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Commit 53435c62 authored by Yezhen Cong's avatar Yezhen Cong Committed by Tai-Wang
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[Refactor] Refactor code structure and docstrings (#803) 

* refactor points_in_boxes

* Merge same functions of three boxes

* More docstring fixes and unify x/y/z size

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Add "optional" and fix "Default"

* Remove None in function param type

* Fix unittest

* Add comments for NMS functions

* Merge methods of Points

* Add unittest

* Add optional and default value

* Fix box conversion and add unittest

* Fix comments

* Add unit test

* Indent

* Fix CI

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Remove useless \\

* Add unit test for box bev

* More unit tests and refine docstrings in box_np_ops

* Fix comment

* Add deprecation warning
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mmdet3d/models/dense_heads/centerpoint_head.py
View file @ 53435c62
......@@ -21,16 +21,16 @@ class SeparateHead(BaseModule):
Args:
in_channels (int): Input channels for conv_layer.
heads (dict): Conv information.
head_conv (int): Output channels.
head_conv (int, optional): Output channels.
Default: 64.
final_kernal (int): Kernal size for the last conv layer.
final_kernal (int, optional): Kernal size for the last conv layer.
Deafult: 1.
init_bias (float): Initial bias. Default: -2.19.
conv_cfg (dict): Config of conv layer.
init_bias (float, optional): Initial bias. Default: -2.19.
conv_cfg (dict, optional): Config of conv layer.
Default: dict(type='Conv2d')
norm_cfg (dict): Config of norm layer.
norm_cfg (dict, optional): Config of norm layer.
Default: dict(type='BN2d').
bias (str): Type of bias. Default: 'auto'.
bias (str, optional): Type of bias. Default: 'auto'.
"""
def __init__(self,
......@@ -100,17 +100,17 @@ class SeparateHead(BaseModule):
Returns:
dict[str: torch.Tensor]: contains the following keys:
-reg （torch.Tensor): 2D regression value with the \
-reg （torch.Tensor): 2D regression value with the
shape of [B, 2, H, W].
-height (torch.Tensor): Height value with the \
-height (torch.Tensor): Height value with the
shape of [B, 1, H, W].
-dim (torch.Tensor): Size value with the shape \
-dim (torch.Tensor): Size value with the shape
of [B, 3, H, W].
-rot (torch.Tensor): Rotation value with the \
-rot (torch.Tensor): Rotation value with the
shape of [B, 2, H, W].
-vel (torch.Tensor): Velocity value with the \
-vel (torch.Tensor): Velocity value with the
shape of [B, 2, H, W].
-heatmap (torch.Tensor): Heatmap with the shape of \
-heatmap (torch.Tensor): Heatmap with the shape of
[B, N, H, W].
"""
ret_dict = dict()
......@@ -131,18 +131,19 @@ class DCNSeparateHead(BaseModule):
Args:
in_channels (int): Input channels for conv_layer.
num_cls (int): Number of classes.
heads (dict): Conv information.
dcn_config (dict): Config of dcn layer.
num_cls (int): Output channels.
head_conv (int, optional): Output channels.
Default: 64.
final_kernal (int): Kernal size for the last conv layer.
Deafult: 1.
init_bias (float): Initial bias. Default: -2.19.
conv_cfg (dict): Config of conv layer.
final_kernal (int, optional): Kernal size for the last conv
layer. Deafult: 1.
init_bias (float, optional): Initial bias. Default: -2.19.
conv_cfg (dict, optional): Config of conv layer.
Default: dict(type='Conv2d')
norm_cfg (dict): Config of norm layer.
norm_cfg (dict, optional): Config of norm layer.
Default: dict(type='BN2d').
bias (str): Type of bias. Default: 'auto'.
bias (str, optional): Type of bias. Default: 'auto'.
""" # noqa: W605
def __init__(self,
......@@ -215,17 +216,17 @@ class DCNSeparateHead(BaseModule):
Returns:
dict[str: torch.Tensor]: contains the following keys:
-reg （torch.Tensor): 2D regression value with the \
-reg （torch.Tensor): 2D regression value with the
shape of [B, 2, H, W].
-height (torch.Tensor): Height value with the \
-height (torch.Tensor): Height value with the
shape of [B, 1, H, W].
-dim (torch.Tensor): Size value with the shape \
-dim (torch.Tensor): Size value with the shape
of [B, 3, H, W].
-rot (torch.Tensor): Rotation value with the \
-rot (torch.Tensor): Rotation value with the
shape of [B, 2, H, W].
-vel (torch.Tensor): Velocity value with the \
-vel (torch.Tensor): Velocity value with the
shape of [B, 2, H, W].
-heatmap (torch.Tensor): Heatmap with the shape of \
-heatmap (torch.Tensor): Heatmap with the shape of
[B, N, H, W].
"""
center_feat = self.feature_adapt_cls(x)
......@@ -243,31 +244,30 @@ class CenterHead(BaseModule):
"""CenterHead for CenterPoint.
Args:
mode (str): Mode of the head. Default: '3d'.
in_channels (list[int] | int): Channels of the input feature map.
Default: [128].
tasks (list[dict]): Task information including class number
in_channels (list[int] | int, optional): Channels of the input
feature map. Default: [128].
tasks (list[dict], optional): Task information including class number
