Source code for mmdet3d.models.dense_heads.anchor3d_head
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmcv.runner import BaseModule, force_fp32
from torch import nn as nn
from mmdet3d.core import (PseudoSampler, box3d_multiclass_nms, limit_period,
xywhr2xyxyr)
from mmdet.core import (build_assigner, build_bbox_coder,
build_prior_generator, build_sampler, multi_apply)
from ..builder import HEADS, build_loss
from .train_mixins import AnchorTrainMixin
[docs]@HEADS.register_module()
class Anchor3DHead(BaseModule, AnchorTrainMixin):
"""Anchor head for SECOND/PointPillars/MVXNet/PartA2.
Args:
num_classes (int): Number of classes.
in_channels (int): Number of channels in the input feature map.
train_cfg (dict): Train configs.
test_cfg (dict): Test configs.
feat_channels (int): Number of channels of the feature map.
use_direction_classifier (bool): Whether to add a direction classifier.
anchor_generator(dict): Config dict of anchor generator.
assigner_per_size (bool): Whether to do assignment for each separate
anchor size.
assign_per_class (bool): Whether to do assignment for each class.
diff_rad_by_sin (bool): Whether to change the difference into sin
difference for box regression loss.
dir_offset (float | int): The offset of BEV rotation angles.
(TODO: may be moved into box coder)
dir_limit_offset (float | int): The limited range of BEV
rotation angles. (TODO: may be moved into box coder)
bbox_coder (dict): Config dict of box coders.
loss_cls (dict): Config of classification loss.
loss_bbox (dict): Config of localization loss.
loss_dir (dict): Config of direction classifier loss.
"""
def __init__(self,
num_classes,
in_channels,
train_cfg,
test_cfg,
feat_channels=256,
use_direction_classifier=True,
anchor_generator=dict(
type='Anchor3DRangeGenerator',
range=[0, -39.68, -1.78, 69.12, 39.68, -1.78],
strides=[2],
sizes=[[3.9, 1.6, 1.56]],
rotations=[0, 1.57],
custom_values=[],
reshape_out=False),
assigner_per_size=False,
assign_per_class=False,
diff_rad_by_sin=True,
dir_offset=-np.pi / 2,
dir_limit_offset=0,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
loss_dir=dict(type='CrossEntropyLoss', loss_weight=0.2),
init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.num_classes = num_classes
self.feat_channels = feat_channels
self.diff_rad_by_sin = diff_rad_by_sin
self.use_direction_classifier = use_direction_classifier
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.assigner_per_size = assigner_per_size
self.assign_per_class = assign_per_class
self.dir_offset = dir_offset
self.dir_limit_offset = dir_limit_offset
import warnings
warnings.warn(
'dir_offset and dir_limit_offset will be depressed and be '
'incorporated into box coder in the future')
self.fp16_enabled = False
# build anchor generator
self.anchor_generator = build_prior_generator(anchor_generator)
# In 3D detection, the anchor stride is connected with anchor size
self.num_anchors = self.anchor_generator.num_base_anchors
# build box coder
self.bbox_coder = build_bbox_coder(bbox_coder)
self.box_code_size = self.bbox_coder.code_size
# build loss function
self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
self.sampling = loss_cls['type'] not in ['FocalLoss', 'GHMC']
if not self.use_sigmoid_cls:
self.num_classes += 1
self.loss_cls = build_loss(loss_cls)
self.loss_bbox = build_loss(loss_bbox)
self.loss_dir = build_loss(loss_dir)
self.fp16_enabled = False
self._init_layers()
self._init_assigner_sampler()
if init_cfg is None:
self.init_cfg = dict(
type='Normal',
layer='Conv2d',
std=0.01,
override=dict(
type='Normal', name='conv_cls', std=0.01, bias_prob=0.01))
def _init_assigner_sampler(self):
"""Initialize the target assigner and sampler of the head."""
if self.train_cfg is None:
return
if self.sampling:
self.bbox_sampler = build_sampler(self.train_cfg.sampler)
else:
self.bbox_sampler = PseudoSampler()
if isinstance(self.train_cfg.assigner, dict):
self.bbox_assigner = build_assigner(self.train_cfg.assigner)
elif isinstance(self.train_cfg.assigner, list):
self.bbox_assigner = [
build_assigner(res) for res in self.train_cfg.assigner
]
def _init_layers(self):
"""Initialize neural network layers of the head."""
self.cls_out_channels = self.num_anchors * self.num_classes
self.conv_cls = nn.Conv2d(self.feat_channels, self.cls_out_channels, 1)
self.conv_reg = nn.Conv2d(self.feat_channels,
self.num_anchors * self.box_code_size, 1)
if self.use_direction_classifier:
self.conv_dir_cls = nn.Conv2d(self.feat_channels,
self.num_anchors * 2, 1)
[docs] def forward_single(self, x):
"""Forward function on a single-scale feature map.
