abadithela
commented
2 months ago Running the following command from command line also doesn't give the right answer:
python tools/test.py projects/BEVFusion/configs/bevfusion_lidar-cam_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py checkpoints/bevfusion_converted.pth --cfg-options "test_evaluator.pklfile_prefix=/home/apurvabadithela/nuscenes_dataset/inference_results/bevfusion_model/results.pkl" --task 'multi-modality_det'Instead, I get the following error:
04/22 12:07:56 - mmengine - INFO -
------------------------------------------------------------
System environment:
sys.platform: linux
Python: 3.8.16 (default, Jan 17 2023, 23:13:24) [GCC 11.2.0]
CUDA available: True
numpy_random_seed: 2089837194
GPU 0,1: NVIDIA RTX A6000
CUDA_HOME: /home/apurvabadithela/miniconda3/envs/detection
NVCC: Cuda compilation tools, release 11.7, V11.7.99
GCC: gcc (Ubuntu 10.5.0-1ubuntu1~22.04) 10.5.0
PyTorch: 1.13.1
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.7
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
- CuDNN 8.5
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.14.1
OpenCV: 4.7.0
MMEngine: 0.9.1
Runtime environment:
cudnn_benchmark: False
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 2089837194
Distributed launcher: none
Distributed training: False
GPU number: 1
------------------------------------------------------------
04/22 12:07:56 - mmengine - INFO - Config:
auto_scale_lr = dict(base_batch_size=32, enable=False)
backend_args = None
class_names = [
'car',
'truck',
'construction_vehicle',
'bus',
'trailer',
'barrier',
'motorcycle',
'bicycle',
'pedestrian',
'traffic_cone',
]
custom_imports = dict(
allow_failed_imports=False, imports=[
'projects.BEVFusion.bevfusion',
])
data_prefix = dict(
CAM_BACK='samples/CAM_BACK',
CAM_BACK_LEFT='samples/CAM_BACK_LEFT',
CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
CAM_FRONT='samples/CAM_FRONT',
CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT',
CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT',
pts='samples/LIDAR_TOP',
sweeps='sweeps/LIDAR_TOP')
data_root = 'data/nuscenes/'
dataset_type = 'NuScenesDataset'
db_sampler = dict(
classes=[
'car',
'truck',
'construction_vehicle',
'bus',
'trailer',
'barrier',
'motorcycle',
'bicycle',
'pedestrian',
'traffic_cone',
],
data_root='data/nuscenes/',
info_path='data/nuscenes/nuscenes_dbinfos_train.pkl',
points_loader=dict(
backend_args=None,
coord_type='LIDAR',
load_dim=5,
type='LoadPointsFromFile',
use_dim=[
0,
1,
2,
3,
4,
]),
prepare=dict(
filter_by_difficulty=[
-1,
],
filter_by_min_points=dict(
barrier=5,
bicycle=5,
bus=5,
car=5,
construction_vehicle=5,
motorcycle=5,
pedestrian=5,
traffic_cone=5,
trailer=5,
truck=5)),
rate=1.0,
sample_groups=dict(
barrier=2,
bicycle=6,
bus=4,
car=2,
construction_vehicle=7,
motorcycle=6,
pedestrian=2,
traffic_cone=2,
trailer=6,
truck=3))
default_hooks = dict(
checkpoint=dict(interval=1, type='CheckpointHook'),
logger=dict(interval=50, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(type='Det3DVisualizationHook'))
default_scope = 'mmdet3d'
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
input_modality = dict(use_camera=True, use_lidar=True)
launcher = 'none'
load_from = 'checkpoints/bevfusion_converted.pth'
log_level = 'INFO'
log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
lr = 0.0001
metainfo = dict(classes=[