and class names. Default: None.
dataset (str): Name of the dataset. Default: 'nuscenes'.
weight (float): Weight for location loss. Default: 0.25.
code_weights (list[int]): Code weights for location loss. Default: [].
common_heads (dict): Conv information for common heads.
train_cfg (dict, optional): Train-time configs. Default: None.
test_cfg (dict, optional): Test-time configs. Default: None.
bbox_coder (dict, optional): Bbox coder configs. Default: None.
common_heads (dict, optional): Conv information for common heads.
Default: dict().
loss_cls (dict): Config of classification loss function.
loss_cls (dict, optional): Config of classification loss function.
Default: dict(type='GaussianFocalLoss', reduction='mean').
loss_bbox (dict): Config of regression loss function.
loss_bbox (dict, optional): Config of regression loss function.
Default: dict(type='L1Loss', reduction='none').
separate_head (dict): Config of separate head. Default: dict(
separate_head (dict, optional): Config of separate head. Default: dict(
type='SeparateHead', init_bias=-2.19, final_kernel=3)
share_conv_channel (int): Output channels for share_conv_layer.
Default: 64.
num_heatmap_convs (int): Number of conv layers for heatmap conv layer.
Default: 2.
conv_cfg (dict): Config of conv layer.
share_conv_channel (int, optional): Output channels for share_conv
layer. Default: 64.
num_heatmap_convs (int, optional): Number of conv layers for heatmap
conv layer. Default: 2.
conv_cfg (dict, optional): Config of conv layer.
Default: dict(type='Conv2d')
norm_cfg (dict): Config of norm layer.
norm_cfg (dict, optional): Config of norm layer.
Default: dict(type='BN2d').
bias (str): Type of bias. Default: 'auto'.
bias (str, optional): Type of bias. Default: 'auto'.
"""
def __init__(self,
......@@ -366,8 +366,8 @@ class CenterHead(BaseModule):
feat (torch.tensor): Feature map with the shape of [B, H*W, 10].
ind (torch.Tensor): Index of the ground truth boxes with the
shape of [B, max_obj].
mask (torch.Tensor): Mask of the feature map with the shape
of [B, max_obj]. Default: None.
mask (torch.Tensor, optional): Mask of the feature map with the
shape of [B, max_obj]. Default: None.
Returns:
torch.Tensor: Feature map after gathering with the shape
......@@ -403,14 +403,14 @@ class CenterHead(BaseModule):
Returns:
Returns:
tuple[list[torch.Tensor]]: Tuple of target including \
tuple[list[torch.Tensor]]: Tuple of target including
the following results in order.
- list[torch.Tensor]: Heatmap scores.
- list[torch.Tensor]: Ground truth boxes.
- list[torch.Tensor]: Indexes indicating the \
- list[torch.Tensor]: Indexes indicating the
position of the valid boxes.
- list[torch.Tensor]: Masks indicating which \
- list[torch.Tensor]: Masks indicating which
boxes are valid.
"""
heatmaps, anno_boxes, inds, masks = multi_apply(
......@@ -437,14 +437,14 @@ class CenterHead(BaseModule):
gt_labels_3d (torch.Tensor): Labels of boxes.
Returns:
tuple[list[torch.Tensor]]: Tuple of target including \
tuple[list[torch.Tensor]]: Tuple of target including
the following results in order.
- list[torch.Tensor]: Heatmap scores.
- list[torch.Tensor]: Ground truth boxes.
- list[torch.Tensor]: Indexes indicating the position \
- list[torch.Tensor]: Indexes indicating the position
of the valid boxes.
- list[torch.Tensor]: Masks indicating which boxes \
- list[torch.Tensor]: Masks indicating which boxes
are valid.
"""
device = gt_labels_3d.device
......@@ -728,11 +728,11 @@ class CenterHead(BaseModule):
Returns:
list[dict[str: torch.Tensor]]: contains the following keys:
-bboxes (torch.Tensor): Prediction bboxes after nms with the \
-bboxes (torch.Tensor): Prediction bboxes after nms with the
shape of [N, 9].
-scores (torch.Tensor): Prediction scores after nms with the \
-scores (torch.Tensor): Prediction scores after nms with the
shape of [N].
-labels (torch.Tensor): Prediction labels after nms with the \
-labels (torch.Tensor): Prediction labels after nms with the
shape of [N].
"""
predictions_dicts = []
......@@ -781,7 +781,7 @@ class CenterHead(BaseModule):
boxes_for_nms,
top_scores,
thresh=self.test_cfg['nms_thr'],
pre_maxsize=self.test_cfg['pre_max_size'],
pre_max_size=self.test_cfg['pre_max_size'],
post_max_size=self.test_cfg['post_max_size'])
else:
selected = []
......
mmdet3d/models/dense_heads/fcos_mono3d_head.py
View file @ 53435c62
......@@ -21,25 +21,25 @@ class FCOSMono3DHead(AnchorFreeMono3DHead):
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
regress_ranges (tuple[tuple[int, int]]): Regress range of multiple
regress_ranges (tuple[tuple[int, int]], optional): Regress range of multiple
level points.
center_sampling (bool): If true, use center sampling. Default: True.
center_sample_radius (float): Radius of center sampling. Default: 1.5.
norm_on_bbox (bool): If true, normalize the regression targets
center_sampling (bool, optional): If true, use center sampling. Default: True.
center_sample_radius (float, optional): Radius of center sampling. Default: 1.5.
