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new file: projects/configs/Actformer/small_5_cam_weight.py
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	new file:   projects/configs/Actformer/tiny_5_cam.py
	modified:   tools/analysis_tools/visual.py
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@hsz0403
hsz0403 committed Mar 31, 2024
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271 changes: 271 additions & 0 deletions projects/configs/Actformer/small_5_cam_weight.py
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# BEvFormer-small consumes at lease 10500M GPU memory
# compared to bevformer_base, bevformer_small has
# smaller BEV: 200*200 -> 150*150
# less encoder layers: 6 -> 3
# smaller input size: 1600*900 -> (1600*900)*0.8
# multi-scale feautres -> single scale features (C5)
# with_cp of backbone = True

_base_ = [
'../datasets/custom_nus-3d.py',
'../_base_/default_runtime.py'
]
#
plugin = True
plugin_dir = 'projects/mmdet3d_plugin/'

# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
voxel_size = [0.2, 0.2, 8]


img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]

input_modality = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_map=False,
use_external=True)

_dim_ = 256
_pos_dim_ = _dim_//2
_ffn_dim_ = _dim_*2
_num_levels_ = 1
bev_h_ = 150
bev_w_ = 150
queue_length = 3 # each sequence contains `queue_length` frames.

model = dict(
type='BEVFormer',
use_grid_mask=True,
video_test_mode=True,
padding=True,
num_cams=30,
img_backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(3,),
frozen_stages=1,
norm_cfg=dict(type='BN2d', requires_grad=False),
norm_eval=True,
style='caffe',
with_cp=True, # using checkpoint to save GPU memory
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), # original DCNv2 will print log when perform load_state_dict
stage_with_dcn=(False, False, True, True)),
img_neck=dict(
type='FPN',
in_channels=[2048],
out_channels=_dim_,
start_level=0,
add_extra_convs='on_output',
num_outs=_num_levels_,
relu_before_extra_convs=True),
pts_bbox_head=dict(
type='BEVFormerHead',
bev_h=bev_h_,
bev_w=bev_w_,
num_query=900,
num_classes=10,
in_channels=_dim_,
sync_cls_avg_factor=True,
with_box_refine=True,
as_two_stage=False,
transformer=dict(
type='PerceptionTransformer',
rotate_prev_bev=True,
use_shift=True,
use_can_bus=True,
embed_dims=_dim_,
encoder=dict(
type='BEVFormerEncoder',
num_layers=3,
pc_range=point_cloud_range,
num_points_in_pillar=4,
return_intermediate=False,
transformerlayers=dict(
type='BEVFormerLayer',
attn_cfgs=[
dict(
type='TemporalSelfAttention',
embed_dims=_dim_,
num_levels=1),
dict(
type='PoseSelectiveAttention',
pc_range=point_cloud_range,
use_weight=True,
deformable_attention=dict(
type='MSDeformableAttention3D',
embed_dims=_dim_,
num_points=8,
num_levels=_num_levels_),
embed_dims=_dim_,
)
],
feedforward_channels=_ffn_dim_,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm'))),
decoder=dict(
type='DetectionTransformerDecoder',
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=_dim_,
num_heads=8,
dropout=0.1),
dict(
type='CustomMSDeformableAttention',
embed_dims=_dim_,
num_levels=1),
],

feedforward_channels=_ffn_dim_,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm')))),
bbox_coder=dict(
type='NMSFreeCoder',
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
pc_range=point_cloud_range,
max_num=300,
voxel_size=voxel_size,
num_classes=10),
positional_encoding=dict(
type='LearnedPositionalEncoding',
num_feats=_pos_dim_,
row_num_embed=bev_h_,
col_num_embed=bev_w_,
),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=2.0),
loss_bbox=dict(type='L1Loss', loss_weight=0.25),
loss_iou=dict(type='GIoULoss', loss_weight=0.0)),
# model training and testing settings
train_cfg=dict(pts=dict(
grid_size=[512, 512, 1],
voxel_size=voxel_size,
point_cloud_range=point_cloud_range,
out_size_factor=4,
assigner=dict(
type='HungarianAssigner3D',
cls_cost=dict(type='FocalLossCost', weight=2.0),
reg_cost=dict(type='BBox3DL1Cost', weight=0.25),
iou_cost=dict(type='IoUCost', weight=0.0), # Fake cost. This is just to make it compatible with DETR head.
pc_range=point_cloud_range))))

dataset_type = 'CustomV2XSIMDataset'
data_root = 'data/V2X-Sim-2.0/'
file_client_args = dict(backend='disk')


train_pipeline = [
dict(type='LoadMultiViewImageFromFiles', to_float32=True),
dict(type='PhotoMetricDistortionMultiViewImage'),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=False),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='NormalizeMultiviewImage', **img_norm_cfg),
dict(type='RandomScaleImageMultiViewImage', scales=[0.8]),
dict(type='PadMultiViewImage', size_divisor=32),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='CustomCollect3D', keys=['gt_bboxes_3d', 'gt_labels_3d', 'img'])
]

test_pipeline = [
dict(type='LoadMultiViewImageFromFiles', to_float32=True),
dict(type='NormalizeMultiviewImage', **img_norm_cfg),
# dict(type='PadMultiViewImage', size_divisor=32),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1600, 900),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='RandomScaleImageMultiViewImage', scales=[0.8]),
dict(type='PadMultiViewImage', size_divisor=32),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='CustomCollect3D', keys=['img'])
])
]

data = dict(
samples_per_gpu=1,
workers_per_gpu=4,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'v2x_sim_infos_train_5.pkl',
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False,
use_valid_flag=True,
bev_size=(bev_h_, bev_w_),
queue_length=queue_length,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR'),
val=dict(type=dataset_type,
data_root=data_root,
ann_file=data_root + 'v2x_sim_infos_val_5.pkl',
pipeline=test_pipeline, bev_size=(bev_h_, bev_w_),
classes=class_names, modality=input_modality, samples_per_gpu=1),
test=dict(type=dataset_type,
data_root=data_root,
ann_file=data_root + 'v2x_sim_infos_val_5.pkl',
pipeline=test_pipeline, bev_size=(bev_h_, bev_w_),
classes=class_names, modality=input_modality),
shuffler_sampler=dict(type='DistributedGroupSampler'),
nonshuffler_sampler=dict(type='DistributedSampler')
)

optimizer = dict(
type='AdamW',
lr=2e-4,
paramwise_cfg=dict(
custom_keys={
'img_backbone': dict(lr_mult=0.1),
}),
weight_decay=0.01)

optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
min_lr_ratio=1e-3)
total_epochs = 24
evaluation = dict(interval=1, pipeline=test_pipeline)

runner = dict(type='EpochBasedRunner', max_epochs=total_epochs)
load_from = 'ckpts/r101_dcn_fcos3d_pretrain.pth'
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])

checkpoint_config = dict(interval=1)
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