'car',
'truck',
'construction_vehicle',
'bus',
'trailer',
'barrier',
'motorcycle',
'bicycle',
'pedestrian',
'traffic_cone',
])
model = dict(
bbox_head=dict(
auxiliary=True,
bbox_coder=dict(
code_size=10,
out_size_factor=8,
pc_range=[
-54.0,
-54.0,
],
post_center_range=[
-61.2,
-61.2,
-10.0,
61.2,
61.2,
10.0,
],
score_threshold=0.0,
type='TransFusionBBoxCoder',
voxel_size=[
0.075,
0.075,
]),
bn_momentum=0.1,
common_heads=dict(
center=[
2,
2,
],
dim=[
3,
2,
],
height=[
1,
2,
],
rot=[
2,
2,
],
vel=[
2,
2,
]),
decoder_layer=dict(
cross_attn_cfg=dict(dropout=0.1, embed_dims=128, num_heads=8),
ffn_cfg=dict(
act_cfg=dict(inplace=True, type='ReLU'),
embed_dims=128,
feedforward_channels=256,
ffn_drop=0.1,
num_fcs=2),
norm_cfg=dict(type='LN'),
pos_encoding_cfg=dict(input_channel=2, num_pos_feats=128),
self_attn_cfg=dict(dropout=0.1, embed_dims=128, num_heads=8),
type='TransformerDecoderLayer'),
hidden_channel=128,
in_channels=512,
loss_bbox=dict(
loss_weight=0.25, reduction='mean', type='mmdet.L1Loss'),
loss_cls=dict(
alpha=0.25,
gamma=2.0,
loss_weight=1.0,
reduction='mean',
type='mmdet.FocalLoss',
use_sigmoid=True),
loss_heatmap=dict(
loss_weight=1.0, reduction='mean', type='mmdet.GaussianFocalLoss'),
nms_kernel_size=3,
num_classes=10,
num_decoder_layers=1,
num_proposals=200,
test_cfg=dict(
dataset='nuScenes',
grid_size=[
1440,
1440,
41,
],
nms_type=None,
out_size_factor=8,
pc_range=[
-54.0,
-54.0,
],
voxel_size=[
0.075,
0.075,
]),
train_cfg=dict(
assigner=dict(
cls_cost=dict(
alpha=0.25,
gamma=2.0,
type='mmdet.FocalLossCost',
weight=0.15),
iou_calculator=dict(coordinate='lidar', type='BboxOverlaps3D'),
iou_cost=dict(type='IoU3DCost', weight=0.25),
reg_cost=dict(type='BBoxBEVL1Cost', weight=0.25),
type='HungarianAssigner3D'),
code_weights=[
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.2,
0.2,
],
dataset='nuScenes',
gaussian_overlap=0.1,
grid_size=[
1440,
1440,
41,
],
min_radius=2,
out_size_factor=8,
point_cloud_range=[
-54.0,
-54.0,
-5.0,
54.0,
54.0,
3.0,
],
pos_weight=-1,
voxel_size=[
0.075,
0.075,
0.2,
]),
type='TransFusionHead'),
data_preprocessor=dict(
bgr_to_rgb=False,
mean=[
123.675,
116.28,
103.53,
],
pad_size_divisor=32,
std=[
58.395,
57.12,
57.375,
],
type='Det3DDataPreprocessor',
voxelize_cfg=dict(
max_num_points=10,
max_voxels=[
120000,
160000,
],
point_cloud_range=[
-54.0,
-54.0,
-5.0,
54.0,
54.0,
3.0,
],
voxel_size=[
0.075,
0.075,
0.2,
],
voxelize_reduce=True)),
fusion_layer=dict(
in_channels=[
80,
256,
], out_channels=256, type='ConvFuser'),
img_backbone=dict(
attn_drop_rate=0.0,
convert_weights=True,
depths=[
2,
2,
6,
2,
],
drop_path_rate=0.2,
drop_rate=0.0,
embed_dims=96,
init_cfg=dict(
checkpoint=
'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth',
type='Pretrained'),
mlp_ratio=4,
num_heads=[
3,
6,
12,
24,
],
out_indices=[
1,
2,
3,
],
patch_norm=True,
qk_scale=None,
qkv_bias=True,
type='mmdet.SwinTransformer',
window_size=7,
with_cp=False),
img_neck=dict(
act_cfg=dict(inplace=True, type='ReLU'),
in_channels=[
192,
384,
768,
],
norm_cfg=dict(requires_grad=True, type='BN2d'),
num_outs=3,
out_channels=256,
start_level=0,
type='GeneralizedLSSFPN',
upsample_cfg=dict(align_corners=False, mode='bilinear')),
pts_backbone=dict(
conv_cfg=dict(bias=False, type='Conv2d'),
in_channels=256,
layer_nums=[
5,
5,
],
layer_strides=[
1,
2,
],
norm_cfg=dict(eps=0.001, momentum=0.01, type='BN'),
