=1)
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# self._update(self._get_detections_content(data_2), class_id=CLASS_ID_NONE, data_id=2)
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# return
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# if data_1.class_id is None:
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# self._update(self._get_detections_content(data_1), class_id=CLASS_ID_NONE, data_id=1)
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# else:
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# for class_id in set(data_1.class_id):
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# data_1_of_class = data_1[data_1.class_id == class_id]
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# assert isinstance(data_1_of_class, Detections)
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# self._update(self._get_detections_content(data_1_of_class), class_id=class_id, data_id=1)
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# if data_2.class_id is None:
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# self._update(self._get_detections_content(data_2), class_id=CLASS_ID_NONE, data_id=2)
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# else:
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# for class_id in set(data_2.class_id):
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# data_2_of_class = data_2[data_2.class_id == class_id]
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# assert isinstance(data_2_of_class, Detections)
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# self._update(self._get_detections_content(data_2_of_class), class_id=class_id, data_id=2)
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def update(
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self,
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data_1: Union[npt.NDArray, Detections],
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data_2: Union[npt.NDArray, Detections],
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) -> None:
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content_1 = self._get_content(data_1)
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content_2 = self._get_content(data_2)
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class_ids_1 = self._get_class_ids(data_1)
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class_ids_2 = self._get_class_ids(data_2)
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self._validate_class_ids(class_ids_1)
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self._validate_class_ids(class_ids_2)
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if content_1 is not None and len(content_1) > 0:
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assert len(content_1) == len(class_ids_1)
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for class_id in set(class_ids_1):
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content_of_class = content_1[class_ids_1 == class_id]
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if class_id not in self._data_1:
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self._data_1[class_id] = content_of_class
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continue
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self._data_1[class_id] = np.vstack(
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(self._data_1[class_id], content_of_class)
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)
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if content_2 is not None and len(content_2) > 0:
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assert len(content_2) == len(class_ids_2)
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for class_id in set(class_ids_2):
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content_of_class = content_2[class_ids_2 == class_id]
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if class_id not in self._data_2:
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self._data_2[class_id] = content_of_class
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continue
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self._data_2[class_id] = np.vstack(
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(self._data_2[class_id], content_of_class)
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)
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def __iter__(
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self,
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) -> Iterator[Tuple[int, Optional[npt.NDArray], Optional[npt.NDArray]]]:
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class_ids = sorted(
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set.union(set(self._data_1.keys()), set(self._data_2.keys()))
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)
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for class_id in class_ids:
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yield (
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class_id,
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self._data_1.get(class_id, None),
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self._data_2.get(class_id, None),
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)
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def _get_content(self, data: Data) -> Optional[npt.NDArray]:
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"""Return boxes, masks or oriented bounding boxes from the data."""
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if isinstance(data, npt.NDArray):
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return data
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assert isinstance(data, Detections)
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if self._metric_target == MetricTarget.BOXES:
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return data.xyxy
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if self._metric_target == MetricTarget.MASKS:
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return data.mask
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if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
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obb = data.data.get(config.ORIENTED_BOX_COORDINATES, None)
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if isinstance(obb, list):
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obb = np.array(obb, dtype=np.float32)
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return obb
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raise ValueError(f"Invalid metric target: {self._metric_target}")
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def _get_class_ids(self, data: Data) -> npt.NDArray[np.int_]:
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if self._class_agnostic or isinstance(data, npt.NDArray):
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return np.array([CLASS_ID_NONE] * len(data), dtype=int)
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assert isinstance(data, Detections)
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if data.class_id is None:
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return np.array([CLASS_ID_NONE] * len(data), dtype=int)
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return data.class_id
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# def _get_detections_content(self, data: Detections) -> Optional[npt.NDArray]:
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# if self._metric_target == MetricTarget.BOXES:
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# return data.xyxy
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# if self._metric_target == MetricTarget.MASKS:
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# return data.mask
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# if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
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# obb = data.data.get(config.ORIENTED_BOX_COORDINATES, None)
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# if isinstance(obb, list):
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# obb = np.array(obb, dtype=np.float32)
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# return obb
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# raise ValueError(f"Invalid metric target: {self._metric_target}")
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def _validate_class_ids(self, class_id: npt.NDArray[np.int_]) -> None:
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class_set = set(class_id)
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if len(class_set) >= 2 and -1 in class_set:
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raise ValueError(
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"Metrics store received results with partially defined classes."
