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Commit 522cc20d authored by VVsssssk's avatar VVsssssk Committed by ChaimZhu
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[Refactor]Refactor ShapeAwareHead and FreeAnchor3DHead

parent 3c57cc41
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Showing with 377 additions and 182 deletions
configs/_base_/models/hv_pointpillars_fpn_nus.py
View file @ 522cc20d
......@@ -32,7 +32,7 @@ model = dict(
layer_strides=[2, 2, 2],
out_channels=[64, 128, 256]),
pts_neck=dict(
type='FPN',
type='mmdet.FPN',
norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
act_cfg=dict(type='ReLU'),
in_channels=[64, 128, 256],
......
configs/_base_/schedules/cyclic_20e.py
View file @ 522cc20d
......@@ -47,6 +47,6 @@ param_scheduler = [
]
# runtime settings
train_cfg = dict(by_epoch=True, max_epochs=20, val_interval=1)
train_cfg = dict(by_epoch=True, max_epochs=20, val_interval=20)
val_cfg = dict()
test_cfg = dict()
configs/_base_/schedules/schedule_2x.py
View file @ 522cc20d
......@@ -8,7 +8,7 @@ optim_wrapper = dict(
clip_grad=dict(max_norm=35, norm_type=2))
# training schedule for 2x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=1)
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=24)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
......
configs/free_anchor/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py
View file @ 522cc20d
......@@ -34,14 +34,16 @@ model = dict(
dir_offset=-0.7854, # -pi / 4
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=9),
loss_cls=dict(
type='FocalLoss',
type='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.8),
loss_bbox=dict(
type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.8),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
type='mmdet.CrossEntropyLoss', use_sigmoid=False,
loss_weight=0.2)),
# model training and testing settings
train_cfg=dict(
pts=dict(code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.25, 0.25])))
configs/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py
View file @ 522cc20d
......@@ -60,11 +60,25 @@ train_pipeline = [
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
data = dict(train=dict(pipeline=train_pipeline))
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
lr_config = dict(step=[28, 34])
runner = dict(max_epochs=36)
evaluation = dict(interval=36)
train_cfg = dict(max_epochs=36, val_interval=36)
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0 / 1000,
by_epoch=False,
begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=24,
by_epoch=True,
milestones=[28, 34],
gamma=0.1)
]
configs/free_anchor/hv_pointpillars_regnet-3.2gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py
View file @ 522cc20d
......@@ -60,11 +60,25 @@ train_pipeline = [
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
data = dict(train=dict(pipeline=train_pipeline))
lr_config = dict(step=[28, 34])
runner = dict(max_epochs=36)
evaluation = dict(interval=36)
train_cfg = dict(max_epochs=36, val_interval=36)
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0 / 1000,
by_epoch=False,
begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=36,
by_epoch=True,
milestones=[28, 34],
gamma=0.1)
]
configs/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d.py
View file @ 522cc20d
......@@ -41,9 +41,3 @@ model = dict(
],
rotations=[0, 1.57],
reshape_out=True)))