out_channels=[
128,
256,
],
type='SECOND'),
pts_middle_encoder=dict(
block_type='basicblock',
encoder_channels=(
(
16,
16,
32,
),
(
32,
32,
64,
),
(
64,
64,
128,
),
(
128,
128,
),
),
encoder_paddings=(
(
0,
0,
1,
),
(
0,
0,
1,
),
(
0,
0,
(
1,
1,
0,
),
),
(
0,
0,
),
),
in_channels=5,
norm_cfg=dict(eps=0.001, momentum=0.01, type='BN1d'),
order=(
'conv',
'norm',
'act',
),
sparse_shape=[
1440,
1440,
41,
],
type='BEVFusionSparseEncoder'),
pts_neck=dict(
in_channels=[
128,
256,
],
norm_cfg=dict(eps=0.001, momentum=0.01, type='BN'),
out_channels=[
256,
256,
],
type='SECONDFPN',
upsample_cfg=dict(bias=False, type='deconv'),
upsample_strides=[
1,
2,
],
use_conv_for_no_stride=True),
pts_voxel_encoder=dict(num_features=5, type='HardSimpleVFE'),
type='BEVFusion',
view_transform=dict(
dbound=[
1.0,
60.0,
0.5,
],
downsample=2,
feature_size=[
32,
88,
],
image_size=[
256,
704,
],
in_channels=256,
out_channels=80,
type='DepthLSSTransform',
xbound=[
-54.0,
54.0,
0.3,
],
ybound=[
-54.0,
54.0,
0.3,
],
zbound=[
-10.0,
10.0,
20.0,
]))
optim_wrapper = dict(
clip_grad=dict(max_norm=35, norm_type=2),
optimizer=dict(lr=0.0002, type='AdamW', weight_decay=0.01),
type='OptimWrapper')
param_scheduler = [
dict(
begin=0,
by_epoch=False,
end=500,
start_factor=0.33333333,
type='LinearLR'),
dict(
T_max=6,
begin=0,
by_epoch=True,
convert_to_iter_based=True,
end=6,
eta_min_ratio=0.0001,
type='CosineAnnealingLR'),
dict(
begin=0,
by_epoch=True,
convert_to_iter_based=True,
end=2.4,
eta_min=0.8947368421052632,
type='CosineAnnealingMomentum'),
dict(
begin=2.4,
by_epoch=True,
convert_to_iter_based=True,
end=6,
eta_min=1,
type='CosineAnnealingMomentum'),
]
point_cloud_range = [
-54.0,
-54.0,
-5.0,
54.0,
54.0,
3.0,
]
resume = False
test_cfg = dict()
test_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='nuscenes_infos_val.pkl',
backend_args=None,
box_type_3d='LiDAR',
data_prefix=dict(
CAM_BACK='samples/CAM_BACK',
CAM_BACK_LEFT='samples/CAM_BACK_LEFT',
CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
CAM_FRONT='samples/CAM_FRONT',
CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT',
CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT',
pts='samples/LIDAR_TOP',
sweeps='sweeps/LIDAR_TOP'),
data_root='data/nuscenes/',
metainfo=dict(classes=[
'car',
'truck',
'construction_vehicle',
'bus',
'trailer',
'barrier',
'motorcycle',
'bicycle',
'pedestrian',
'traffic_cone',
]),
modality=dict(use_camera=True, use_lidar=True),
pipeline=[
dict(
backend_args=None,
color_type='color',
to_float32=True,
type='BEVLoadMultiViewImageFromFiles'),
dict(
backend_args=None,
coord_type='LIDAR',
load_dim=5,
type='LoadPointsFromFile',
use_dim=5),
dict(
backend_args=None,
load_dim=5,
pad_empty_sweeps=True,
remove_close=True,
sweeps_num=9,
type='LoadPointsFromMultiSweeps',
use_dim=5),
dict(
bot_pct_lim=[
0.0,
0.0,
],
final_dim=[
256,
704,
],
is_train=False,
rand_flip=False,
resize_lim=[
0.48,
0.48,
],
rot_lim=[
0.0,
0.0,
],
type='ImageAug3D'),
dict(
point_cloud_range=[
-54.0,
-54.0,
-5.0,
54.0,
54.0,
3.0,
],
type='PointsRangeFilter'),
dict(
keys=[
'img',
'points',
'gt_bboxes_3d',
'gt_labels_3d',
],
meta_keys=[
'cam2img',
'ori_cam2img',
'lidar2cam',
'lidar2img',
'cam2lidar',
'ori_lidar2img',
'img_aug_matrix',
'box_type_3d',
'sample_idx',
'lidar_path',
'img_path',
'num_pts_feats',
],
type='Pack3DDetInputs'),
],