norm_on_bbox (bool, optional): If true, normalize the regression targets
with FPN strides. Default: True.
centerness_on_reg (bool): If true, position centerness on the
centerness_on_reg (bool, optional): If true, position centerness on the
regress branch. Please refer to https://github.com/tianzhi0549/FCOS/issues/89#issuecomment-516877042.
Default: True.
centerness_alpha: Parameter used to adjust the intensity attenuation
from the center to the periphery. Default: 2.5.
loss_cls (dict): Config of classification loss.
loss_bbox (dict): Config of localization loss.
loss_dir (dict): Config of direction classification loss.
loss_attr (dict): Config of attribute classification loss.
loss_centerness (dict): Config of centerness loss.
norm_cfg (dict): dictionary to construct and config norm layer.
centerness_alpha (int, optional): Parameter used to adjust the intensity
attenuation from the center to the periphery. Default: 2.5.
loss_cls (dict, optional): Config of classification loss.
loss_bbox (dict, optional): Config of localization loss.
loss_dir (dict, optional): Config of direction classification loss.
loss_attr (dict, optional): Config of attribute classification loss.
loss_centerness (dict, optional): Config of centerness loss.
norm_cfg (dict, optional): dictionary to construct and config norm layer.
Default: norm_cfg=dict(type='GN', num_groups=32, requires_grad=True).
centerness_branch (tuple[int]): Channels for centerness branch.
centerness_branch (tuple[int], optional): Channels for centerness branch.
Default: (64, ).
""" # noqa: E501
......@@ -153,7 +153,7 @@ class FCOSMono3DHead(AnchorFreeMono3DHead):
is True.
Returns:
tuple: scores for each class, bbox and direction class \
tuple: scores for each class, bbox and direction class
predictions, centerness predictions of input feature maps.
"""
cls_score, bbox_pred, dir_cls_pred, attr_pred, cls_feat, reg_feat = \
......@@ -201,7 +201,7 @@ class FCOSMono3DHead(AnchorFreeMono3DHead):
the 7th dimension is rotation dimension.
Returns:
tuple[torch.Tensor]: ``boxes1`` and ``boxes2`` whose 7th \
tuple[torch.Tensor]: ``boxes1`` and ``boxes2`` whose 7th
dimensions are changed.
"""
rad_pred_encoding = torch.sin(boxes1[..., 6:7]) * torch.cos(
......@@ -295,7 +295,7 @@ class FCOSMono3DHead(AnchorFreeMono3DHead):
attr_labels (list[Tensor]): Attributes indices of each box.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
gt_bboxes_ignore (list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
Returns:
......@@ -507,11 +507,11 @@ class FCOSMono3DHead(AnchorFreeMono3DHead):
rescale (bool): If True, return boxes in original image space
Returns:
list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. \
The first item is an (n, 5) tensor, where the first 4 columns \
are bounding box positions (tl_x, tl_y, br_x, br_y) and the \
5-th column is a score between 0 and 1. The second item is a \
(n,) tensor where each item is the predicted class label of \
list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
The first item is an (n, 5) tensor, where the first 4 columns
are bounding box positions (tl_x, tl_y, br_x, br_y) and the
5-th column is a score between 0 and 1. The second item is a
(n,) tensor where each item is the predicted class label of
the corresponding box.
"""
assert len(cls_scores) == len(bbox_preds) == len(dir_cls_preds) == \
......@@ -580,7 +580,7 @@ class FCOSMono3DHead(AnchorFreeMono3DHead):
bbox_preds (list[Tensor]): Box energies / deltas for a single scale
level with shape (num_points * bbox_code_size, H, W).
dir_cls_preds (list[Tensor]): Box scores for direction class
predictions on a single scale level with shape \
predictions on a single scale level with shape
(num_points * 2, H, W)
attr_preds (list[Tensor]): Attribute scores for each scale level
Has shape (N, num_points * num_attrs, H, W)
......@@ -700,12 +700,12 @@ class FCOSMono3DHead(AnchorFreeMono3DHead):
def pts2Dto3D(points, view):
"""
Args:
points (torch.Tensor): points in 2D images, [N, 3], \
points (torch.Tensor): points in 2D images, [N, 3],
3 corresponds with x, y in the image and depth.
view (np.ndarray): camera instrinsic, [3, 3]
Returns:
torch.Tensor: points in 3D space. [N, 3], \
torch.Tensor: points in 3D space. [N, 3],
3 corresponds with x, y, z in 3D space.
"""
assert view.shape[0] <= 4
......@@ -767,8 +767,8 @@ class FCOSMono3DHead(AnchorFreeMono3DHead):
Returns:
tuple:
concat_lvl_labels (list[Tensor]): Labels of each level. \
concat_lvl_bbox_targets (list[Tensor]): BBox targets of each \
concat_lvl_labels (list[Tensor]): Labels of each level.
concat_lvl_bbox_targets (list[Tensor]): BBox targets of each
level.
"""
assert len(points) == len(self.regress_ranges)
......
mmdet3d/models/dense_heads/groupfree3d_head.py
View file @ 53435c62
......@@ -25,13 +25,13 @@ class PointsObjClsModule(BaseModule):
Args:
in_channel (int): number of channels of seed point features.
num_convs (int): number of conv layers.
num_convs (int, optional): number of conv layers.
