def __init__(self,
output_shape,
num_class=2,
num_input_features=4,
vfe_class_name="VoxelFeatureExtractor",
vfe_num_filters=[32, 128],
with_distance=False,
middle_class_name="SparseMiddleExtractor",
middle_num_input_features=-1,
middle_num_filters_d1=[64],
middle_num_filters_d2=[64, 64],
rpn_class_name="RPN",
rpn_num_input_features=-1,
rpn_layer_nums=[3, 5, 5],
rpn_layer_strides=[2, 2, 2],
rpn_num_filters=[128, 128, 256],
rpn_upsample_strides=[1, 2, 4],
rpn_num_upsample_filters=[256, 256, 256],
use_norm=True,
use_groupnorm=False,
num_groups=32,
use_direction_classifier=True,
use_sigmoid_score=False,
encode_background_as_zeros=True,
use_rotate_nms=True,
multiclass_nms=False,
nms_score_thresholds=None,
nms_pre_max_sizes=None,
nms_post_max_sizes=None,
nms_iou_thresholds=None,
target_assigner=None,
cls_loss_weight=1.0,
loc_loss_weight=1.0,
pos_cls_weight=1.0,
neg_cls_weight=1.0,
direction_loss_weight=1.0,
loss_norm_type=LossNormType.NormByNumPositives,
encode_rad_error_by_sin=False,
loc_loss_ftor=None,
cls_loss_ftor=None,
measure_time=False,
voxel_generator=None,
post_center_range=None,
dir_offset=0.0,
sin_error_factor=1.0,
nms_class_agnostic=False,
num_direction_bins=2,
direction_limit_offset=0,
name='voxelnet'):
super().__init__()
self.name = name
self._sin_error_factor = sin_error_factor
self._num_class = num_class
self._use_rotate_nms = use_rotate_nms
self._multiclass_nms = multiclass_nms
self._nms_score_thresholds = nms_score_thresholds
self._nms_pre_max_sizes = nms_pre_max_sizes
self._nms_post_max_sizes = nms_post_max_sizes
self._nms_iou_thresholds = nms_iou_thresholds
self._use_sigmoid_score = use_sigmoid_score
self._encode_background_as_zeros = encode_background_as_zeros
self._use_direction_classifier = use_direction_classifier
self._num_input_features = num_input_features
self._box_coder = target_assigner.box_coder
self.target_assigner = target_assigner
self.voxel_generator = voxel_generator
self._pos_cls_weight = pos_cls_weight
self._neg_cls_weight = neg_cls_weight
self._encode_rad_error_by_sin = encode_rad_error_by_sin
self._loss_norm_type = loss_norm_type
self._dir_loss_ftor = WeightedSoftmaxClassificationLoss()
self._diff_loc_loss_ftor = WeightedSmoothL1LocalizationLoss()
self._dir_offset = dir_offset
self._loc_loss_ftor = loc_loss_ftor
self._cls_loss_ftor = cls_loss_ftor
self._direction_loss_weight = direction_loss_weight
self._cls_loss_weight = cls_loss_weight
self._loc_loss_weight = loc_loss_weight
self._post_center_range = post_center_range or []
self.measure_time = measure_time
self._nms_class_agnostic = nms_class_agnostic
self._num_direction_bins = num_direction_bins
self._dir_limit_offset = direction_limit_offset
self.voxel_feature_extractor = voxel_encoder.get_vfe_class(
vfe_class_name)(
num_input_features,
use_norm,
num_filters=vfe_num_filters,
with_distance=with_distance,
voxel_size=self.voxel_generator.voxel_size,
pc_range=self.voxel_generator.point_cloud_range,
)
self.middle_feature_extractor = middle.get_middle_class(
middle_class_name)(output_shape,
use_norm,
num_input_features=middle_num_input_features,
num_filters_down1=middle_num_filters_d1,
num_filters_down2=middle_num_filters_d2)