Args:
x (torch.Tensor): Input features.
Returns:
tuple[torch.Tensor]: Contain score of each class, bbox
regression and direction classification predictions.
"""
cls_score = self.conv_cls(x)
bbox_pred = self.conv_reg(x)
dir_cls_preds = None
if self.use_direction_classifier:
dir_cls_preds = self.conv_dir_cls(x)
return cls_score, bbox_pred, dir_cls_preds
[docs] def forward(self, feats):
"""Forward pass.
Args:
feats (list[torch.Tensor]): Multi-level features, e.g.,
features produced by FPN.
Returns:
tuple[list[torch.Tensor]]: Multi-level class score, bbox
and direction predictions.
"""
return multi_apply(self.forward_single, feats)
[docs] def get_anchors(self, featmap_sizes, input_metas, device='cuda'):
"""Get anchors according to feature map sizes.
Args:
featmap_sizes (list[tuple]): Multi-level feature map sizes.
input_metas (list[dict]): contain pcd and img's meta info.
device (str): device of current module.
Returns:
list[list[torch.Tensor]]: Anchors of each image, valid flags
of each image.
"""
num_imgs = len(input_metas)
# since feature map sizes of all images are the same, we only compute
# anchors for one time
multi_level_anchors = self.anchor_generator.grid_anchors(
featmap_sizes, device=device)
anchor_list = [multi_level_anchors for _ in range(num_imgs)]
return anchor_list
[docs] def loss_single(self, cls_score, bbox_pred, dir_cls_preds, labels,
label_weights, bbox_targets, bbox_weights, dir_targets,
dir_weights, num_total_samples):
"""Calculate loss of Single-level results.
Args:
cls_score (torch.Tensor): Class score in single-level.
bbox_pred (torch.Tensor): Bbox prediction in single-level.
dir_cls_preds (torch.Tensor): Predictions of direction class
in single-level.
labels (torch.Tensor): Labels of class.
label_weights (torch.Tensor): Weights of class loss.
bbox_targets (torch.Tensor): Targets of bbox predictions.
bbox_weights (torch.Tensor): Weights of bbox loss.
dir_targets (torch.Tensor): Targets of direction predictions.
dir_weights (torch.Tensor): Weights of direction loss.
num_total_samples (int): The number of valid samples.
Returns:
tuple[torch.Tensor]: Losses of class, bbox
and direction, respectively.
"""
# classification loss
if num_total_samples is None:
num_total_samples = int(cls_score.shape[0])
labels = labels.reshape(-1)
label_weights = label_weights.reshape(-1)
cls_score = cls_score.permute(0, 2, 3, 1).reshape(-1, self.num_classes)
assert labels.max().item() <= self.num_classes
loss_cls = self.loss_cls(
cls_score, labels, label_weights, avg_factor=num_total_samples)
# regression loss
bbox_pred = bbox_pred.permute(0, 2, 3,
1).reshape(-1, self.box_code_size)
bbox_targets = bbox_targets.reshape(-1, self.box_code_size)
bbox_weights = bbox_weights.reshape(-1, self.box_code_size)
bg_class_ind = self.num_classes
pos_inds = ((labels >= 0)
& (labels < bg_class_ind)).nonzero(
as_tuple=False).reshape(-1)
num_pos = len(pos_inds)
pos_bbox_pred = bbox_pred[pos_inds]
pos_bbox_targets = bbox_targets[pos_inds]
pos_bbox_weights = bbox_weights[pos_inds]
# dir loss
if self.use_direction_classifier:
dir_cls_preds = dir_cls_preds.permute(0, 2, 3, 1).reshape(-1, 2)
dir_targets = dir_targets.reshape(-1)
dir_weights = dir_weights.reshape(-1)
pos_dir_cls_preds = dir_cls_preds[pos_inds]
pos_dir_targets = dir_targets[pos_inds]
pos_dir_weights = dir_weights[pos_inds]
if num_pos > 0:
code_weight = self.train_cfg.get('code_weight', None)
if code_weight:
pos_bbox_weights = pos_bbox_weights * bbox_weights.new_tensor(
code_weight)
if self.diff_rad_by_sin:
pos_bbox_pred, pos_bbox_targets = self.add_sin_difference(
pos_bbox_pred, pos_bbox_targets)
loss_bbox = self.loss_bbox(
pos_bbox_pred,
pos_bbox_targets,
pos_bbox_weights,
avg_factor=num_total_samples)
# direction classification loss
loss_dir = None
if self.use_direction_classifier:
loss_dir = self.loss_dir(
pos_dir_cls_preds,
pos_dir_targets,
pos_dir_weights,
avg_factor=num_total_samples)
else:
loss_bbox = pos_bbox_pred.sum()
if self.use_direction_classifier:
loss_dir = pos_dir_cls_preds.sum()
return loss_cls, loss_bbox, loss_dir
[docs] @staticmethod
def add_sin_difference(boxes1, boxes2):
"""Convert the rotation difference to difference in sine function.