Default: 3.
conv_cfg (dict): Config of convolution.
conv_cfg (dict, optional): Config of convolution.
Default: dict(type='Conv1d').
norm_cfg (dict): Config of normalization.
norm_cfg (dict, optional): Config of normalization.
Default: dict(type='BN1d').
act_cfg (dict): Config of activation.
act_cfg (dict, optional): Config of activation.
Default: dict(type='ReLU').
"""
......@@ -405,15 +405,15 @@ class GroupFree3DHead(BaseModule):
Args:
bbox_preds (dict): Predictions from forward of vote head.
points (list[torch.Tensor]): Input points.
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth \
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
bboxes of each sample.
gt_labels_3d (list[torch.Tensor]): Labels of each sample.
pts_semantic_mask (None | list[torch.Tensor]): Point-wise
pts_semantic_mask (list[torch.Tensor]): Point-wise
semantic mask.
pts_instance_mask (None | list[torch.Tensor]): Point-wise
pts_instance_mask (list[torch.Tensor]): Point-wise
instance mask.
img_metas (list[dict]): Contain pcd and img's meta info.
gt_bboxes_ignore (None | list[torch.Tensor]): Specify
gt_bboxes_ignore (list[torch.Tensor]): Specify
which bounding.
ret_target (Bool): Return targets or not.
......@@ -545,12 +545,12 @@ class GroupFree3DHead(BaseModule):
Args:
points (list[torch.Tensor]): Points of each batch.
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth \
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
bboxes of each batch.
gt_labels_3d (list[torch.Tensor]): Labels of each batch.
pts_semantic_mask (None | list[torch.Tensor]): Point-wise semantic
pts_semantic_mask (list[torch.Tensor]): Point-wise semantic
label of each batch.
pts_instance_mask (None | list[torch.Tensor]): Point-wise instance
pts_instance_mask (list[torch.Tensor]): Point-wise instance
label of each batch.
bbox_preds (torch.Tensor): Bounding box predictions of vote head.
max_gt_num (int): Max number of GTs for single batch.
......@@ -657,12 +657,12 @@ class GroupFree3DHead(BaseModule):
Args:
points (torch.Tensor): Points of each batch.
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): Ground truth \
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): Ground truth
boxes of each batch.
gt_labels_3d (torch.Tensor): Labels of each batch.
pts_semantic_mask (None | torch.Tensor): Point-wise semantic
pts_semantic_mask (torch.Tensor): Point-wise semantic
label of each batch.
pts_instance_mask (None | torch.Tensor): Point-wise instance
pts_instance_mask (torch.Tensor): Point-wise instance
label of each batch.
max_gt_nums (int): Max number of GTs for single batch.
seed_points (torch.Tensor): Coordinates of seed points.
......@@ -710,7 +710,7 @@ class GroupFree3DHead(BaseModule):
if self.bbox_coder.with_rot:
vote_targets = points.new_zeros([num_points, 4 * self.gt_per_seed])
vote_target_idx = points.new_zeros([num_points], dtype=torch.long)
box_indices_all = gt_bboxes_3d.points_in_boxes(points)
box_indices_all = gt_bboxes_3d.points_in_boxes_part(points)
for i in range(gt_labels_3d.shape[0]):
box_indices = box_indices_all[:, i]
indices = torch.nonzero(
......@@ -951,7 +951,7 @@ class GroupFree3DHead(BaseModule):
box_dim=bbox.shape[-1],
with_yaw=self.bbox_coder.with_rot,
origin=(0.5, 0.5, 0.5))
box_indices = bbox.points_in_boxes_batch(points)
box_indices = bbox.points_in_boxes_all(points)
corner3d = bbox.corners
minmax_box3d = corner3d.new(torch.Size((corner3d.shape[0], 6)))
......
mmdet3d/models/dense_heads/parta2_rpn_head.py
View file @ 53435c62
......@@ -100,20 +100,20 @@ class PartA2RPNHead(Anchor3DHead):
bbox_preds (list[torch.Tensor]): Multi-level bbox predictions.
dir_cls_preds (list[torch.Tensor]): Multi-level direction
class predictions.
gt_bboxes (list[:obj:`BaseInstance3DBoxes`]): Ground truth boxes \
gt_bboxes (list[:obj:`BaseInstance3DBoxes`]): Ground truth boxes
of each sample.
gt_labels (list[torch.Tensor]): Labels of each sample.
input_metas (list[dict]): Point cloud and image's meta info.
gt_bboxes_ignore (None | list[torch.Tensor]): Specify
gt_bboxes_ignore (list[torch.Tensor]): Specify
which bounding.
Returns:
dict[str, list[torch.Tensor]]: Classification, bbox, and \
dict[str, list[torch.Tensor]]: Classification, bbox, and
direction losses of each level.
- loss_rpn_cls (list[torch.Tensor]): Classification losses.
- loss_rpn_bbox (list[torch.Tensor]): Box regression losses.
- loss_rpn_dir (list[torch.Tensor]): Direction classification \
- loss_rpn_dir (list[torch.Tensor]): Direction classification
losses.
"""
loss_dict = super().loss(cls_scores, bbox_preds, dir_cls_preds,
......@@ -143,7 +143,7 @@ class PartA2RPNHead(Anchor3DHead):
mlvl_anchors (List[torch.Tensor]): Multi-level anchors
in single batch.
input_meta (list[dict]): Contain pcd and img's meta info.
cfg (None | :obj:`ConfigDict`): Training or testing config.
cfg (:obj:`ConfigDict`): Training or testing config.
rescale (list[torch.Tensor]): whether th rescale bbox.
Returns:
......@@ -240,7 +240,7 @@ class PartA2RPNHead(Anchor3DHead):
