daxiongpro
commented
3 years ago this is my imvotenet with kitti dataset, but it occurs an error.
_base_ = [
'../_base_/datasets/kitti-3d-3class.py',
'../_base_/schedules/schedule_3x.py', '../_base_/default_runtime.py',
'../_base_/models/imvotenet_image.py'
]
# dataset settings
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
input_modality = dict(use_lidar=True, use_camera=True)
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'kitti_dbinfos_train.pkl',
rate=1.0,
prepare=dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(Car=5, Pedestrian=5, Cyclist=5)),
classes=class_names,
sample_groups=dict(Car=20, Pedestrian=15, Cyclist=15))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
model = dict(
pts_backbone=dict(
type='PointNet2SASSG',
in_channels=4,
num_points=(2048, 1024, 512, 256),
radius=(0.2, 0.4, 0.8, 1.2),
num_samples=(64, 32, 16, 16),
sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
(128, 128, 256)),
fp_channels=((256, 256), (256, 256)),
norm_cfg=dict(type='BN2d'),
sa_cfg=dict(
type='PointSAModule',
pool_mod='max',
use_xyz=True,
normalize_xyz=True)),
pts_bbox_heads=dict(
common=dict(
type='VoteHead',
num_classes=3,
bbox_coder=dict(
type='PartialBinBasedBBoxCoder',
num_sizes=3,
num_dir_bins=12,
with_rot=True,
mean_sizes=[[2.114256, 1.620300, 0.927272],
[0.791118, 1.279516, 0.718182],
[0.923508, 1.867419, 0.845495],
]),
pred_layer_cfg=dict(
in_channels=128, shared_conv_channels=(128, 128), bias=True),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
objectness_loss=dict(
type='CrossEntropyLoss',
class_weight=[0.2, 0.8],
reduction='sum',
loss_weight=5.0),
center_loss=dict(
type='ChamferDistance',
mode='l2',
reduction='sum',
loss_src_weight=10.0,
loss_dst_weight=10.0),
dir_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
dir_res_loss=dict(
type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
size_class_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
size_res_loss=dict(
type='SmoothL1Loss', reduction='sum', loss_weight=10.0 / 3.0),
semantic_loss=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0)),
joint=dict(
vote_module_cfg=dict(
in_channels=512,
vote_per_seed=1,
gt_per_seed=3,
conv_channels=(512, 256),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
norm_feats=True,
vote_loss=dict(
type='ChamferDistance',
mode='l1',
reduction='none',
loss_dst_weight=10.0)),
vote_aggregation_cfg=dict(
type='PointSAModule',
num_point=256,
radius=0.3,
num_sample=16,
mlp_channels=[512, 128, 128, 128],
use_xyz=True,
normalize_xyz=True)),
pts=dict(
vote_module_cfg=dict(
in_channels=256,
vote_per_seed=1,
gt_per_seed=3,
conv_channels=(256, 256),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
norm_feats=True,
vote_loss=dict(
type='ChamferDistance',
mode='l1',
reduction='none',
loss_dst_weight=10.0)),
vote_aggregation_cfg=dict(
type='PointSAModule',
num_point=256,
radius=0.3,
num_sample=16,
mlp_channels=[256, 128, 128, 128],
use_xyz=True,
normalize_xyz=True)),
img=dict(
vote_module_cfg=dict(
in_channels=256,
vote_per_seed=1,
gt_per_seed=3,
conv_channels=(256, 256),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
norm_feats=True,
vote_loss=dict(
type='ChamferDistance',
mode='l1',
reduction='none',
loss_dst_weight=10.0)),
vote_aggregation_cfg=dict(
type='PointSAModule',
num_point=256,
radius=0.3,
num_sample=16,
mlp_channels=[256, 128, 128, 128],
use_xyz=True,
normalize_xyz=True)),
loss_weights=[0.4, 0.3, 0.3]),
img_mlp=dict(
in_channel=18,
conv_channels=(256, 256),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
act_cfg=dict(type='ReLU')),
fusion_layer=dict(
type='VoteFusion',
num_classes=len(class_names),
max_imvote_per_pixel=3),
num_sampled_seed=1024,
freeze_img_branch=True,
# model training and testing settings
train_cfg=dict(
pts=dict(
pos_distance_thr=0.3, neg_distance_thr=0.6, sample_mod='vote')),
test_cfg=dict(
img_rcnn=dict(score_thr=0.1),
pts=dict(
sample_mod='seed',
nms_thr=0.25,
score_thr=0.05,
per_class_proposal=True)))