test_mode=True,
type='NuScenesDataset'),
drop_last=False,
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
ann_file='data/nuscenes/nuscenes_infos_val.pkl',
backend_args=None,
data_root='data/nuscenes/',
metric='bbox',
pklfile_prefix=
'/home/apurvabadithela/nuscenes_dataset/inference_results/bevfusion_model/results.pkl',
type='NuScenesMetric')
test_pipeline = [
dict(
backend_args=None,
color_type='color',
to_float32=True,
type='BEVLoadMultiViewImageFromFiles'),
dict(
backend_args=None,
coord_type='LIDAR',
load_dim=5,
type='LoadPointsFromFile',
use_dim=5),
dict(
backend_args=None,
load_dim=5,
pad_empty_sweeps=True,
remove_close=True,
sweeps_num=9,
type='LoadPointsFromMultiSweeps',
use_dim=5),
dict(
bot_pct_lim=[
0.0,
0.0,
],
final_dim=[
256,
704,
],
is_train=False,
rand_flip=False,
resize_lim=[
0.48,
0.48,
],
rot_lim=[
0.0,
0.0,
],
type='ImageAug3D'),
dict(
point_cloud_range=[
-54.0,
-54.0,
-5.0,
54.0,
54.0,
3.0,
],
type='PointsRangeFilter'),
dict(
keys=[
'img',
'points',
'gt_bboxes_3d',
'gt_labels_3d',
],
meta_keys=[
'cam2img',
'ori_cam2img',
'lidar2cam',
'lidar2img',
'cam2lidar',
'ori_lidar2img',
'img_aug_matrix',
'box_type_3d',
'sample_idx',
'lidar_path',
'img_path',
'num_pts_feats',
],
type='Pack3DDetInputs'),
]
train_cfg = dict(by_epoch=True, max_epochs=6, val_interval=1)
train_dataloader = dict(
batch_size=4,
dataset=dict(
dataset=dict(
ann_file='nuscenes_infos_train.pkl',
box_type_3d='LiDAR',
data_prefix=dict(
CAM_BACK='samples/CAM_BACK',
CAM_BACK_LEFT='samples/CAM_BACK_LEFT',
CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
CAM_FRONT='samples/CAM_FRONT',
CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT',
CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT',
pts='samples/LIDAR_TOP',
sweeps='sweeps/LIDAR_TOP'),
data_root='data/nuscenes/',
metainfo=dict(classes=[
'car',
'truck',
'construction_vehicle',
'bus',
'trailer',
'barrier',
'motorcycle',
'bicycle',
'pedestrian',
'traffic_cone',
]),
modality=dict(use_camera=True, use_lidar=True),
pipeline=[
dict(
backend_args=None,
color_type='color',
to_float32=True,
type='BEVLoadMultiViewImageFromFiles'),
dict(
backend_args=None,
coord_type='LIDAR',
load_dim=5,
type='LoadPointsFromFile',
use_dim=5),
dict(
backend_args=None,
load_dim=5,
pad_empty_sweeps=True,
remove_close=True,
sweeps_num=9,
type='LoadPointsFromMultiSweeps',
use_dim=5),
dict(
type='LoadAnnotations3D',
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True),
dict(
bot_pct_lim=[
0.0,
0.0,
],
final_dim=[
256,
704,
],
is_train=True,
rand_flip=True,
resize_lim=[
0.38,
0.55,
],
rot_lim=[
-5.4,
5.4,
],
type='ImageAug3D'),
dict(
rot_range=[
-0.78539816,
0.78539816,
],
scale_ratio_range=[
0.9,
1.1,
],
translation_std=0.5,
type='BEVFusionGlobalRotScaleTrans'),
dict(type='BEVFusionRandomFlip3D'),
dict(
point_cloud_range=[
-54.0,
-54.0,
-5.0,
54.0,
54.0,
3.0,
],
type='PointsRangeFilter'),
dict(
point_cloud_range=[
-54.0,
-54.0,
-5.0,
54.0,
54.0,
3.0,
],
type='ObjectRangeFilter'),
dict(
classes=[
'car',
'truck',
'construction_vehicle',
'bus',
'trailer',
'barrier',
'motorcycle',
'bicycle',
'pedestrian',
'traffic_cone',
],
type='ObjectNameFilter'),
dict(
fixed_prob=True,
max_epoch=6,
mode=1,
offset=False,
prob=0.0,
ratio=0.5,
rotate=1,
type='GridMask',
use_h=True,
use_w=True),
dict(type='PointShuffle'),
dict(
keys=[
'points',
'img',
'gt_bboxes_3d',
'gt_labels_3d',