Multi-level bbox.
score_thr (int): Score threshold.
max_num (int): Max number of bboxes after nms.
cfg (None | :obj:`ConfigDict`): Training or testing config.
cfg (:obj:`ConfigDict`): Training or testing config.
input_meta (dict): Contain pcd and img's meta info.
Returns:
......
mmdet3d/models/dense_heads/shape_aware_head.py
View file @ 53435c62
......@@ -30,15 +30,17 @@ class BaseShapeHead(BaseModule):
num_base_anchors (int): Number of anchors per location.
box_code_size (int): The dimension of boxes to be encoded.
in_channels (int): Input channels for convolutional layers.
shared_conv_channels (tuple): Channels for shared convolutional \
layers. Default: (64, 64). \
shared_conv_strides (tuple): Strides for shared convolutional \
layers. Default: (1, 1).
use_direction_classifier (bool, optional): Whether to use direction \
shared_conv_channels (tuple, optional): Channels for shared
convolutional layers. Default: (64, 64).
shared_conv_strides (tuple, optional): Strides for shared
convolutional layers. Default: (1, 1).
use_direction_classifier (bool, optional): Whether to use direction
classifier. Default: True.
conv_cfg (dict): Config of conv layer. Default: dict(type='Conv2d')
norm_cfg (dict): Config of norm layer. Default: dict(type='BN2d').
bias (bool|str, optional): Type of bias. Default: False.
conv_cfg (dict, optional): Config of conv layer.
Default: dict(type='Conv2d')
norm_cfg (dict, optional): Config of norm layer.
Default: dict(type='BN2d').
bias (bool | str, optional): Type of bias. Default: False.
"""
def __init__(self,
......@@ -127,11 +129,11 @@ class BaseShapeHead(BaseModule):
[B, C, H, W].
Returns:
dict[torch.Tensor]: Contain score of each class, bbox \
regression and direction classification predictions. \
Note that all the returned tensors are reshaped as \
[bs*num_base_anchors*H*W, num_cls/box_code_size/dir_bins]. \
It is more convenient to concat anchors for different \
dict[torch.Tensor]: Contain score of each class, bbox
regression and direction classification predictions.
Note that all the returned tensors are reshaped as
[bs*num_base_anchors*H*W, num_cls/box_code_size/dir_bins].
It is more convenient to concat anchors for different
classes even though they have different feature map sizes.
"""
x = self.shared_conv(x)
......@@ -168,9 +170,9 @@ class ShapeAwareHead(Anchor3DHead):
Args:
tasks (dict): Shape-aware groups of multi-class objects.
assign_per_class (bool, optional): Whether to do assignment for each \
assign_per_class (bool, optional): Whether to do assignment for each
class. Default: True.
kwargs (dict): Other arguments are the same as those in \
kwargs (dict): Other arguments are the same as those in
:class:`Anchor3DHead`.
"""
......@@ -217,7 +219,7 @@ class ShapeAwareHead(Anchor3DHead):
Args:
x (torch.Tensor): Input features.
Returns:
tuple[torch.Tensor]: Contain score of each class, bbox \
tuple[torch.Tensor]: Contain score of each class, bbox
regression and direction classification predictions.
"""
results = []
......@@ -263,7 +265,7 @@ class ShapeAwareHead(Anchor3DHead):
num_total_samples (int): The number of valid samples.
Returns:
tuple[torch.Tensor]: Losses of class, bbox \
tuple[torch.Tensor]: Losses of class, bbox
and direction, respectively.
"""
# classification loss
......@@ -325,16 +327,16 @@ class ShapeAwareHead(Anchor3DHead):
of each sample.
gt_labels (list[torch.Tensor]): Gt labels of each sample.
input_metas (list[dict]): Contain pcd and img's meta info.
gt_bboxes_ignore (None | list[torch.Tensor]): Specify
gt_bboxes_ignore (list[torch.Tensor]): Specify
which bounding.
Returns:
dict[str, list[torch.Tensor]]: Classification, bbox, and \
dict[str, list[torch.Tensor]]: Classification, bbox, and
direction losses of each level.
- loss_cls (list[torch.Tensor]): Classification losses.
- loss_bbox (list[torch.Tensor]): Box regression losses.
- loss_dir (list[torch.Tensor]): Direction classification \
- loss_dir (list[torch.Tensor]): Direction classification
losses.
"""
device = cls_scores[0].device
......@@ -388,7 +390,7 @@ class ShapeAwareHead(Anchor3DHead):
dir_cls_preds (list[torch.Tensor]): Multi-level direction
class predictions.
input_metas (list[dict]): Contain pcd and img's meta info.
cfg (None | :obj:`ConfigDict`): Training or testing config.
cfg (:obj:`ConfigDict`, optional): Training or testing config.
Default: None.
rescale (list[torch.Tensor], optional): Whether to rescale bbox.
Default: False.
......@@ -443,8 +445,8 @@ class ShapeAwareHead(Anchor3DHead):
mlvl_anchors (List[torch.Tensor]): Multi-level anchors
in single batch.
input_meta (list[dict]): Contain pcd and img's meta info.
cfg (None | :obj:`ConfigDict`): Training or testing config.
rescale (list[torch.Tensor], optional): whether to rescale bbox. \
cfg (:obj:`ConfigDict`): Training or testing config.
rescale (list[torch.Tensor], optional): whether to rescale bbox.
Default: False.
Returns:
......
mmdet3d/models/dense_heads/ssd_3d_head.py
