increase24
commented
1 year ago Here is my config of the ResNet50 version. The ResNet50 checkpoint is pretrained on ImageNet. Need it be pretrained on nuScenes with FCOS3D network?
_base_ = [
'../../../mmdetection3d/configs/_base_/datasets/nus-3d.py',
'../../../mmdetection3d/configs/_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)
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
# 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=False)
model = dict(
type='Detr3D',
use_grid_mask=True,
# 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=False),
# norm_eval=True,
# style='caffe',
# dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
# stage_with_dcn=(False, False, True, True)),
pretrained=dict(img='./ckpts/resnet50-19c8e357.pth'),
img_backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch'),
img_neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=4,
relu_before_extra_convs=True),
pts_bbox_head=dict(
type='Detr3DHead',
num_query=900,
num_classes=10,
in_channels=256,
sync_cls_avg_factor=True,
with_box_refine=True,
as_two_stage=False,
transformer=dict(
type='Detr3DTransformer',
decoder=dict(
type='Detr3DTransformerDecoder',
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=256,
num_heads=8,
dropout=0.1),
dict(
type='Detr3DCrossAtten',
pc_range=point_cloud_range,
num_points=1,
embed_dims=256)
],
feedforward_channels=512,
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='SinePositionalEncoding',
num_feats=128,
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)),
# 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 = 'CustomNuScenesDataset'
data_root = 'data/nuscenes/'
# file_client_args = dict(backend='disk')
file_client_args = dict(
backend='petrel',
path_mapping=dict({
'./data/nuscenes-all/': 's3://nus_bevf/',
'data/nuscenes-all/': 's3://nus_bevf/',
'./data/nuscenes/': 's3://nus_bevf/',
'data/nuscenes/': 's3://nus_bevf/',
}))
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'nuscenes_dbinfos_train.pkl',
rate=1.0,
prepare=dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(
car=5,
truck=5,
bus=5,
trailer=5,
construction_vehicle=5,
traffic_cone=5,
barrier=5,
motorcycle=5,
bicycle=5,
pedestrian=5)),
classes=class_names,
sample_groups=dict(
car=2,
truck=3,
construction_vehicle=7,
bus=4,
trailer=6,
barrier=2,
motorcycle=6,
bicycle=6,
pedestrian=2,
traffic_cone=2),
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=[0, 1, 2, 3, 4],
file_client_args=file_client_args))
train_pipeline = [
dict(type='LoadMultiViewImageFromFilesInCeph', to_float32=True, file_client_args=file_client_args),
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='PadMultiViewImage', size_divisor=32),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['gt_bboxes_3d', 'gt_labels_3d', 'img'])
]
test_pipeline = [
dict(type='LoadMultiViewImageFromFilesInCeph', to_float32=True, file_client_args=file_client_args),
dict(type='NormalizeMultiviewImage', **img_norm_cfg),
dict(type='PadMultiViewImage', size_divisor=32),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', 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 + 'nuscenes_infos_train.pkl',
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False,
use_valid_flag=True,
# 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',
file_client_args = file_client_args,),
val=dict(type=dataset_type, pipeline=test_pipeline, classes=class_names, modality=input_modality, file_client_args = file_client_args,),
test=dict(type=dataset_type, pipeline=test_pipeline, classes=class_names, modality=input_modality, file_client_args = file_client_args,))
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=2, pipeline=test_pipeline)
runner = dict(type='EpochBasedRunner', max_epochs=total_epochs)
# load_from='ckpts/fcos3d.pth'
I have tried to reproduce the result of replacing the backbone as ResNet50, but I cannot reach the accuracy present in the paper. The reproduced NDS and mAP are
0.3510and0.2821respectively.Could you provide some tips to train the ResNet50 version or release the correspongding config?