# For Lyft dataset, we usually evaluate the model at the end of training.
# Since the models are trained by 24 epochs by default, we set evaluation
# interval to be 24. Please change the interval accordingly if you do not
# use a default schedule.
train_cfg = dict(val_interval=24)
configs/ssn/hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d.py
View file @ 522cc20d
......@@ -29,8 +29,9 @@ train_pipeline = [
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
......@@ -48,20 +49,18 @@ test_pipeline = [
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
type='PointsRangeFilter', point_cloud_range=point_cloud_range)
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=4,
train=dict(pipeline=train_pipeline, classes=class_names),
val=dict(pipeline=test_pipeline, classes=class_names),
test=dict(pipeline=test_pipeline, classes=class_names))
train_dataloader = dict(
batch_size=2,
num_workers=4,
dataset=dict(pipeline=train_pipeline, metainfo=dict(CLASSES=class_names)))
test_dataloader = dict(
dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
val_dataloader = dict(
dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
# model settings
model = dict(
......@@ -148,84 +147,86 @@ model = dict(
dir_limit_offset=0,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=9),
loss_cls=dict(
type='FocalLoss',
type='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
loss_bbox=dict(
type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
type='mmdet.CrossEntropyLoss', use_sigmoid=False,
loss_weight=0.2)),
# model training and testing settings
train_cfg=dict(
_delete_=True,
pts=dict(
assigner=[
dict( # bicycle
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.35,
min_pos_iou=0.35,
ignore_iof_thr=-1),
dict( # motorcycle
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
dict( # pedestrian
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
dict( # traffic cone
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
dict( # barrier
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.55,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
dict( # car
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
min_pos_iou=0.45,
ignore_iof_thr=-1),
dict( # truck
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.55,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
dict( # trailer
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.35,
min_pos_iou=0.35,
ignore_iof_thr=-1),
dict( # bus
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.55,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
dict( # construction vehicle
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.35,
......
mmdet3d/models/dense_heads/base_3d_dense_head.py
View file @ 522cc20d
......@@ -264,8 +264,8 @@ class Base3DDenseHead(BaseModule, metaclass=ABCMeta):
cfg: ConfigDict,
rescale: bool = False,
**kwargs) -> InstanceData:
"""Transform a single image's features extracted from the head into
bbox results.
"""Transform a single points sample's features extracted from the head
into bbox results.
Args:
cls_score_list (list[Tensor]): Box scores from all scale
......
mmdet3d/models/dense_heads/free_anchor3d_head.py
View file @ 522cc20d
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List
import torch
from mmcv.runner import force_fp32
from torch import Tensor
from torch.nn import functional as F
from mmdet3d.core.bbox import bbox_overlaps_nearest_3d
from mmdet3d.core.utils import InstanceList, OptInstanceList
from mmdet3d.registry import MODELS
from .anchor3d_head import Anchor3DHead
from .train_mixins import get_direction_target
......@@ -29,27 +32,26 @@ class FreeAnchor3DHead(Anchor3DHead):
""" # noqa: E501
def __init__(self,
pre_anchor_topk=50,
bbox_thr=0.6,
gamma=2.0,
alpha=0.5,
init_cfg=None,
**kwargs):
pre_anchor_topk: int = 50,
bbox_thr: float = 0.6,
gamma: float = 2.0,
alpha: float = 0.5,
init_cfg: dict = None,
**kwargs) -> None:
super().__init__(init_cfg=init_cfg, **kwargs)
self.pre_anchor_topk = pre_anchor_topk
self.bbox_thr = bbox_thr
self.gamma = gamma
self.alpha = alpha
@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):
def loss_by_feat(
self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
dir_cls_preds: List[Tensor],
batch_gt_instances_3d: InstanceList,
batch_input_metas: List[dict],
batch_gt_instances_ignore: OptInstanceList = None) -> Dict:
"""Calculate loss of FreeAnchor head.
Args:
......@@ -59,11 +61,14 @@ class FreeAnchor3DHead(Anchor3DHead):
different samples
dir_cls_preds (list[torch.Tensor]): Direction predictions of
different samples
gt_bboxes (list[:obj:`BaseInstance3DBoxes`]): Ground truth boxes.
gt_labels (list[torch.Tensor]): Ground truth labels.
input_metas (list[dict]): List of input meta information.
gt_bboxes_ignore (list[:obj:`BaseInstance3DBoxes`], optional):
Ground truth boxes that should be ignored. Defaults to None.
batch_gt_instances_3d (list[:obj:`InstanceData`]): Batch of
gt_instances. It usually includes ``bboxes_3d`` and
``labels_3d`` attributes.
batch_input_metas (list[dict]): Contain pcd and img's meta info.
batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
Batch of gt_instances_ignore. It includes ``bboxes`` attribute
data that is ignored during training and testing.
Defaults to None.