'gt_bboxes',
'gt_labels',
],
meta_keys=[
'cam2img',
'ori_cam2img',
'lidar2cam',
'lidar2img',
'cam2lidar',
'ori_lidar2img',
'img_aug_matrix',
'box_type_3d',
'sample_idx',
'lidar_path',
'img_path',
'transformation_3d_flow',
'pcd_rotation',
'pcd_scale_factor',
'pcd_trans',
'img_aug_matrix',
'lidar_aug_matrix',
'num_pts_feats',
],
type='Pack3DDetInputs'),
],
test_mode=False,
type='NuScenesDataset',
use_valid_flag=True),
type='CBGSDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
dict(
backend_args=None,
color_type='color',
to_float32=True,
type='BEVLoadMultiViewImageFromFiles'),
dict(
backend_args=None,
coord_type='LIDAR',
load_dim=5,
type='LoadPointsFromFile',
use_dim=5),
dict(
backend_args=None,
load_dim=5,
pad_empty_sweeps=True,
remove_close=True,
sweeps_num=9,
type='LoadPointsFromMultiSweeps',
use_dim=5),
dict(
type='LoadAnnotations3D',
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True),
dict(
bot_pct_lim=[
0.0,
0.0,
],
final_dim=[
256,
704,
],
is_train=True,
rand_flip=True,
resize_lim=[
0.38,
0.55,
],
rot_lim=[
-5.4,
5.4,
],
type='ImageAug3D'),
dict(
rot_range=[
-0.78539816,
0.78539816,
],
scale_ratio_range=[
0.9,
1.1,
],
translation_std=0.5,
type='BEVFusionGlobalRotScaleTrans'),
dict(type='BEVFusionRandomFlip3D'),
dict(
point_cloud_range=[
-54.0,
-54.0,
-5.0,
54.0,
54.0,
3.0,
],
type='PointsRangeFilter'),
dict(
point_cloud_range=[
-54.0,
-54.0,
-5.0,
54.0,
54.0,
3.0,
],
type='ObjectRangeFilter'),
dict(
classes=[
'car',
'truck',
'construction_vehicle',
'bus',
'trailer',
'barrier',
'motorcycle',
'bicycle',
'pedestrian',
'traffic_cone',
],
type='ObjectNameFilter'),
dict(
fixed_prob=True,
max_epoch=6,
mode=1,
offset=False,
prob=0.0,
ratio=0.5,
rotate=1,
type='GridMask',
use_h=True,
use_w=True),
dict(type='PointShuffle'),
dict(
keys=[
'points',
'img',
'gt_bboxes_3d',
'gt_labels_3d',
'gt_bboxes',
'gt_labels',
],
meta_keys=[
'cam2img',
'ori_cam2img',
'lidar2cam',
'lidar2img',
'cam2lidar',
'ori_lidar2img',
'img_aug_matrix',
'box_type_3d',
'sample_idx',
'lidar_path',
'img_path',
'transformation_3d_flow',
'pcd_rotation',
'pcd_scale_factor',
'pcd_trans',
'img_aug_matrix',
'lidar_aug_matrix',
'num_pts_feats',
],
type='Pack3DDetInputs'),
]
val_cfg = dict()
val_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='nuscenes_infos_val.pkl',
backend_args=None,
box_type_3d='LiDAR',
data_prefix=dict(
CAM_BACK='samples/CAM_BACK',
CAM_BACK_LEFT='samples/CAM_BACK_LEFT',
CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
CAM_FRONT='samples/CAM_FRONT',
CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT',
CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT',
pts='samples/LIDAR_TOP',
sweeps='sweeps/LIDAR_TOP'),
data_root='data/nuscenes/',
metainfo=dict(classes=[
'car',
'truck',
'construction_vehicle',
'bus',
'trailer',
'barrier',
'motorcycle',
'bicycle',
'pedestrian',
'traffic_cone',
]),
modality=dict(use_camera=True, use_lidar=True),
pipeline=[
dict(
backend_args=None,
color_type='color',
to_float32=True,
type='BEVLoadMultiViewImageFromFiles'),
dict(
backend_args=None,
coord_type='LIDAR',
load_dim=5,
type='LoadPointsFromFile',
use_dim=5),
dict(
backend_args=None,
load_dim=5,
pad_empty_sweeps=True,
remove_close=True,
sweeps_num=9,
type='LoadPointsFromMultiSweeps',
use_dim=5),
dict(
bot_pct_lim=[
0.0,
0.0,
],
final_dim=[
256,
704,
],
is_train=False,
rand_flip=False,
resize_lim=[
0.48,