View file @ 53435c62
......@@ -128,15 +128,15 @@ class SSD3DHead(VoteHead):
Args:
bbox_preds (dict): Predictions from forward of SSD3DHead.
points (list[torch.Tensor]): Input points.
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth \
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
bboxes of each sample.
gt_labels_3d (list[torch.Tensor]): Labels of each sample.
pts_semantic_mask (None | list[torch.Tensor]): Point-wise
pts_semantic_mask (list[torch.Tensor]): Point-wise
semantic mask.
pts_instance_mask (None | list[torch.Tensor]): Point-wise
pts_instance_mask (list[torch.Tensor]): Point-wise
instance mask.
img_metas (list[dict]): Contain pcd and img's meta info.
gt_bboxes_ignore (None | list[torch.Tensor]): Specify
gt_bboxes_ignore (list[torch.Tensor]): Specify
which bounding.
Returns:
......@@ -231,12 +231,12 @@ class SSD3DHead(VoteHead):
Args:
points (list[torch.Tensor]): Points of each batch.
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth \
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
bboxes of each batch.
gt_labels_3d (list[torch.Tensor]): Labels of each batch.
pts_semantic_mask (None | list[torch.Tensor]): Point-wise semantic
pts_semantic_mask (list[torch.Tensor]): Point-wise semantic
label of each batch.
pts_instance_mask (None | list[torch.Tensor]): Point-wise instance
pts_instance_mask (list[torch.Tensor]): Point-wise instance
label of each batch.
bbox_preds (torch.Tensor): Bounding box predictions of ssd3d head.
......@@ -320,12 +320,12 @@ class SSD3DHead(VoteHead):
Args:
points (torch.Tensor): Points of each batch.
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): Ground truth \
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): Ground truth
boxes of each batch.
gt_labels_3d (torch.Tensor): Labels of each batch.
pts_semantic_mask (None | torch.Tensor): Point-wise semantic
pts_semantic_mask (torch.Tensor): Point-wise semantic
label of each batch.
pts_instance_mask (None | torch.Tensor): Point-wise instance
pts_instance_mask (torch.Tensor): Point-wise instance
label of each batch.
aggregated_points (torch.Tensor): Aggregated points from
candidate points layer.
......@@ -494,7 +494,7 @@ class SSD3DHead(VoteHead):
origin=(0.5, 0.5, 0.5))
if isinstance(bbox, (LiDARInstance3DBoxes, DepthInstance3DBoxes)):
box_indices = bbox.points_in_boxes_batch(points)
box_indices = bbox.points_in_boxes_all(points)
nonempty_box_mask = box_indices.T.sum(1) >= 0
else:
raise NotImplementedError('Unsupported bbox type!')
......@@ -550,7 +550,7 @@ class SSD3DHead(VoteHead):
inside bbox and the index of box where each point are in.
"""
if isinstance(bboxes_3d, (LiDARInstance3DBoxes, DepthInstance3DBoxes)):
points_mask = bboxes_3d.points_in_boxes_batch(points)
points_mask = bboxes_3d.points_in_boxes_all(points)
assignment = points_mask.argmax(dim=-1)
else:
raise NotImplementedError('Unsupported bbox type!')
......
mmdet3d/models/dense_heads/train_mixins.py
View file @ 53435c62
......@@ -25,7 +25,7 @@ class AnchorTrainMixin(object):
gt_bboxes_list (list[:obj:`BaseInstance3DBoxes`]): Ground truth
bboxes of each image.
input_metas (list[dict]): Meta info of each image.
gt_bboxes_ignore_list (None | list): Ignore list of gt bboxes.
gt_bboxes_ignore_list (list): Ignore list of gt bboxes.
gt_labels_list (list[torch.Tensor]): Gt labels of batches.
label_channels (int): The channel of labels.
num_classes (int): The number of classes.
......
mmdet3d/models/dense_heads/vote_head.py
View file @ 53435c62
......@@ -234,15 +234,15 @@ class VoteHead(BaseModule):
Args:
bbox_preds (dict): Predictions from forward of vote head.
points (list[torch.Tensor]): Input points.
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth \
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
bboxes of each sample.
gt_labels_3d (list[torch.Tensor]): Labels of each sample.
pts_semantic_mask (None | list[torch.Tensor]): Point-wise
pts_semantic_mask (list[torch.Tensor]): Point-wise
semantic mask.
pts_instance_mask (None | list[torch.Tensor]): Point-wise
pts_instance_mask (list[torch.Tensor]): Point-wise
instance mask.
img_metas (list[dict]): Contain pcd and img's meta info.
gt_bboxes_ignore (None | list[torch.Tensor]): Specify
gt_bboxes_ignore (list[torch.Tensor]): Specify
which bounding.
ret_target (Bool): Return targets or not.
......@@ -358,12 +358,12 @@ class VoteHead(BaseModule):
Args:
points (list[torch.Tensor]): Points of each batch.
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth \
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
bboxes of each batch.
gt_labels_3d (list[torch.Tensor]): Labels of each batch.
pts_semantic_mask (None | list[torch.Tensor]): Point-wise semantic
pts_semantic_mask (list[torch.Tensor]): Point-wise semantic
label of each batch.
pts_instance_mask (None | list[torch.Tensor]): Point-wise instance
pts_instance_mask (list[torch.Tensor]): Point-wise instance
label of each batch.
bbox_preds (torch.Tensor): Bounding box predictions of vote head.