self.rpn = rpn.get_rpn_class(rpn_class_name)(
use_norm=True,
num_class=num_class,
layer_nums=rpn_layer_nums,
layer_strides=rpn_layer_strides,
num_filters=rpn_num_filters,
upsample_strides=rpn_upsample_strides,
num_upsample_filters=rpn_num_upsample_filters,
num_input_features=rpn_num_input_features,
num_anchor_per_loc=target_assigner.num_anchors_per_location,
encode_background_as_zeros=encode_background_as_zeros,
use_direction_classifier=use_direction_classifier,
use_groupnorm=use_groupnorm,
num_groups=num_groups,
box_code_size=target_assigner.box_coder.code_size,
num_direction_bins=self._num_direction_bins)
self.rpn_acc = metrics.Accuracy(
dim=-1, encode_background_as_zeros=encode_background_as_zeros)
self.rpn_precision = metrics.Precision(dim=-1)
self.rpn_recall = metrics.Recall(dim=-1)
self.rpn_metrics = metrics.PrecisionRecall(
dim=-1,
thresholds=[0.1, 0.3, 0.5, 0.7, 0.8, 0.9, 0.95],
use_sigmoid_score=use_sigmoid_score,
encode_background_as_zeros=encode_background_as_zeros)
self.rpn_cls_loss = metrics.Scalar()
self.rpn_loc_loss = metrics.Scalar()
self.rpn_total_loss = metrics.Scalar()
self.register_buffer("global_step", torch.LongTensor(1).zero_())
self._time_dict = {}
self._time_total_dict = {}
self._time_count_dict = {}
def __init__(self,
output_shape,
num_class=2,
num_input_features=4,
vfe_class_name="VoxelFeatureExtractor",
vfe_num_filters=[32, 128],
with_distance=False,
middle_class_name="SparseMiddleExtractor",
middle_num_input_features=-1,
middle_num_filters_d1=[64],
middle_num_filters_d2=[64, 64],
rpn_class_name="RPN",
rpn_num_input_features=-1,
rpn_layer_nums=[3, 5, 5],
rpn_layer_strides=[2, 2, 2],
rpn_num_filters=[128, 128, 256],
rpn_upsample_strides=[1, 2, 4],
rpn_num_upsample_filters=[256, 256, 256],
use_norm=True,
use_groupnorm=False,
num_groups=32,
use_direction_classifier=True,
use_sigmoid_score=False,
encode_background_as_zeros=True,
use_rotate_nms=True,
multiclass_nms=False,
nms_score_thresholds=None,
nms_pre_max_sizes=None,
nms_post_max_sizes=None,
nms_iou_thresholds=None,
target_assigner=None,
cls_loss_weight=1.0,
loc_loss_weight=1.0,
pos_cls_weight=1.0,
neg_cls_weight=1.0,
direction_loss_weight=1.0,
loss_norm_type=LossNormType.NormByNumPositives,
encode_rad_error_by_sin=False,
loc_loss_ftor=None,
cls_loss_ftor=None,
measure_time=False,
voxel_generator=None,
post_center_range=None,
dir_offset=0.0,
sin_error_factor=1.0,
nms_class_agnostic=False,
num_direction_bins=2,
direction_limit_offset=0,
name='voxelnet'):
super().__init__()
self.name = name
self._sin_error_factor = sin_error_factor
self._num_class = num_class
self._use_rotate_nms = use_rotate_nms
self._multiclass_nms = multiclass_nms
self._nms_score_thresholds = nms_score_thresholds
self._nms_pre_max_sizes = nms_pre_max_sizes
self._nms_post_max_sizes = nms_post_max_sizes
self._nms_iou_thresholds = nms_iou_thresholds
self._use_sigmoid_score = use_sigmoid_score
self._encode_background_as_zeros = encode_background_as_zeros
self._use_direction_classifier = use_direction_classifier
self._num_input_features = num_input_features
self._box_coder = target_assigner.box_coder
self.target_assigner = target_assigner
self.voxel_generator = voxel_generator
self._pos_cls_weight = pos_cls_weight