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
shift_height=True,
load_dim=4,
use_dim=[0, 1, 2]),
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations3D'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 600), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.0),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.523599, 0.523599],
scale_ratio_range=[0.85, 1.15],
shift_height=True),
dict(type='IndoorPointSample', num_points=20000),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'points', 'gt_bboxes_3d',
'gt_labels_3d', 'calib'
])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
shift_height=True,
load_dim=4,
use_dim=[0, 1, 2]),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 600),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.0),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
),
dict(type='IndoorPointSample', num_points=20000),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['img', 'points', 'calib'])
]),
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
shift_height=False,
load_dim=4,
use_dim=[0, 1, 2]),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['img', 'points', 'calib'])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=train_pipeline,
modality=input_modality,
classes=class_names,
test_mode=False,
box_type_3d='LiDAR')),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True,
box_type_3d='LiDAR'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True,
box_type_3d='LiDAR'))
evaluation = dict(pipeline=eval_pipeline,
interval=12)
# may also use your own pre-trained image branch
# load_from = 'https://download.openmmlab.com/mmdetection3d/v0.1.0_models/imvotenet/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class_20210323_173222-cad62aeb.pth' # noqathe error is:
2021-05-12 12:41:43,646 - mmdet - INFO - Start running, host: caizhengyi@csgpu1, work_dir:
/mnt/2TB1/caizhengyi/code/mmdetection3d/work_dirs/imvotenet_stage2_16x2_kitti-3d-3class
2021-05-12 12:41:43,646 - mmdet - INFO - workflow: [('train', 1)], max: 36 epochs
Traceback (most recent call last):
File "/home/caizhengyi/2TB1/code/mmdetection3d/tools/train.py", line 212, in <module>
main()
File "/home/caizhengyi/2TB1/code/mmdetection3d/tools/train.py", line 208, in main
meta=meta)
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/apis/train.py", line 170, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 125, in run
epoch_runner(data_loaders[i], **kwargs)
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 47, in train
for i, data_batch in enumerate(self.data_loader):
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 517, in __next__
data = self._next_data()
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1199, in _next_data
return self._process_data(data)
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1225, in _process_data
data.reraise()
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/_utils.py", line 429, in reraise
raise self.exc_type(msg)
KeyError: Caught KeyError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop
data = fetcher.fetch(index)
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/datasets/dataset_wrappers.py", line 151, in __getitem__
return self.dataset[idx % self._ori_len]
File "/home/caizhengyi/2TB1/code/mmdetection3d/mmdet3d/datasets/custom_3d.py", line 378, in __getitem__
data = self.prepare_train_data(idx)
File "/home/caizhengyi/2TB1/code/mmdetection3d/mmdet3d/datasets/custom_3d.py", line 153, in prepare_train_data
example = self.pipeline(input_dict)
File "/usr/miniconda3/envs/open-mmlab/lib/python3.7/site-packages/mmdet/datasets/pipelines/compose.py", line 40, in __call__
data = t(data)
File "/home/caizhengyi/2TB1/code/mmdetection3d/mmdet3d/datasets/pipelines/formating.py", line 166, in __call__
data[key] = results[key]
KeyError: 'calib'how can i solve it?
I want to ask how to write config file for train
kittidataset onimvotenet. I find the default dataset is sun-rgbd dataset on imvotenet