Args:
boxes1 (torch.Tensor): Original Boxes in shape (NxC), where C>=7
and the 7th dimension is rotation dimension.
boxes2 (torch.Tensor): Target boxes in shape (NxC), where C>=7 and
the 7th dimension is rotation dimension.
Returns:
tuple[torch.Tensor]: ``boxes1`` and ``boxes2`` whose 7th
dimensions are changed.
"""
rad_pred_encoding = torch.sin(boxes1[..., 6:7]) * torch.cos(
boxes2[..., 6:7])
rad_tg_encoding = torch.cos(boxes1[..., 6:7]) * torch.sin(boxes2[...,
6:7])
boxes1 = torch.cat(
[boxes1[..., :6], rad_pred_encoding, boxes1[..., 7:]], dim=-1)
boxes2 = torch.cat([boxes2[..., :6], rad_tg_encoding, boxes2[..., 7:]],
dim=-1)
return boxes1, boxes2
[docs] @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'dir_cls_preds'))
def loss(self,
cls_scores,
bbox_preds,
dir_cls_preds,
gt_bboxes,
gt_labels,
input_metas,
gt_bboxes_ignore=None):
"""Calculate losses.
Args:
cls_scores (list[torch.Tensor]): Multi-level class scores.
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`]): Gt bboxes
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 (list[torch.Tensor]): Specify
which bounding boxes to ignore.
Returns:
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
losses.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.anchor_generator.num_levels
device = cls_scores[0].device
anchor_list = self.get_anchors(
featmap_sizes, input_metas, device=device)
label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
cls_reg_targets = self.anchor_target_3d(
anchor_list,
gt_bboxes,
input_metas,
gt_bboxes_ignore_list=gt_bboxes_ignore,
gt_labels_list=gt_labels,
num_classes=self.num_classes,
label_channels=label_channels,
sampling=self.sampling)
if cls_reg_targets is None:
return None
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
dir_targets_list, dir_weights_list, num_total_pos,
num_total_neg) = cls_reg_targets
num_total_samples = (
num_total_pos + num_total_neg if self.sampling else num_total_pos)
# num_total_samples = None
losses_cls, losses_bbox, losses_dir = multi_apply(
self.loss_single,
cls_scores,
bbox_preds,
dir_cls_preds,
labels_list,
label_weights_list,
bbox_targets_list,
bbox_weights_list,
dir_targets_list,
dir_weights_list,
num_total_samples=num_total_samples)
return dict(
loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dir=losses_dir)
[docs] def get_bboxes(self,
cls_scores,
bbox_preds,
dir_cls_preds,
input_metas,
cfg=None,
rescale=False):
"""Get bboxes of anchor head.
Args:
cls_scores (list[torch.Tensor]): Multi-level class scores.
bbox_preds (list[torch.Tensor]): Multi-level bbox predictions.
dir_cls_preds (list[torch.Tensor]): Multi-level direction
class predictions.
input_metas (list[dict]): Contain pcd and img's meta info.
cfg (:obj:`ConfigDict`): Training or testing config.
rescale (list[torch.Tensor]): Whether th rescale bbox.
Returns:
list[tuple]: Prediction resultes of batches.
