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Fix mutable default <class 'numpy.ndarray'> for field grid_min is not allowed: use default_factory #78

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Fix mutable default <class 'numpy.ndarray'> for field grid_min is not allowed: use default_factory #78

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10 changes: 5 additions & 5 deletions zod/visualization/bev_utils.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
"""Utilities for creating point cloud input representation."""

from dataclasses import dataclass
from dataclasses import dataclass, field
from typing import List, Tuple

import numpy as np
Expand All @@ -25,8 +25,8 @@ class BEVSettings:

# pylint: disable=too-many-instance-attributes
# General settings
grid_min: np.ndarray = np.array([-50.0, 0.0])
grid_max: np.ndarray = np.array([50.0, 100.0])
grid_min: np.ndarray = field(default_factory=lambda: np.array([-50.0, 0.0]))
grid_max: np.ndarray = field(default_factory=lambda: np.array([50.0, 100.0]))
grid_cell_size: float = 0.1 # Default in PIXOR: 0.1

# Pixor settings
Expand Down Expand Up @@ -94,7 +94,7 @@ def _create_pointcloud_input_pixor(
A PIXOR style BEV projection of the input point cloud.

"""
point_indices_c = np.cast["int32"]((points[:, 2] - settings.pixor_z_min) / settings.grid_cell_size)
point_indices_c = np.asarray((points[:, 2] - settings.pixor_z_min) / settings.grid_cell_size, dtype="int32")
point_indices_c = 1 + np.clip(
point_indices_c,
a_min=-1,
Expand Down Expand Up @@ -156,7 +156,7 @@ def get_grid_indices_xy(cloud: np.ndarray, settings: BEVSettings) -> np.ndarray:

"""
# Convert points to indices
indices_xy = np.cast["int32"]((cloud[:, :2] - settings.grid_min) / settings.grid_cell_size)
indices_xy = np.asarray((cloud[:, :2] - settings.grid_min) / settings.grid_cell_size, dtype="int32")
return indices_xy


Expand Down
4 changes: 2 additions & 2 deletions zod/visualization/lidar_bev.py
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Original file line number Diff line number Diff line change
Expand Up @@ -122,7 +122,7 @@ def _add_object(
)

# Add arrow in the direction of the object
arrow_length = int(np.cast["float32"](dimension[0]) * 0.8)
arrow_length = int(np.asarray(dimension[0]) * 0.8, dtype="float32")
end_point = np.array([arrow_length, 0, 0])
end_point = rotation.rotate(end_point)[:2] + position
fig.add_annotation(
Expand Down Expand Up @@ -156,6 +156,6 @@ def _activate_legend(self, fig):
@staticmethod
def _create_od_vis_background(input_array: np.ndarray) -> np.ndarray:
"""Create a gray occupancy grid as background to visualize over."""
occupancy = np.maximum.reduce(np.cast["float32"](np.abs(input_array) > 0.0), axis=0, keepdims=True)
occupancy = np.maximum.reduce(np.asarray(np.abs(input_array) > 0.0, dtype="float32"), axis=0, keepdims=True)
vis_bg = np.transpose(np.repeat(occupancy * 77, 3, axis=0), [2, 1, 0])
return vis_bg