0.48,
],
rot_lim=[
0.0,
0.0,
],
type='ImageAug3D'),
dict(
point_cloud_range=[
-54.0,
-54.0,
-5.0,
54.0,
54.0,
3.0,
],
type='PointsRangeFilter'),
dict(
keys=[
'img',
'points',
'gt_bboxes_3d',
'gt_labels_3d',
],
meta_keys=[
'cam2img',
'ori_cam2img',
'lidar2cam',
'lidar2img',
'cam2lidar',
'ori_lidar2img',
'img_aug_matrix',
'box_type_3d',
'sample_idx',
'lidar_path',
'img_path',
'num_pts_feats',
],
type='Pack3DDetInputs'),
],
test_mode=True,
type='NuScenesDataset'),
drop_last=False,
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
ann_file='data/nuscenes/nuscenes_infos_val.pkl',
backend_args=None,
data_root='data/nuscenes/',
metric='bbox',
type='NuScenesMetric')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='Det3DLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
voxel_size = [
0.075,
0.075,
0.2,
]
work_dir = './work_dirs/bevfusion_lidar-cam_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d'
04/22 12:07:58 - mmengine - INFO - Loads checkpoint by http backend from path: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
04/22 12:08:02 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
04/22 12:08:02 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) RuntimeInfoHook
(BELOW_NORMAL) LoggerHook
--------------------
before_train:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook
--------------------
before_train_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook
--------------------
before_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
--------------------
after_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_train_epoch:
(NORMAL ) IterTimerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
before_val:
(VERY_HIGH ) RuntimeInfoHook
--------------------
before_val_epoch:
(NORMAL ) IterTimerHook
--------------------
before_val_iter:
(NORMAL ) IterTimerHook
--------------------
after_val_iter:
(NORMAL ) IterTimerHook
(NORMAL ) Det3DVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_val_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_val:
(VERY_HIGH ) RuntimeInfoHook
--------------------
after_train:
(VERY_HIGH ) RuntimeInfoHook
(VERY_LOW ) CheckpointHook
--------------------
before_test:
(VERY_HIGH ) RuntimeInfoHook
--------------------
before_test_epoch:
(NORMAL ) IterTimerHook
--------------------
before_test_iter:
(NORMAL ) IterTimerHook
--------------------
after_test_iter:
(NORMAL ) IterTimerHook
(NORMAL ) Det3DVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test:
(VERY_HIGH ) RuntimeInfoHook
--------------------
after_run:
(BELOW_NORMAL) LoggerHook
--------------------
04/22 12:08:10 - mmengine - INFO - ------------------------------
04/22 12:08:10 - mmengine - INFO - The length of test dataset: 6019
04/22 12:08:10 - mmengine - INFO - The number of instances per category in the dataset:
+----------------------+--------+
| category | number |
+----------------------+--------+
| car | 80004 |
| truck | 15704 |
| construction_vehicle | 2678 |
| bus | 3158 |
| trailer | 4159 |
| barrier | 26992 |
| motorcycle | 2508 |
| bicycle | 2381 |
| pedestrian | 34347 |
| traffic_cone | 15597 |
+----------------------+--------+
/home/apurvabadithela/miniconda3/envs/detection/lib/python3.8/site-packages/mmdet/models/task_modules/builder.py:17: UserWarning: ``build_sampler`` would be deprecated soon, please use ``mmdet.registry.TASK_UTILS.build()``