......@@ -447,12 +447,12 @@ class VoteHead(BaseModule):
Args:
points (torch.Tensor): Points of each batch.
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): Ground truth \
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): Ground truth
boxes of each batch.
gt_labels_3d (torch.Tensor): Labels of each batch.
pts_semantic_mask (None | torch.Tensor): Point-wise semantic
pts_semantic_mask (torch.Tensor): Point-wise semantic
label of each batch.
pts_instance_mask (None | torch.Tensor): Point-wise instance
pts_instance_mask (torch.Tensor): Point-wise instance
label of each batch.
aggregated_points (torch.Tensor): Aggregated points from
vote aggregation layer.
......@@ -471,7 +471,7 @@ class VoteHead(BaseModule):
vote_target_masks = points.new_zeros([num_points],
dtype=torch.long)
vote_target_idx = points.new_zeros([num_points], dtype=torch.long)
box_indices_all = gt_bboxes_3d.points_in_boxes_batch(points)
box_indices_all = gt_bboxes_3d.points_in_boxes_all(points)
for i in range(gt_labels_3d.shape[0]):
box_indices = box_indices_all[:, i]
indices = torch.nonzero(
......@@ -621,7 +621,7 @@ class VoteHead(BaseModule):
box_dim=bbox.shape[-1],
with_yaw=self.bbox_coder.with_rot,
origin=(0.5, 0.5, 0.5))
box_indices = bbox.points_in_boxes_batch(points)
box_indices = bbox.points_in_boxes_all(points)
corner3d = bbox.corners
minmax_box3d = corner3d.new(torch.Size((corner3d.shape[0], 6)))
......
mmdet3d/models/detectors/centerpoint.py
View file @ 53435c62
......@@ -97,7 +97,8 @@ class CenterPoint(MVXTwoStageDetector):
Args:
feats (list[torch.Tensor]): Feature of point cloud.
img_metas (list[dict]): Meta information of samples.
rescale (bool): Whether to rescale bboxes. Default: False.
rescale (bool, optional): Whether to rescale bboxes.
Default: False.
Returns:
dict: Returned bboxes consists of the following keys:
......
mmdet3d/models/detectors/groupfree3dnet.py
View file @ 53435c62
......@@ -38,11 +38,11 @@ class GroupFree3DNet(SingleStage3DDetector):
img_metas (list): Image metas.
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): gt bboxes of each batch.
gt_labels_3d (list[torch.Tensor]): gt class labels of each batch.
pts_semantic_mask (None | list[torch.Tensor]): point-wise semantic
pts_semantic_mask (list[torch.Tensor]): point-wise semantic
label of each batch.
pts_instance_mask (None | list[torch.Tensor]): point-wise instance
pts_instance_mask (list[torch.Tensor]): point-wise instance
label of each batch.
gt_bboxes_ignore (None | list[torch.Tensor]): Specify
gt_bboxes_ignore (list[torch.Tensor]): Specify
which bounding.
Returns:
......
mmdet3d/models/detectors/h3dnet.py
View file @ 53435c62
......@@ -47,11 +47,11 @@ class H3DNet(TwoStage3DDetector):
img_metas (list): Image metas.
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): gt bboxes of each batch.
gt_labels_3d (list[torch.Tensor]): gt class labels of each batch.
pts_semantic_mask (None | list[torch.Tensor]): point-wise semantic
pts_semantic_mask (list[torch.Tensor]): point-wise semantic
label of each batch.
pts_instance_mask (None | list[torch.Tensor]): point-wise instance
pts_instance_mask (list[torch.Tensor]): point-wise instance
label of each batch.
gt_bboxes_ignore (None | list[torch.Tensor]): Specify
gt_bboxes_ignore (list[torch.Tensor]): Specify
which bounding.
Returns:
......
mmdet3d/models/detectors/imvotenet.py
View file @ 53435c62
......@@ -149,21 +149,21 @@ class ImVoteNet(Base3DDetector):
if self.with_img_backbone:
if img_pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated \
key, please consider using init_cfg')
warnings.warn('DeprecationWarning: pretrained is a deprecated '
'key, please consider using init_cfg.')
self.img_backbone.init_cfg = dict(
type='Pretrained', checkpoint=img_pretrained)
if self.with_img_roi_head:
if img_pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated \
key, please consider using init_cfg')
warnings.warn('DeprecationWarning: pretrained is a deprecated '
'key, please consider using init_cfg.')
self.img_roi_head.init_cfg = dict(
type='Pretrained', checkpoint=img_pretrained)
if self.with_pts_backbone:
if img_pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated \
key, please consider using init_cfg')
warnings.warn('DeprecationWarning: pretrained is a deprecated '
'key, please consider using init_cfg.')
self.pts_backbone.init_cfg = dict(
type='Pretrained', checkpoint=pts_pretrained)
......@@ -393,9 +393,9 @@ class ImVoteNet(Base3DDetector):
with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[torch.Tensor]): class indices for each
2d bounding box.
gt_bboxes_ignore (None | list[torch.Tensor]): specify which
gt_bboxes_ignore (list[torch.Tensor]): specify which
2d bounding boxes can be ignored when computing the loss.
gt_masks (None | torch.Tensor): true segmentation masks for each
gt_masks (torch.Tensor): true segmentation masks for each
2d bbox, used if the architecture supports a segmentation task.
proposals: override rpn proposals (2d) with custom proposals.