Returns:
dict[str, torch.Tensor]: Loss items.
......@@ -72,10 +77,10 @@ class FreeAnchor3DHead(Anchor3DHead):
- negative_bag_loss (torch.Tensor): Loss of negative samples.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.anchor_generator.num_levels
assert len(featmap_sizes) == self.prior_generator.num_levels
anchor_list = self.get_anchors(featmap_sizes, input_metas)
anchors = [torch.cat(anchor) for anchor in anchor_list]
anchor_list = self.get_anchors(featmap_sizes, batch_input_metas)
mlvl_anchors = [torch.cat(anchor) for anchor in anchor_list]
# concatenate each level
cls_scores = [
......@@ -98,24 +103,24 @@ class FreeAnchor3DHead(Anchor3DHead):
bbox_preds = torch.cat(bbox_preds, dim=1)
dir_cls_preds = torch.cat(dir_cls_preds, dim=1)
cls_prob = torch.sigmoid(cls_scores)
cls_probs = torch.sigmoid(cls_scores)
box_prob = []
num_pos = 0
positive_losses = []
for _, (anchors_, gt_labels_, gt_bboxes_, cls_prob_, bbox_preds_,
dir_cls_preds_) in enumerate(
zip(anchors, gt_labels, gt_bboxes, cls_prob, bbox_preds,
dir_cls_preds)):
gt_bboxes_ = gt_bboxes_.tensor.to(anchors_.device)
for _, (anchors, gt_instance_3d, cls_prob, bbox_pred,
dir_cls_pred) in enumerate(
zip(mlvl_anchors, batch_gt_instances_3d, cls_probs,
bbox_preds, dir_cls_preds)):
gt_bboxes = gt_instance_3d.bboxes_3d.tensor.to(anchors.device)
gt_labels = gt_instance_3d.labels_3d.to(anchors.device)
with torch.no_grad():
# box_localization: a_{j}^{loc}, shape: [j, 4]
pred_boxes = self.bbox_coder.decode(anchors_, bbox_preds_)
pred_boxes = self.bbox_coder.decode(anchors, bbox_pred)
# object_box_iou: IoU_{ij}^{loc}, shape: [i, j]
object_box_iou = bbox_overlaps_nearest_3d(
gt_bboxes_, pred_boxes)
gt_bboxes, pred_boxes)
# object_box_prob: P{a_{j} -> b_{i}}, shape: [i, j]
t1 = self.bbox_thr
......@@ -125,9 +130,9 @@ class FreeAnchor3DHead(Anchor3DHead):
min=0, max=1)
# object_cls_box_prob: P{a_{j} -> b_{i}}, shape: [i, c, j]
num_obj = gt_labels_.size(0)
num_obj = gt_labels.size(0)
indices = torch.stack(
[torch.arange(num_obj).type_as(gt_labels_), gt_labels_],
[torch.arange(num_obj).type_as(gt_labels), gt_labels],
dim=0)
object_cls_box_prob = torch.sparse_coo_tensor(
......@@ -147,11 +152,11 @@ class FreeAnchor3DHead(Anchor3DHead):
indices = torch.nonzero(box_cls_prob, as_tuple=False).t_()
if indices.numel() == 0:
image_box_prob = torch.zeros(
anchors_.size(0),
anchors.size(0),
self.num_classes).type_as(object_box_prob)
else:
nonzero_box_prob = torch.where(
(gt_labels_.unsqueeze(dim=-1) == indices[0]),
(gt_labels.unsqueeze(dim=-1) == indices[0]),
object_box_prob[:, indices[1]],
torch.tensor(
[0]).type_as(object_box_prob)).max(dim=0).values
......@@ -160,14 +165,13 @@ class FreeAnchor3DHead(Anchor3DHead):
image_box_prob = torch.sparse_coo_tensor(
indices.flip([0]),
nonzero_box_prob,
size=(anchors_.size(0), self.num_classes)).to_dense()
size=(anchors.size(0), self.num_classes)).to_dense()