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)
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def _validate_shape(self, data: npt.NDArray) -> None:
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if self._datapoint_shape is None:
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assert self._metric_target == MetricTarget.MASKS
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self._datapoint_shape = data.shape[1:]
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return
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if data.shape[1:] != self._datapoint_shape:
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raise ValueError(
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f"Invalid data shape: {data.shape}. Expected: (N, {self._datapoint_shape})"
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)
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class IntersectionOverUnion(Metric):
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def __init__(
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self,
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@ -261,22 +83,36 @@ class IntersectionOverUnion(Metric):
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ious[class_id] = iou
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return ious
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def to_pandas(self) -> pd.DataFrame:
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"""
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Return a pandas DataFrame representation of the metric.
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"""
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self._ensure_pandas_installed()
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import pandas as pd
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# TODO: This would return dict[int, pd.DataFrame]. Do we want that?
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# It'd be cleaner if it returned a single DataFrame, but the sizes
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# differ if class_agnostic=False.
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# TODO: use cache results instead
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ious = self.compute()
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# def to_pandas(self) -> 'pd.DataFrame':
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# """
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# Return a pandas DataFrame representation of the metric.
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# """
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# self._ensure_pandas_installed()
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# import pandas as pd
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# TODO: continue
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# # TODO: use cached results
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# ious = self.compute()
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# print(len(ious))
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# data_frame = pd.DataFrame()
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# class_names = []
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# arrays = []
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# return s
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return pd.DataFrame()
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# for class_id, array in ious.items():
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# print(array.shape)
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# class_names.append(np.full(array.shape[0], class_id))
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# arrays.append(array)
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# stacked_class_ids = np.concatenate(class_names)
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# stacked_ious = np.vstack(arrays)
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# combined = np.column_stack((stacked_class_ids, stacked_ious))
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# column_names = ['class_id'] + [f'col_{i+1}' for i in range(stacked_ious.shape[1])]
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# result = pd.DataFrame(combined, columns=column_names)
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# return result
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@staticmethod
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def _compute_box_iou(
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|
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@ -0,0 +1,282 @@
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from contextlib import ExitStack as DoesNotRaise
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from test.test_utils import mock_detections
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from typing import List, Optional, Tuple, Union
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import numpy as np
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import numpy.typing as npt
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import pytest
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from supervision.detection.core import Detections
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from supervision.metrics.core import (
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InternalMetricDataStore,
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MetricTarget,
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CLASS_ID_NONE
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)
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# Boxes, class-agnostic
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@pytest.mark.parametrize(
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"data_1, data_2, expected_result, exception",
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[
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# Empty
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(
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Detections.empty(),
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Detections.empty(),
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[],
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DoesNotRaise()
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),
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(
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np.empty((0, 4), dtype=np.float32),
|
||||
np.empty((0, 4), dtype=np.float32),
|
||||
[],
|
||||
DoesNotRaise()
|
||||
),
|
||||
(
|
||||
Detections.empty(),
|
||||
np.empty((0, 4), dtype=np.float32),
|
||||
[],
|
||||
DoesNotRaise()
|
||||
),
|
||||
|
||||
# Single box + Empty
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1]], dtype=np.float32)
|
||||
),
|
||||
Detections.empty(),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
None
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
(
|
||||
Detections.empty(),
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1]], dtype=np.float32)
|
||||
),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
None,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32)
