[BUG] UnboundLocalError: local variable 'img_path' referenced before assignment #2420
Description
when i run 'python tools/test.py configs/votenet/votenet_8xb8_scannet-3d.py checkpoints/votenet_8x8_scannet-3d-18class_20210823_234503-cf8134fa.pth --show --show-dir ./data/scannet/show_results' to get show_results, but get following results.
Branch
master branch https://github.com/open-mmlab/mmdetection3d/tree/dev-1.x
/home/miniconda3/envs/openmmlab/lib/python3.8/site-packages/MinkowskiEngine/__init__.py:36: UserWarning: The environment variable `OMP_NUM_THREADS` not set. MinkowskiEngine will automatically set `OMP_NUM_THREADS=16`. If you want to set `OMP_NUM_THREADS` manually, please export it on the command line before running a python script. e.g. `export OMP_NUM_THREADS=12; python your_program.py`. It is recommended to set it below 24.
warnings.warn(
04/09 21:07:54 - mmengine - INFO -
------------------------------------------------------------
System environment:
sys.platform: linux
Python: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]
CUDA available: True
numpy_random_seed: 1313724512
GPU 0: GeForce RTX 3090
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.2, V11.2.152
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.10.0+cu111
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX512
- CUDA Runtime 11.1
- 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_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
- CuDNN 8.0.5
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -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 -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, 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,
TorchVision: 0.11.0+cu111
OpenCV: 4.7.0
MMEngine: 0.7.1
Runtime environment:
cudnn_benchmark: False
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: None
Distributed launcher: none
Distributed training: False
GPU number: 1
------------------------------------------------------------
04/09 21:07:56 - mmengine - INFO - Config:
dataset_type = 'ScanNetDataset'
data_root = '../all_data/scannet_v2/'
metainfo = dict(
classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'bookshelf', 'picture', 'counter', 'desk', 'curtain',
'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
'garbagebin'))
backend_args = None
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2],
backend_args=None),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True,
backend_args=None),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='PointSegClassMapping'),
dict(type='PointSample', num_points=40000),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[1.0, 1.0],
shift_height=True),
dict(
type='Pack3DDetInputs',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
'pts_instance_mask'
])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2],
backend_args=None),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1.0, 1.0],
translation_std=[0, 0, 0]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointSample', num_points=40000)
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=8,
num_workers=4,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=5,
dataset=dict(
type='ScanNetDataset',
data_root='../all_data/scannet_v2/',
ann_file='scannet_infos_train_v1.0.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2],
backend_args=None),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True,
backend_args=None),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='PointSegClassMapping'),
dict(type='PointSample', num_points=40000),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[1.0, 1.0],
shift_height=True),
dict(
type='Pack3DDetInputs',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d',
'pts_semantic_mask', 'pts_instance_mask'
])
],
filter_empty_gt=False,
metainfo=dict(
classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')),
box_type_3d='Depth',
backend_args=None)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='ScanNetDataset',
data_root='../all_data/scannet_v2/',
ann_file='scannet_infos_val_v1.0.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2],
backend_args=None),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1.0, 1.0],
translation_std=[0, 0, 0]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointSample', num_points=40000)
]),
dict(type='Pack3DDetInputs', keys=['points'])
],
metainfo=dict(
classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')),
test_mode=True,
box_type_3d='Depth',
backend_args=None))
test_dataloader = dict(
batch_size=1,
num_workers=1,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='ScanNetDataset',
data_root='../all_data/scannet_v2/',