# end
box_prob.append(image_box_prob)
# construct bags for objects
match_quality_matrix = bbox_overlaps_nearest_3d(
gt_bboxes_, anchors_)
match_quality_matrix = bbox_overlaps_nearest_3d(gt_bboxes, anchors)
_, matched = torch.topk(
match_quality_matrix,
self.pre_anchor_topk,
......@@ -177,15 +181,15 @@ class FreeAnchor3DHead(Anchor3DHead):
# matched_cls_prob: P_{ij}^{cls}
matched_cls_prob = torch.gather(
cls_prob_[matched], 2,
gt_labels_.view(-1, 1, 1).repeat(1, self.pre_anchor_topk,
1)).squeeze(2)
cls_prob[matched], 2,
gt_labels.view(-1, 1, 1).repeat(1, self.pre_anchor_topk,
1)).squeeze(2)
# matched_box_prob: P_{ij}^{loc}
matched_anchors = anchors_[matched]
matched_anchors = anchors[matched]
matched_object_targets = self.bbox_coder.encode(
matched_anchors,
gt_bboxes_.unsqueeze(dim=1).expand_as(matched_anchors))
gt_bboxes.unsqueeze(dim=1).expand_as(matched_anchors))
# direction classification loss
loss_dir = None
......@@ -198,15 +202,16 @@ class FreeAnchor3DHead(Anchor3DHead):
self.dir_limit_offset,
one_hot=False)
loss_dir = self.loss_dir(
dir_cls_preds_[matched].transpose(-2, -1),
dir_cls_pred[matched].transpose(-2, -1),
matched_dir_targets,
reduction_override='none')
# generate bbox weights
if self.diff_rad_by_sin:
bbox_preds_[matched], matched_object_targets = \
bbox_preds_clone = bbox_pred.clone()
bbox_preds_clone[matched], matched_object_targets = \
self.add_sin_difference(
bbox_preds_[matched], matched_object_targets)
bbox_preds_clone[matched], matched_object_targets)
bbox_weights = matched_anchors.new_ones(matched_anchors.size())
# Use pop is not right, check performance
code_weight = self.train_cfg.get('code_weight', None)
......@@ -214,7 +219,7 @@ class FreeAnchor3DHead(Anchor3DHead):
bbox_weights = bbox_weights * bbox_weights.new_tensor(
code_weight)
loss_bbox = self.loss_bbox(
bbox_preds_[matched],
bbox_preds_clone[matched],
matched_object_targets,
bbox_weights,
reduction_override='none').sum(-1)
......@@ -224,7 +229,7 @@ class FreeAnchor3DHead(Anchor3DHead):
matched_box_prob = torch.exp(-loss_bbox)
# positive_losses: {-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )}
num_pos += len(gt_bboxes_)
num_pos += len(gt_bboxes)
positive_losses.append(
self.positive_bag_loss(matched_cls_prob, matched_box_prob))
......@@ -244,7 +249,8 @@ class FreeAnchor3DHead(Anchor3DHead):
}
return losses
def positive_bag_loss(self, matched_cls_prob, matched_box_prob):
def positive_bag_loss(self, matched_cls_prob: Tensor,
matched_box_prob: Tensor) -> Tensor:
"""Generate positive bag loss.
Args:
......@@ -266,7 +272,7 @@ class FreeAnchor3DHead(Anchor3DHead):
return self.alpha * F.binary_cross_entropy(
bag_prob, torch.ones_like(bag_prob), reduction='none')
def negative_bag_loss(self, cls_prob, box_prob):
def negative_bag_loss(self, cls_prob: Tensor, box_prob: Tensor) -> Tensor:
"""Generate negative bag loss.
