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r50_nuimg_704x256.py
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r50_nuimg_704x256.py
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dataset_type = 'CustomNuScenesDataset'
dataset_root = 'data/nuscenes/'
input_modality = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_map=False,
use_external=True
)
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
# 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]
# arch config
embed_dims = 256
num_layers = 6
num_query = 900
num_frames = 8
num_levels = 4
num_points = 4
img_backbone = dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN2d', requires_grad=True),
norm_eval=True,
style='pytorch',
with_cp=True)
img_neck = dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=embed_dims,
num_outs=num_levels)
img_norm_cfg = dict(
mean=[123.675, 116.280, 103.530],
std=[58.395, 57.120, 57.375],
to_rgb=True)
model = dict(
type='SparseBEV',
data_aug=dict(
img_color_aug=True, # Move some augmentations to GPU
img_norm_cfg=img_norm_cfg,
img_pad_cfg=dict(size_divisor=32)),
stop_prev_grad=0,
img_backbone=img_backbone,
img_neck=img_neck,
pts_bbox_head=dict(
type='SparseBEVHead',
num_classes=10,
in_channels=embed_dims,
num_query=num_query,
query_denoising=True,
query_denoising_groups=10,
code_size=10,
code_weights=[2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
sync_cls_avg_factor=True,
transformer=dict(
type='SparseBEVTransformer',
embed_dims=embed_dims,
num_frames=num_frames,
num_points=num_points,
num_layers=num_layers,
num_levels=num_levels,
num_classes=10,
code_size=10,
pc_range=point_cloud_range),
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,
score_threshold=0.05,
num_classes=10),
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=embed_dims // 2,
normalize=True,
offset=-0.5),
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)),
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),
)
))
)
ida_aug_conf = {
'resize_lim': (0.38, 0.55),
'final_dim': (256, 704),
'bot_pct_lim': (0.0, 0.0),
'rot_lim': (0.0, 0.0),
'H': 900, 'W': 1600,
'rand_flip': True,
}
train_pipeline = [
dict(type='LoadMultiViewImageFromFiles', to_float32=False, color_type='color'),
dict(type='LoadMultiViewImageFromMultiSweeps', sweeps_num=num_frames - 1),
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='RandomTransformImage', ida_aug_conf=ida_aug_conf, training=True),
dict(type='GlobalRotScaleTransImage', rot_range=[-0.3925, 0.3925], scale_ratio_range=[0.95, 1.05]),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['gt_bboxes_3d', 'gt_labels_3d', 'img'], meta_keys=(
'filename', 'ori_shape', 'img_shape', 'pad_shape', 'lidar2img', 'img_timestamp'))
]
test_pipeline = [
dict(type='LoadMultiViewImageFromFiles', to_float32=False, color_type='color'),
dict(type='LoadMultiViewImageFromMultiSweeps', sweeps_num=num_frames - 1, test_mode=True),
dict(type='RandomTransformImage', ida_aug_conf=ida_aug_conf, training=False),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1600, 900),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='DefaultFormatBundle3D', class_names=class_names, with_label=False),
dict(type='Collect3D', keys=['img'], meta_keys=(
'filename', 'box_type_3d', 'ori_shape', 'img_shape', 'pad_shape',
'lidar2img', 'img_timestamp'))
])
]
data = dict(
workers_per_gpu=8,
train=dict(
type=dataset_type,
data_root=dataset_root,
ann_file=dataset_root + 'nuscenes_infos_train_sweep.pkl',
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False,
use_valid_flag=True,
box_type_3d='LiDAR'),
val=dict(
type=dataset_type,
data_root=dataset_root,
ann_file=dataset_root + 'nuscenes_infos_val_sweep.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True,
box_type_3d='LiDAR'),
test=dict(
type=dataset_type,
data_root=dataset_root,
ann_file=dataset_root + 'nuscenes_infos_test_sweep.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True,
box_type_3d='LiDAR')
)
optimizer = dict(
type='AdamW',
lr=2e-4,
paramwise_cfg=dict(custom_keys={
'img_backbone': dict(lr_mult=0.1),
'sampling_offset': dict(lr_mult=0.1),
}),
weight_decay=0.01
)
optimizer_config = dict(
type='Fp16OptimizerHook',
loss_scale=512.0,
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
batch_size = 8
# load pretrained weights
load_from = 'pretrain/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim_20201009_124951-40963960.pth'
revise_keys = [('backbone', 'img_backbone')]
# resume the last training
resume_from = None
# checkpointing
checkpoint_config = dict(interval=1, max_keep_ckpts=1)
# logging
log_config = dict(
interval=1,
hooks=[
dict(type='MyTextLoggerHook', interval=1, reset_flag=True),
dict(type='MyTensorboardLoggerHook', interval=500, reset_flag=True)
]
)
# evaluation
eval_config = dict(interval=total_epochs)
# other flags
debug = False