warnings.warn('``build_sampler`` would be deprecated soon, please use '
/home/apurvabadithela/miniconda3/envs/detection/lib/python3.8/site-packages/mmdet/models/task_modules/builder.py:39: UserWarning: ``build_assigner`` would be deprecated soon, please use ``mmdet.registry.TASK_UTILS.build()``
warnings.warn('``build_assigner`` would be deprecated soon, please use '
/home/apurvabadithela/miniconda3/envs/detection/lib/python3.8/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1670525541702/work/aten/src/ATen/native/TensorShape.cpp:3190.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Traceback (most recent call last):
File "tools/test.py", line 149, in <module>
main()
File "tools/test.py", line 145, in main
runner.test()
File "/home/apurvabadithela/miniconda3/envs/detection/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1816, in test
self._test_loop = self.build_test_loop(self._test_loop) # type: ignore
File "/home/apurvabadithela/miniconda3/envs/detection/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1611, in build_test_loop
loop = TestLoop(
File "/home/apurvabadithela/miniconda3/envs/detection/lib/python3.8/site-packages/mmengine/runner/loops.py", line 413, in __init__
self.evaluator = runner.build_evaluator(evaluator) # type: ignore
File "/home/apurvabadithela/miniconda3/envs/detection/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1318, in build_evaluator
return Evaluator(evaluator) # type: ignore
File "/home/apurvabadithela/miniconda3/envs/detection/lib/python3.8/site-packages/mmengine/evaluator/evaluator.py", line 25, in __init__
self.metrics.append(METRICS.build(metric))
File "/home/apurvabadithela/miniconda3/envs/detection/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build
return self.build_func(cfg, *args, **kwargs, registry=self)
File "/home/apurvabadithela/miniconda3/envs/detection/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
obj = obj_cls(**args) # type: ignore
TypeError: __init__() got an unexpected keyword argument 'pklfile_prefix'
VeeranjaneyuluToka
VeeranajenyuluT
Prerequisite
Task
I have modified the scripts/configs, or I'm working on my own tasks/models/datasets.
Branch
main branch https://github.com/open-mmlab/mmdetection3d
Environment
sys.platform: linux Python: 3.8.16 (default, Jan 17 2023, 23:13:24) [GCC 11.2.0] CUDA available: True numpy_random_seed: 2147483648 GPU 0,1: NVIDIA RTX A6000 CUDA_HOME: /home/apurvabadithela/miniconda3/envs/detection NVCC: Cuda compilation tools, release 11.7, V11.7.99 GCC: gcc (Ubuntu 10.5.0-1ubuntu1~22.04) 10.5.0 PyTorch: 1.13.1 PyTorch compiling details: PyTorch built with:
TorchVision: 0.14.1 OpenCV: 4.7.0 MMEngine: 0.9.1 MMDetection: 3.2.0 MMDetection3D: 1.4.0+fe25f7a spconv2.0: True
Reproduces the problem - code sample
I want to save prediction results from running the project mmdet3d/projects/BEVFusion. The documentation states to add the tag pklfile_prefix to the test_evaluator, which I do in the config file: config_file by adding the following line after:
test_evaluator.update({'pklfile_prefix':'/home/apurvabadithela/nuscenes_dataset/inference_results/bevfusion_model/results.pkl'})Reproduces the problem - command or script
Then, I run the following from command line:
Reproduces the problem - error message
And I get the following error message.
Additional information