Args:
......
mmdet3d/models/dense_heads/shape_aware_head.py
View file @ 522cc20d
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Dict, List, Optional, Tuple
import numpy as np
import torch
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from mmengine.data import InstanceData
from mmengine.model import BaseModule
from torch import Tensor
from torch import nn as nn
from mmdet3d.core import box3d_multiclass_nms, limit_period, xywhr2xyxyr
from mmdet3d.core.utils import InstanceList, OptInstanceList
from mmdet3d.registry import MODELS
from mmdet.core import multi_apply
from ..builder import build_head
......@@ -33,29 +37,30 @@ class BaseShapeHead(BaseModule):
in_channels (int): Input channels for convolutional layers.
shared_conv_channels (tuple, optional): Channels for shared
convolutional layers. Default: (64, 64).
shared_conv_strides (tuple, optional): Strides for shared
shared_conv_strides (tuple): Strides for shared
convolutional layers. Default: (1, 1).
use_direction_classifier (bool, optional): Whether to use direction
use_direction_classifier (bool): Whether to use direction
classifier. Default: True.
conv_cfg (dict, optional): Config of conv layer.
conv_cfg (dict): Config of conv layer.
Default: dict(type='Conv2d')
norm_cfg (dict, optional): Config of norm layer.
norm_cfg (dict): Config of norm layer.
Default: dict(type='BN2d').
bias (bool | str, optional): Type of bias. Default: False.
bias (bool | str): Type of bias. Default: False.
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(self,
num_cls,
num_base_anchors,
box_code_size,
in_channels,
shared_conv_channels=(64, 64),
shared_conv_strides=(1, 1),
use_direction_classifier=True,
conv_cfg=dict(type='Conv2d'),
norm_cfg=dict(type='BN2d'),
bias=False,
init_cfg=None):
num_cls: int,
num_base_anchors: int,
box_code_size: int,
in_channels: int,
shared_conv_channels: Tuple = (64, 64),
shared_conv_strides: Tuple = (1, 1),
use_direction_classifier: bool = True,
conv_cfg: Dict = dict(type='Conv2d'),
norm_cfg: Dict = dict(type='BN2d'),
bias: bool = False,
init_cfg: Optional[dict] = None) -> None:
super().__init__(init_cfg=init_cfg)
self.num_cls = num_cls
self.num_base_anchors = num_base_anchors
......@@ -122,7 +127,7 @@ class BaseShapeHead(BaseModule):
bias_prob=0.01)
])
def forward(self, x):
def forward(self, x: Tensor) -> Dict:
"""Forward function for SmallHead.
Args:
......@@ -171,13 +176,16 @@ 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): Whether to do assignment for each
class. Default: True.
kwargs (dict): Other arguments are the same as those in
:class:`Anchor3DHead`.
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(self, tasks, assign_per_class=True, init_cfg=None, **kwargs):
def __init__(self,
tasks: Dict,
assign_per_class: bool = True,
init_cfg: Optional[dict] = None,
**kwargs) -> Dict:
self.tasks = tasks
self.featmap_sizes = []
super().__init__(
......@@ -198,10 +206,10 @@ class ShapeAwareHead(Anchor3DHead):
self.heads = nn.ModuleList()
cls_ptr = 0
for task in self.tasks:
sizes = self.anchor_generator.sizes[cls_ptr:cls_ptr +
task['num_class']]
sizes = self.prior_generator.sizes[cls_ptr:cls_ptr +
task['num_class']]
num_size = torch.tensor(sizes).reshape(-1, 3).size(0)
num_rot = len(self.anchor_generator.rotations)
num_rot = len(self.prior_generator.rotations)
num_base_anchors = num_rot * num_size
branch = dict(
type='BaseShapeHead',
......@@ -214,7 +222,7 @@ class ShapeAwareHead(Anchor3DHead):
self.heads.append(build_head(branch))
cls_ptr += task['num_class']
def forward_single(self, x):
def forward_single(self, x: Tensor) -> Tuple[Tensor]:
"""Forward function on a single-scale feature map.