self._neg_cls_weight = neg_cls_weight
self._encode_rad_error_by_sin = encode_rad_error_by_sin
self._loss_norm_type = loss_norm_type
self._dir_loss_ftor = WeightedSoftmaxClassificationLoss()
self._diff_loc_loss_ftor = WeightedSmoothL1LocalizationLoss()
self._dir_offset = dir_offset
self._loc_loss_ftor = loc_loss_ftor
self._cls_loss_ftor = cls_loss_ftor
self._direction_loss_weight = direction_loss_weight
self._cls_loss_weight = cls_loss_weight
self._loc_loss_weight = loc_loss_weight
self._post_center_range = post_center_range or []
self.measure_time = measure_time
self._nms_class_agnostic = nms_class_agnostic
self._num_direction_bins = num_direction_bins
self._dir_limit_offset = direction_limit_offset
self.voxel_feature_extractor = voxel_encoder.get_vfe_class(vfe_class_name)(
num_input_features,
use_norm,
num_filters=vfe_num_filters,
with_distance=with_distance,
voxel_size=self.voxel_generator.voxel_size,
pc_range=self.voxel_generator.point_cloud_range,
)
self.middle_feature_extractor = middle.get_middle_class(middle_class_name)(
output_shape,
use_norm,
num_input_features=middle_num_input_features,
num_filters_down1=middle_num_filters_d1,
num_filters_down2=middle_num_filters_d2)
self._rpn_layer_nums = rpn_layer_nums
self.rpn = rpn.get_rpn_class(rpn_class_name)(
use_norm=True,
num_class=num_class,
layer_nums=rpn_layer_nums,
layer_strides=rpn_layer_strides,
num_filters=rpn_num_filters,
upsample_strides=rpn_upsample_strides,
num_upsample_filters=rpn_num_upsample_filters,
num_input_features=rpn_num_input_features,
num_anchor_per_loc=target_assigner.num_anchors_per_location,
encode_background_as_zeros=encode_background_as_zeros,
use_direction_classifier=use_direction_classifier,
use_groupnorm=use_groupnorm,
num_groups=num_groups,
box_code_size=target_assigner.box_coder.code_size,
num_direction_bins=self._num_direction_bins)
self.rpn_acc = metrics.Accuracy(
dim=-1, encode_background_as_zeros=encode_background_as_zeros)
self.rpn_precision = metrics.Precision(dim=-1)
self.rpn_recall = metrics.Recall(dim=-1)
self.rpn_metrics = metrics.PrecisionRecall(
dim=-1,
thresholds=[0.1, 0.3, 0.5, 0.7, 0.8, 0.9, 0.95],
use_sigmoid_score=use_sigmoid_score,
encode_background_as_zeros=encode_background_as_zeros)
self.rpn_cls_loss = metrics.Scalar()
self.rpn_loc_loss = metrics.Scalar()
self.rpn_total_loss = metrics.Scalar()
self.register_buffer("global_step", torch.LongTensor(1).zero_())
self._time_dict = {
'PFE': [],
#'PillarGen': [],
#'PillarPrep': [],
#'PillarFeatureNet': [],
#'PillarScatter': [],
'RPN-stage-1': [],
'RPN-stage-2': [],
'RPN-stage-3': [],
'RPN-finalize': [],
'Predict': [],
'Pre-stage-1': [],
'Post-stage-1': [],
'End-to-end': [],}
self._cuda_event_dict = copy.deepcopy(self._time_dict)
self._det_num = 1
self._dump_scores = False
if self._dump_scores:
for i in range(1, len(rpn_layer_nums) + 1):
mypath = os.getenv('HOME') + "/scores_" + str(i)
if not os.path.exists(mypath):
os.makedirs(mypath)
#self._cuda_streams = []
#for i in range(4): # We are assuming 4 slices
# self._cuda_streams.append(torch.cuda.Stream())
self._num_class_with_bg = self._num_class
if not self._encode_background_as_zeros:
self._num_class_with_bg = self._num_class + 1