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
|
||||
# Multiple boxes
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
),
|
||||
Detections.empty(),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
None
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
),
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
|
||||
# with classes - should be ignored.
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
class_id=np.array([1, 2], dtype=int)
|
||||
),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
]
|
||||
)
|
||||
def test_store_boxes_class_agnostic(
|
||||
data_1: Union[npt.NDArray, Detections],
|
||||
data_2: Union[npt.NDArray, Detections],
|
||||
expected_result: List[Tuple[int, Optional[npt.NDArray], Optional[npt.NDArray]]],
|
||||
exception: Exception
|
||||
) -> None:
|
||||
store = InternalMetricDataStore(MetricTarget.BOXES, class_agnostic=True)
|
||||
store.update(data_1, data_2)
|
||||
result = [result for result in store]
|
||||
assert len(result) == len(expected_result)
|
||||
with exception:
|
||||
for (class_id, content_1, content_2), (expected_class_id, expected_content_1, expected_content_2) in zip(result, expected_result):
|
||||
assert class_id == expected_class_id
|
||||
assert (content_1 is None and expected_content_1 is None) or np.array_equal(content_1, expected_content_1)
|
||||
assert (content_2 is None and expected_content_2 is None) or np.array_equal(content_2, expected_content_2)
|
||||
|
||||
# Boxes, by-class
|
||||
@pytest.mark.parametrize(
|
||||
"data_1, data_2, expected_result, exception",
|
||||
[
|
||||
# Single box + Empty
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
class_id=np.array([1], dtype=int)
|
||||
),
|
||||
Detections.empty(),
|
||||
[
|
||||
(
|
||||
1,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
None
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
(
|
||||
Detections.empty(),
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
class_id=np.array([1], dtype=int)
|
||||
),
|
||||
[
|
||||
(
|
||||
1,
|
||||
None,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32)
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
|
||||
# Multiple classes
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
class_id=np.array([1, 2], dtype=int)
|
||||
),
|
||||
Detections.empty(),
|
||||
[
|
||||
(
|
||||
1,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
None
|
||||
),
|
||||
(
|
||||
2,
|
||||
np.array([[0, 0, 2, 2]], dtype=np.float32),
|
||||
None
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
class_id=np.array([1, 2], dtype=int)
|
||||
),
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
class_id=np.array([2, 3], dtype=int)
|
||||
),
|
||||
[
|
||||
(
|
||||
1,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
None
|
||||
),
|
||||
(
|
||||
2,
|
||||
np.array([[0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32)
|
||||
),
|
||||
(
|
||||
3,
|
||||
None,
|
||||
np.array([[0, 0, 2, 2]], dtype=np.float32)
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
|
||||
# with classes - should be ignored.
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
class_id=np.array([1, 2], dtype=int)
|
||||
),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
),
|
||||
]
|
||||
)
|
||||
def test_store_boxes_by_class(
|
||||
data_1: Union[npt.NDArray, Detections],
|
||||
data_2: Union[npt.NDArray, Detections],
|
||||
expected_result: List[Tuple[int, Optional[npt.NDArray], Optional[npt.NDArray]]],
|
||||
exception: Exception
|
||||
) -> None:
|
||||
store = InternalMetricDataStore(MetricTarget.BOXES, class_agnostic=False)
|
||||
store.update(data_1, data_2)
|
||||
result = [result for result in store]
|
||||
assert len(result) == len(expected_result)
|
||||
with exception:
|
||||
for (class_id, content_1, content_2), (expected_class_id, expected_content_1, expected_content_2) in zip(result, expected_result):
|
||||
assert class_id == expected_class_id
|
||||
assert (content_1 is None and expected_content_1 is None) or np.array_equal(content_1, expected_content_1)
|
||||
assert (content_2 is None and expected_content_2 is None) or np.array_equal(content_2, expected_content_2)
|
||||
Loading…
Reference in New Issue