Args:
......@@ -241,15 +249,18 @@ class ShapeAwareHead(Anchor3DHead):
for i, task in enumerate(self.tasks):
for _ in range(task['num_class']):
self.featmap_sizes.append(results[i]['featmap_size'])
assert len(self.featmap_sizes) == len(self.anchor_generator.ranges), \
assert len(self.featmap_sizes) == len(self.prior_generator.ranges), \
'Length of feature map sizes must be equal to length of ' + \
'different ranges of anchor generator.'
return cls_score, bbox_pred, dir_cls_preds
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):
def loss_single(self, cls_score: Tensor, bbox_pred: Tensor,
dir_cls_preds: Tensor, labels: Tensor,
label_weights: Tensor, bbox_targets: Tensor,
bbox_weights: Tensor, dir_targets: Tensor,
dir_weights: Tensor,
num_total_samples: int) -> Tuple[Tensor]:
"""Calculate loss of Single-level results.
Args:
......@@ -309,27 +320,30 @@ class ShapeAwareHead(Anchor3DHead):
return loss_cls, loss_bbox, loss_dir
def loss(self,
cls_scores,
bbox_preds,
dir_cls_preds,
gt_bboxes,
gt_labels,
input_metas,
gt_bboxes_ignore=None):
"""Calculate losses.
def loss_by_feat(
self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
dir_cls_preds: List[Tensor],
batch_gt_instances_3d: InstanceList,
batch_input_metas: List[dict],
batch_gt_instances_ignore: OptInstanceList = None) -> Dict:
"""Calculate the loss based on the features extracted by the detection
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.
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.
batch_gt_instances_3d (list[:obj:`InstanceData`]): Batch of
gt_instances. It usually includes ``bboxes_3d`` and
``labels_3d`` attributes.
batch_input_metas (list[dict]): Contain pcd and sample's meta info.
batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
Batch of gt_instances_ignore. It includes ``bboxes`` attribute
data that is ignored during training and testing.
Defaults to None.
Returns:
dict[str, list[torch.Tensor]]: Classification, bbox, and
......@@ -342,13 +356,12 @@ class ShapeAwareHead(Anchor3DHead):
"""
device = cls_scores[0].device
anchor_list = self.get_anchors(
self.featmap_sizes, input_metas, device=device)
self.featmap_sizes, batch_input_metas, device=device)
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,
batch_gt_instances_3d,
batch_input_metas,
batch_gt_instances_ignore=batch_gt_instances_ignore,
num_classes=self.num_classes,
sampling=self.sampling)
......@@ -376,21 +389,22 @@ class ShapeAwareHead(Anchor3DHead):
return dict(
loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dir=losses_dir)
def get_bboxes(self,
cls_scores,
bbox_preds,
dir_cls_preds,
input_metas,
cfg=None,
rescale=False):
"""Get bboxes of anchor head.
def predict_by_feat(self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
dir_cls_preds: List[Tensor],
batch_input_metas: List[dict],
cfg: Optional[dict] = None,
rescale: List[Tensor] = False) -> List[tuple]:
"""Transform a batch of output features extracted from the head into
bbox results.
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.
batch_input_metas (list[dict]): Contain pcd and img's meta info.
cfg (:obj:`ConfigDict`, optional): Training or testing config.
Default: None.
rescale (list[torch.Tensor], optional): Whether to rescale bbox.
......@@ -404,13 +418,13 @@ class ShapeAwareHead(Anchor3DHead):
num_levels = len(cls_scores)
assert num_levels == 1, 'Only support single level inference.'
device = cls_scores[0].device
mlvl_anchors = self.anchor_generator.grid_anchors(
mlvl_anchors = self.prior_generator.grid_anchors(
self.featmap_sizes, device=device)
# `anchor` is a list of anchors for different classes
mlvl_anchors = [torch.cat(anchor, dim=0) for anchor in mlvl_anchors]
result_list = []
for img_id in range(len(input_metas)):
for img_id in range(len(batch_input_metas)):
cls_score_list = [
cls_scores[i][img_id].detach() for i in range(num_levels)
]
......@@ -421,22 +435,25 @@ class ShapeAwareHead(Anchor3DHead):
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)
input_meta = batch_input_metas[img_id]
proposals = self._predict_by_feat_single(cls_score_list,
bbox_pred_list,
dir_cls_pred_list,
mlvl_anchors, input_meta,
cfg, rescale)
result_list.append(proposals)
return result_list
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.