"""
assert len(cls_scores) == len(bbox_preds)
assert len(cls_scores) == len(dir_cls_preds)
num_levels = len(cls_scores)
featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
device = cls_scores[0].device
mlvl_anchors = self.anchor_generator.grid_anchors(
featmap_sizes, device=device)
mlvl_anchors = [
anchor.reshape(-1, self.box_code_size) for anchor in mlvl_anchors
]
result_list = []
for img_id in range(len(input_metas)):
cls_score_list = [
cls_scores[i][img_id].detach() for i in range(num_levels)
]
bbox_pred_list = [
bbox_preds[i][img_id].detach() for i in range(num_levels)
]
dir_cls_pred_list = [
dir_cls_preds[i][img_id].detach() for i in range(num_levels)
]
input_meta = input_metas[img_id]
proposals = self.get_bboxes_single(cls_score_list, bbox_pred_list,
dir_cls_pred_list, mlvl_anchors,
input_meta, cfg, rescale)
result_list.append(proposals)
return result_list
[docs] def get_bboxes_single(self,
cls_scores,
bbox_preds,
dir_cls_preds,
mlvl_anchors,
input_meta,
cfg=None,
rescale=False):
"""Get bboxes of single branch.
Args:
cls_scores (torch.Tensor): Class score in single batch.
bbox_preds (torch.Tensor): Bbox prediction in single batch.
dir_cls_preds (torch.Tensor): Predictions of direction class
in single batch.
mlvl_anchors (List[torch.Tensor]): Multi-level anchors
in single batch.
input_meta (list[dict]): Contain pcd and img's meta info.
cfg (:obj:`ConfigDict`): Training or testing config.
rescale (list[torch.Tensor]): whether th rescale bbox.
Returns:
tuple: Contain predictions of single batch.
- bboxes (:obj:`BaseInstance3DBoxes`): Predicted 3d bboxes.
- scores (torch.Tensor): Class score of each bbox.
- labels (torch.Tensor): Label of each bbox.
"""
cfg = self.test_cfg if cfg is None else cfg
assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
mlvl_bboxes = []
mlvl_scores = []
mlvl_dir_scores = []
for cls_score, bbox_pred, dir_cls_pred, anchors in zip(
cls_scores, bbox_preds, dir_cls_preds, mlvl_anchors):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
assert cls_score.size()[-2:] == dir_cls_pred.size()[-2:]
dir_cls_pred = dir_cls_pred.permute(1, 2, 0).reshape(-1, 2)
dir_cls_score = torch.max(dir_cls_pred, dim=-1)[1]
cls_score = cls_score.permute(1, 2,
0).reshape(-1, self.num_classes)
if self.use_sigmoid_cls:
scores = cls_score.sigmoid()
else:
scores = cls_score.softmax(-1)
bbox_pred = bbox_pred.permute(1, 2,
0).reshape(-1, self.box_code_size)
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
if self.use_sigmoid_cls:
max_scores, _ = scores.max(dim=1)
else:
max_scores, _ = scores[:, :-1].max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
anchors = anchors[topk_inds, :]
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
dir_cls_score = dir_cls_score[topk_inds]
bboxes = self.bbox_coder.decode(anchors, bbox_pred)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_dir_scores.append(dir_cls_score)
mlvl_bboxes = torch.cat(mlvl_bboxes)
mlvl_bboxes_for_nms = xywhr2xyxyr(input_meta['box_type_3d'](
mlvl_bboxes, box_dim=self.box_code_size).bev)
mlvl_scores = torch.cat(mlvl_scores)
mlvl_dir_scores = torch.cat(mlvl_dir_scores)
if self.use_sigmoid_cls:
# Add a dummy background class to the front when using sigmoid
padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
score_thr = cfg.get('score_thr', 0)
results = box3d_multiclass_nms(mlvl_bboxes, mlvl_bboxes_for_nms,
mlvl_scores, score_thr, cfg.max_num,
cfg, mlvl_dir_scores)
bboxes, scores, labels, dir_scores = results
if bboxes.shape[0] > 0:
dir_rot = limit_period(bboxes[..., 6] - self.dir_offset,
self.dir_limit_offset, np.pi)
bboxes[..., 6] = (
dir_rot + self.dir_offset +
np.pi * dir_scores.to(bboxes.dtype))
bboxes = input_meta['box_type_3d'](bboxes, box_dim=self.box_code_size)
return bboxes, scores, labels