Use when `with_rpn` is False.
......@@ -403,9 +403,9 @@ class ImVoteNet(Base3DDetector):
not supported yet.
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): 3d gt bboxes.
gt_labels_3d (list[torch.Tensor]): gt class labels for 3d bboxes.
pts_semantic_mask (None | list[torch.Tensor]): point-wise semantic
pts_semantic_mask (list[torch.Tensor]): point-wise semantic
label of each batch.
pts_instance_mask (None | list[torch.Tensor]): point-wise instance
pts_instance_mask (list[torch.Tensor]): point-wise instance
label of each batch.
Returns:
......
mmdet3d/models/detectors/mvx_two_stage.py
View file @ 53435c62
......@@ -84,21 +84,21 @@ class MVXTwoStageDetector(Base3DDetector):
if self.with_img_backbone:
if img_pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated \
key, please consider using init_cfg')
warnings.warn('DeprecationWarning: pretrained is a deprecated '
'key, please consider using init_cfg.')
self.img_backbone.init_cfg = dict(
type='Pretrained', checkpoint=img_pretrained)
if self.with_img_roi_head:
if img_pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated \
key, please consider using init_cfg')
warnings.warn('DeprecationWarning: pretrained is a deprecated '
'key, please consider using init_cfg.')
self.img_roi_head.init_cfg = dict(
type='Pretrained', checkpoint=img_pretrained)
if self.with_pts_backbone:
if pts_pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated \
key, please consider using init_cfg')
if img_pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated '
'key, please consider using init_cfg.')
self.pts_backbone.init_cfg = dict(
type='Pretrained', checkpoint=pts_pretrained)
......@@ -260,7 +260,7 @@ class MVXTwoStageDetector(Base3DDetector):
of 2D boxes in images. Defaults to None.
gt_bboxes (list[torch.Tensor], optional): Ground truth 2D boxes in
images. Defaults to None.
img (torch.Tensor optional): Images of each sample with shape
img (torch.Tensor, optional): Images of each sample with shape
(N, C, H, W). Defaults to None.
proposals ([list[torch.Tensor], optional): Predicted proposals
used for training Fast RCNN. Defaults to None.
......
mmdet3d/models/detectors/single_stage_mono3d.py
View file @ 53435c62
......@@ -48,14 +48,15 @@ class SingleStageMono3DDetector(SingleStageDetector):
image in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): Class indices corresponding to each box
gt_bboxes_3d (list[Tensor]): Each item are the 3D truth boxes for
each image in [x, y, z, w, l, h, theta, vx, vy] format.
each image in [x, y, z, x_size, y_size, z_size, yaw, vx, vy]
format.
gt_labels_3d (list[Tensor]): 3D class indices corresponding to
each box.
centers2d (list[Tensor]): Projected 3D centers onto 2D images.
depths (list[Tensor]): Depth of projected centers on 2D images.
attr_labels (list[Tensor], optional): Attribute indices
corresponding to each box
gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
gt_bboxes_ignore (list[Tensor]): Specify which bounding
boxes can be ignored when computing the loss.
Returns:
......
mmdet3d/models/detectors/votenet.py
View file @ 53435c62
......@@ -40,11 +40,11 @@ class VoteNet(SingleStage3DDetector):
img_metas (list): Image metas.
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): gt bboxes of each batch.
gt_labels_3d (list[torch.Tensor]): gt class labels of each batch.
pts_semantic_mask (None | list[torch.Tensor]): point-wise semantic
pts_semantic_mask (list[torch.Tensor]): point-wise semantic
label of each batch.
pts_instance_mask (None | list[torch.Tensor]): point-wise instance
pts_instance_mask (list[torch.Tensor]): point-wise instance
label of each batch.
gt_bboxes_ignore (None | list[torch.Tensor]): Specify
gt_bboxes_ignore (list[torch.Tensor]): Specify
which bounding.
Returns:
......
mmdet3d/models/fusion_layers/point_fusion.py
View file @ 53435c62
......@@ -34,7 +34,7 @@ def point_sample(img_meta,
coord_type (str): 'DEPTH' or 'CAMERA' or 'LIDAR'.
img_scale_factor (torch.Tensor): Scale factor with shape of \
(w_scale, h_scale).
img_crop_offset (torch.Tensor): Crop offset used to crop \
img_crop_offset (torch.Tensor): Crop offset used to crop
image during data augmentation with shape of (w_offset, h_offset).
img_flip (bool): Whether the image is flipped.
img_pad_shape (tuple[int]): int tuple indicates the h & w after
......
mmdet3d/models/losses/axis_aligned_iou_loss.py
View file @ 53435c62
......@@ -54,7 +54,7 @@ class AxisAlignedIoULoss(nn.Module):
Args:
pred (torch.Tensor): Bbox predictions with shape [..., 3].
target (torch.Tensor): Bbox targets (gt) with shape [..., 3].
weight (torch.Tensor|float, optional): Weight of loss. \
weight (torch.Tensor | float, optional): Weight of loss.
Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
......
mmdet3d/models/losses/chamfer_distance.py
View file @ 53435c62
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mmdet3d/models/model_utils/transformer.py
View file @ 53435c62
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