def _predict_by_feat_single(self,
cls_scores: Tensor,
bbox_preds: Tensor,
dir_cls_preds: Tensor,
mlvl_anchors: List[Tensor],
input_meta: List[dict],
cfg: Dict = None,
rescale: List[Tensor] = False):
"""Transform a single point's features extracted from the head into
bbox results.
Args:
cls_scores (torch.Tensor): Class score in single batch.
......@@ -447,7 +464,7 @@ class ShapeAwareHead(Anchor3DHead):
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], optional): whether to rescale bbox.
rescale (list[torch.Tensor]): whether to rescale bbox.
Default: False.
Returns:
......@@ -513,4 +530,8 @@ class ShapeAwareHead(Anchor3DHead):
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
results = InstanceData()
results.bboxes_3d = bboxes
results.scores_3d = scores
results.labels_3d = labels
return results
tests/test_models/test_dense_heads/test_freeanchors.py 0 → 100644
View file @ 522cc20d
import unittest
import torch
from mmengine import DefaultScope
from mmdet3d.core import LiDARInstance3DBoxes
from mmdet3d.registry import MODELS
from tests.utils.model_utils import (_create_detector_inputs,
_get_detector_cfg, _setup_seed)
class TestFreeAnchor(unittest.TestCase):
def test_freeanchor(self):
import mmdet3d.models
assert hasattr(mmdet3d.models.dense_heads, 'FreeAnchor3DHead')
DefaultScope.get_instance('test_freeanchor', scope_name='mmdet3d')
_setup_seed(0)
freeanchor_cfg = _get_detector_cfg(
'free_anchor/hv_pointpillars_fpn_sbn-all_free-'
'anchor_4x8_2x_nus-3d.py')
model = MODELS.build(freeanchor_cfg)
num_gt_instance = 50
data = [
_create_detector_inputs(
num_gt_instance=num_gt_instance, gt_bboxes_dim=9)
]
aug_data = [
_create_detector_inputs(
num_gt_instance=num_gt_instance, gt_bboxes_dim=9),
_create_detector_inputs(
num_gt_instance=num_gt_instance + 1, gt_bboxes_dim=9)
]
# test_aug_test
metainfo = {
'pcd_scale_factor': 1,
'pcd_horizontal_flip': 1,
'pcd_vertical_flip': 1,
'box_type_3d': LiDARInstance3DBoxes
}
for item in aug_data:
item['data_sample'].set_metainfo(metainfo)
if torch.cuda.is_available():
model = model.cuda()
# test simple_test
with torch.no_grad():
batch_inputs, data_samples = model.data_preprocessor(
data, True)
results = model.forward(
batch_inputs, data_samples, mode='predict')
self.assertEqual(len(results), len(data))
self.assertIn('bboxes_3d', results[0].pred_instances_3d)
self.assertIn('scores_3d', results[0].pred_instances_3d)
self.assertIn('labels_3d', results[0].pred_instances_3d)
batch_inputs, data_samples = model.data_preprocessor(
aug_data, True)
aug_results = model.forward(
batch_inputs, data_samples, mode='predict')
self.assertEqual(len(results), len(data))
self.assertIn('bboxes_3d', aug_results[0].pred_instances_3d)
self.assertIn('scores_3d', aug_results[0].pred_instances_3d)
self.assertIn('labels_3d', aug_results[0].pred_instances_3d)
self.assertIn('bboxes_3d', aug_results[1].pred_instances_3d)
self.assertIn('scores_3d', aug_results[1].pred_instances_3d)
self.assertIn('labels_3d', aug_results[1].pred_instances_3d)
losses = model.forward(batch_inputs, data_samples, mode='loss')
self.assertGreater(losses['positive_bag_loss'], 0)
