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r50_nuimg_704x256_8f.py
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r50_nuimg_704x256_8f.py
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dataset_type = 'NuSceneOcc'
dataset_root = 'data/nuscenes/'
occ_gt_root = 'data/nuscenes/occ3d'
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-40, -40, -1.0, 40, 40, 5.4]
occ_size = [200, 200, 16]
img_norm_cfg = dict(
mean=[123.675, 116.280, 103.530],
std=[58.395, 57.120, 57.375],
to_rgb=True
)
# For nuScenes we usually do 10-class detection
det_class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
occ_class_names = [
'others', 'barrier', 'bicycle', 'bus', 'car', 'construction_vehicle',
'motorcycle', 'pedestrian', 'traffic_cone', 'trailer', 'truck',
'driveable_surface', 'other_flat', 'sidewalk',
'terrain', 'manmade', 'vegetation', 'free'
]
input_modality = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_map=False,
use_external=False
)
_dim_ = 256
_num_points_ = 4
_num_groups_ = 4
_num_layers_ = 2
_num_frames_ = 8
_num_queries_ = 100
_topk_training_ = [4000, 16000, 64000]
_topk_testing_ = [2000, 8000, 32000]
model = dict(
type='SparseOcc',
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)),
use_mask_camera=False,
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=_dim_,
num_outs=4),
pts_bbox_head=dict(
type='SparseOccHead',
class_names=occ_class_names,
embed_dims=_dim_,
occ_size=occ_size,
pc_range=point_cloud_range,
transformer=dict(
type='SparseOccTransformer',
embed_dims=_dim_,
num_layers=_num_layers_,
num_frames=_num_frames_,
num_points=_num_points_,
num_groups=_num_groups_,
num_queries=_num_queries_,
num_levels=4,
num_classes=len(occ_class_names),
pc_range=point_cloud_range,
occ_size=occ_size,
topk_training=_topk_training_,
topk_testing=_topk_testing_),
loss_cfgs=dict(
loss_mask2former=dict(
type='Mask2FormerLoss',
num_classes=len(occ_class_names),
no_class_weight=0.1,
loss_cls_weight=2.0,
loss_mask_weight=5.0,
loss_dice_weight=5.0,
),
loss_geo_scal=dict(
type='GeoScalLoss',
num_classes=len(occ_class_names),
loss_weight=1.0
),
loss_sem_scal=dict(
type='SemScalLoss',
num_classes=len(occ_class_names),
loss_weight=1.0
)
),
),
)
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,
}
bda_aug_conf = dict(
rot_lim=(-22.5, 22.5),
scale_lim=(1., 1.),
flip_dx_ratio=0.5,
flip_dy_ratio=0.5
)
train_pipeline = [
dict(type='LoadMultiViewImageFromFiles', to_float32=False, color_type='color'),
dict(type='LoadMultiViewImageFromMultiSweeps', sweeps_num=_num_frames_ - 1),
dict(type='BEVAug', bda_aug_conf=bda_aug_conf, classes=det_class_names, is_train=True),
dict(type='LoadOccGTFromFile', num_classes=len(occ_class_names)),
dict(type='RandomTransformImage', ida_aug_conf=ida_aug_conf, training=True),
dict(type='DefaultFormatBundle3D', class_names=det_class_names),
dict(type='Collect3D', keys=['img', 'voxel_semantics', 'voxel_instances', 'instance_class_ids'], # other keys: 'mask_camera'
meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', 'lidar2img', 'img_timestamp', 'ego2lidar'))
]
test_pipeline = [
dict(type='LoadMultiViewImageFromFiles', to_float32=False, color_type='color'),
dict(type='LoadMultiViewImageFromMultiSweeps', sweeps_num=_num_frames_ - 1, test_mode=True),
dict(type='BEVAug', bda_aug_conf=bda_aug_conf, classes=det_class_names, is_train=False),
dict(type='LoadOccGTFromFile', num_classes=len(occ_class_names)),
dict(type='RandomTransformImage', ida_aug_conf=ida_aug_conf, training=False),
dict(type='DefaultFormatBundle3D', class_names=det_class_names),
dict(type='Collect3D', keys=['img', 'voxel_semantics', 'voxel_instances', 'instance_class_ids'],
meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', 'lidar2img', 'img_timestamp', 'ego2lidar'))
]
data = dict(
workers_per_gpu=8,
train=dict(
type=dataset_type,
data_root=dataset_root,
occ_gt_root=occ_gt_root,
ann_file=dataset_root + 'nuscenes_infos_train_sweep.pkl',
pipeline=train_pipeline,
classes=det_class_names,
modality=input_modality,
test_mode=False
),
val=dict(
type=dataset_type,
data_root=dataset_root,
occ_gt_root=occ_gt_root,
ann_file=dataset_root + 'nuscenes_infos_val_sweep.pkl',
pipeline=test_pipeline,
classes=det_class_names,
modality=input_modality,
test_mode=True
),
test=dict(
type=dataset_type,
data_root=dataset_root,
occ_gt_root=occ_gt_root,
ann_file=dataset_root + 'nuscenes_infos_test_sweep.pkl',
pipeline=test_pipeline,
classes=det_class_names,
modality=input_modality,
test_mode=True
),
)
optimizer = dict(
type='AdamW',
lr=5e-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(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
by_epoch=True,
step=[22, 24],
gamma=0.2
)
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