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from functools import partial
import numpy as np
tv = None
try:
import cumm.tensorview as tv
except:
pass
def mask_points_by_range(points, limit_range):
mask = (points[:, 0] >= limit_range[0]) & (points[:, 0] <= limit_range[3]) \
& (points[:, 1] >= limit_range[1]) & (points[:, 1] <= limit_range[4])
return mask
class VoxelGeneratorWrapper():
def __init__(self, vsize_xyz, coors_range_xyz, num_point_features, max_num_points_per_voxel, max_num_voxels):
try:
from spconv.utils import VoxelGeneratorV2 as VoxelGenerator
self.spconv_ver = 1
except:
try:
from spconv.utils import VoxelGenerator
self.spconv_ver = 1
except:
from spconv.utils import Point2VoxelCPU3d as VoxelGenerator
self.spconv_ver = 2
if self.spconv_ver == 1:
self._voxel_generator = VoxelGenerator(
voxel_size=vsize_xyz,
point_cloud_range=coors_range_xyz,
max_num_points=max_num_points_per_voxel,
max_voxels=max_num_voxels
)
else:
self._voxel_generator = VoxelGenerator(
vsize_xyz=vsize_xyz,
coors_range_xyz=coors_range_xyz,
num_point_features=num_point_features,
max_num_points_per_voxel=max_num_points_per_voxel,
max_num_voxels=max_num_voxels
)
def generate(self, points):
if self.spconv_ver == 1:
voxel_output = self._voxel_generator.generate(points)
if isinstance(voxel_output, dict):
voxels, coordinates, num_points = \
voxel_output['voxels'], voxel_output['coordinates'], voxel_output['num_points_per_voxel']
else:
voxels, coordinates, num_points = voxel_output
else:
assert tv is not None, f"Unexpected error, library: 'cumm' wasn't imported properly."
voxel_output = self._voxel_generator.point_to_voxel(tv.from_numpy(points))
tv_voxels, tv_coordinates, tv_num_points = voxel_output
# make copy with numpy(), since numpy_view() will disappear as soon as the generator is deleted
voxels = tv_voxels.numpy()
coordinates = tv_coordinates.numpy()
num_points = tv_num_points.numpy()
return voxels, coordinates, num_points
class DataProcessor(object):
def __init__(self, processor_configs, point_cloud_range, training, num_point_features):
self.point_cloud_range = point_cloud_range
self.training = training
self.num_point_features = num_point_features
self.mode = 'train' if training else 'test'
self.grid_size = self.voxel_size = None
self.data_processor_queue = []
self.voxel_generator = None
for cur_cfg in processor_configs:
cur_processor = getattr(self, cur_cfg.NAME)(config=cur_cfg)
self.data_processor_queue.append(cur_processor)
def mask_points_and_boxes_outside_range(self, data_dict=None, config=None):
if data_dict is None:
return partial(self.mask_points_and_boxes_outside_range, config=config)
if data_dict.get('points', None) is not None:
mask = mask_points_by_range(data_dict['points'], self.point_cloud_range)
data_dict['points'] = data_dict['points'][mask]
return data_dict
def shuffle_points(self, data_dict=None, config=None):
if data_dict is None:
return partial(self.shuffle_points, config=config)
if config.SHUFFLE_ENABLED[self.mode]:
points = data_dict['points']
shuffle_idx = np.random.permutation(points.shape[0])
points = points[shuffle_idx]
data_dict['points'] = points
return data_dict
def transform_points_to_voxels_placeholder(self, data_dict=None, config=None):
# just calculate grid size
if data_dict is None:
grid_size = (self.point_cloud_range[3:6] - self.point_cloud_range[0:3]) / np.array(config.VOXEL_SIZE)
self.grid_size = np.round(grid_size).astype(np.int64)
self.voxel_size = config.VOXEL_SIZE
return partial(self.transform_points_to_voxels_placeholder, config=config)
return data_dict
def double_flip(self, points):
# y flip
points_yflip = points.copy()
points_yflip[:, 1] = -points_yflip[:, 1]
# x flip
points_xflip = points.copy()
points_xflip[:, 0] = -points_xflip[:, 0]
# x y flip
points_xyflip = points.copy()
points_xyflip[:, 0] = -points_xyflip[:, 0]
points_xyflip[:, 1] = -points_xyflip[:, 1]
return points_yflip, points_xflip, points_xyflip
def transform_points_to_voxels(self, data_dict=None, config=None):
if data_dict is None:
grid_size = (self.point_cloud_range[3:6] - self.point_cloud_range[0:3]) / np.array(config.VOXEL_SIZE)
self.grid_size = np.round(grid_size).astype(np.int64)
self.voxel_size = config.VOXEL_SIZE
# just bind the config, we will create the VoxelGeneratorWrapper later,
# to avoid pickling issues in multiprocess spawn
return partial(self.transform_points_to_voxels, config=config)
if self.voxel_generator is None:
self.voxel_generator = VoxelGeneratorWrapper(
vsize_xyz=config.VOXEL_SIZE,
coors_range_xyz=self.point_cloud_range,
num_point_features=self.num_point_features,
max_num_points_per_voxel=config.MAX_POINTS_PER_VOXEL,
max_num_voxels=config.MAX_NUMBER_OF_VOXELS[self.mode],
)
points = data_dict['points']
voxel_output = self.voxel_generator.generate(points)
voxels, coordinates, num_points = voxel_output
data_dict['voxels'] = voxels
data_dict['voxel_coords'] = coordinates
data_dict['voxel_num_points'] = num_points
return data_dict
def sample_points(self, data_dict=None, config=None):
if data_dict is None:
return partial(self.sample_points, config=config)
num_points = config.NUM_POINTS[self.mode]
if num_points == -1:
return data_dict
points = data_dict['points']
if num_points < len(points):
pts_depth = np.linalg.norm(points[:, 0:3], axis=1)
pts_near_flag = pts_depth < 40.0
far_idxs_choice = np.where(pts_near_flag == 0)[0]
near_idxs = np.where(pts_near_flag == 1)[0]
choice = []
if num_points > len(far_idxs_choice):
near_idxs_choice = np.random.choice(near_idxs, num_points - len(far_idxs_choice), replace=False)
choice = np.concatenate((near_idxs_choice, far_idxs_choice), axis=0) \
if len(far_idxs_choice) > 0 else near_idxs_choice
else:
choice = np.arange(0, len(points), dtype=np.int32)
choice = np.random.choice(choice, num_points, replace=False)
np.random.shuffle(choice)
else:
choice = np.arange(0, len(points), dtype=np.int32)
if num_points > len(points):
extra_choice = np.random.choice(choice, num_points - len(points), replace=False)
choice = np.concatenate((choice, extra_choice), axis=0)
np.random.shuffle(choice)
data_dict['points'] = points[choice]
return data_dict
def calculate_grid_size(self, data_dict=None, config=None):
if data_dict is None:
grid_size = (self.point_cloud_range[3:6] - self.point_cloud_range[0:3]) / np.array(config.VOXEL_SIZE)
self.grid_size = np.round(grid_size).astype(np.int64)
self.voxel_size = config.VOXEL_SIZE
return partial(self.calculate_grid_size, config=config)
return data_dict
def forward(self, data_dict):
"""
Args:
data_dict:
points: (N, 3 + C_in)
gt_boxes: optional, (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
gt_names: optional, (N), string
...
Returns:
"""
for cur_processor in self.data_processor_queue:
data_dict = cur_processor(data_dict=data_dict)
return data_dict
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