self.assertGreater(losses['negative_bag_loss'], 0)
tests/test_models/test_dense_heads/test_ssn.py 0 → 100644
View file @ 522cc20d
import unittest
import torch
from mmengine import DefaultScope
from mmdet3d.core import LiDARInstance3DBoxes
from mmdet3d.registry import MODELS
from tests.utils.model_utils import (_create_detector_inputs,
_get_detector_cfg, _setup_seed)
class TestSSN(unittest.TestCase):
def test_ssn(self):
import mmdet3d.models
assert hasattr(mmdet3d.models.dense_heads, 'ShapeAwareHead')
DefaultScope.get_instance('test_ssn', scope_name='mmdet3d')
_setup_seed(0)
ssn_cfg = _get_detector_cfg(
'ssn/hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d.py')
model = MODELS.build(ssn_cfg)
num_gt_instance = 50
data = [
_create_detector_inputs(
num_gt_instance=num_gt_instance, gt_bboxes_dim=9)
]
aug_data = [
_create_detector_inputs(
num_gt_instance=num_gt_instance, gt_bboxes_dim=9),
_create_detector_inputs(
num_gt_instance=num_gt_instance + 1, gt_bboxes_dim=9)
]
# test_aug_test
metainfo = {
'pcd_scale_factor': 1,
'pcd_horizontal_flip': 1,
'pcd_vertical_flip': 1,
'box_type_3d': LiDARInstance3DBoxes
}
for item in aug_data:
item['data_sample'].set_metainfo(metainfo)
if torch.cuda.is_available():
model = model.cuda()
# test simple_test
with torch.no_grad():
batch_inputs, data_samples = model.data_preprocessor(
data, True)
results = model.forward(
batch_inputs, data_samples, mode='predict')
self.assertEqual(len(results), len(data))
self.assertIn('bboxes_3d', results[0].pred_instances_3d)
self.assertIn('scores_3d', results[0].pred_instances_3d)
self.assertIn('labels_3d', results[0].pred_instances_3d)
batch_inputs, data_samples = model.data_preprocessor(
aug_data, True)
aug_results = model.forward(
batch_inputs, data_samples, mode='predict')
self.assertEqual(len(results), len(data))
self.assertIn('bboxes_3d', aug_results[0].pred_instances_3d)
self.assertIn('scores_3d', aug_results[0].pred_instances_3d)
self.assertIn('labels_3d', aug_results[0].pred_instances_3d)
self.assertIn('bboxes_3d', aug_results[1].pred_instances_3d)
self.assertIn('scores_3d', aug_results[1].pred_instances_3d)
self.assertIn('labels_3d', aug_results[1].pred_instances_3d)
losses = model.forward(batch_inputs, data_samples, mode='loss')
self.assertGreater(losses['loss_cls'][0], 0)
self.assertGreater(losses['loss_bbox'][0], 0)
self.assertGreater(losses['loss_dir'][0], 0)
tests/utils/model_utils.py
View file @ 522cc20d
......@@ -76,6 +76,7 @@ def _create_detector_inputs(seed=0,
with_img=False,
num_gt_instance=20,
points_feat_dim=4,
gt_bboxes_dim=7,
num_classes=3):
_setup_seed(seed)
inputs_dict = dict()
......@@ -88,7 +89,7 @@ def _create_detector_inputs(seed=0,
gt_instance_3d = InstanceData()
gt_instance_3d.bboxes_3d = LiDARInstance3DBoxes(
torch.rand([num_gt_instance, 7]))
torch.rand([num_gt_instance, gt_bboxes_dim]), box_dim=gt_bboxes_dim)
gt_instance_3d.labels_3d = torch.randint(0, num_classes, [num_gt_instance])
data_sample = Det3DDataSample(
metainfo=dict(box_type_3d=LiDARInstance3DBoxes))
......
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