ann_file='scannet_infos_val_v1.0.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2],
backend_args=None),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1.0, 1.0],
translation_std=[0, 0, 0]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointSample', num_points=40000)
]),
dict(type='Pack3DDetInputs', keys=['points'])
],
metainfo=dict(
classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')),
test_mode=True,
box_type_3d='Depth',
backend_args=None))
val_evaluator = dict(type='IndoorMetric')
test_evaluator = dict(type='IndoorMetric')
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer',
vis_backends=[dict(type='LocalVisBackend')],
name='visualizer')
model = dict(
type='VoteNet',
data_preprocessor=dict(type='Det3DDataPreprocessor'),
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)),
bbox_head=dict(
type='VoteHead',
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),
pred_layer_cfg=dict(
in_channels=128, shared_conv_channels=(128, 128), bias=True),
objectness_loss=dict(
type='mmdet.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='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0),
dir_res_loss=dict(
type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=10.0),
size_class_loss=dict(
type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0),
size_res_loss=dict(
type='mmdet.SmoothL1Loss',
reduction='sum',
loss_weight=3.3333333333333335),
semantic_loss=dict(
type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0),
num_classes=18,
bbox_coder=dict(
type='PartialBinBasedBBoxCoder',
num_sizes=18,
num_dir_bins=1,
with_rot=False,
mean_sizes=[[0.76966727, 0.8116021, 0.92573744],
[1.876858, 1.8425595, 1.1931566],
[0.61328, 0.6148609, 0.7182701],
[1.3955007, 1.5121545, 0.83443564],
[0.97949594, 1.0675149, 0.6329687],
[0.531663, 0.5955577, 1.7500148],
[0.9624706, 0.72462326, 1.1481868],
[0.83221924, 1.0490936, 1.6875663],
[0.21132214, 0.4206159, 0.5372846],
[1.4440073, 1.8970833, 0.26985747],
[1.0294262, 1.4040797, 0.87554324],
[1.3766412, 0.65521795, 1.6813129],
[0.6650819, 0.71111923, 1.298853],
[0.41999173, 0.37906948, 1.7513971],
[0.59359556, 0.5912492, 0.73919016],
[0.50867593, 0.50656086, 0.30136237],
[1.1511526, 1.0546296, 0.49706793],
[0.47535285, 0.49249494, 0.5802117]])),
train_cfg=dict(
pos_distance_thr=0.3, neg_distance_thr=0.6, sample_mode='vote'),
test_cfg=dict(
sample_mode='seed',
nms_thr=0.25,
score_thr=0.05,
per_class_proposal=True))
lr = 0.008
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.008, weight_decay=0.01),
clip_grad=dict(max_norm=10, norm_type=2))
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=36, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=36,
by_epoch=True,
milestones=[24, 32],
gamma=0.1)
]
auto_scale_lr = dict(enable=False, base_batch_size=64)
default_scope = 'mmdet3d'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=30),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=-1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(
type='Det3DVisualizationHook',
draw=True,
show=True,
wait_time=2,
test_out_dir='./data/scannet/show_results',
vis_task=None))
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'))
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = 'checkpoints/votenet_8x8_scannet-3d-18class_20210823_234503-cf8134fa.pth'
resume = False
launcher = 'none'
work_dir = './work_dirs/votenet_8xb8_scannet-3d'
/home/miniconda3/envs/openmmlab/lib/python3.8/site-packages/mmcv/cnn/bricks/conv_module.py:153: UserWarning: Unnecessary conv bias before batch/instance norm
warnings.warn(
04/09 21:08:04 - 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.
/opt/data/private/mmdetection3d-dev-1.x/mmdet3d/engine/hooks/visualization_hook.py:67: UserWarning: The show is True, it means that only the prediction results are visualized without storing data, so vis_backends needs to be excluded.
warnings.warn('The show is True, it means that only '
04/09 21:08:04 - 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_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_train:
(VERY_LOW ) CheckpointHook
--------------------
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_run:
(BELOW_NORMAL) LoggerHook
--------------------
04/09 21:08:06 - mmengine - INFO - ------------------------------
04/09 21:08:06 - mmengine - INFO - The length of the dataset: 312
04/09 21:08:06 - mmengine - INFO - The number of instances per category in the dataset:
+----------------+--------+
| category | number |
+----------------+--------+
| cabinet | 372 |
| bed | 81 |
| chair | 1368 |
| sofa | 97 |
| table | 350 |
| door | 467 |
| window | 282 |
| bookshelf | 77 |
| picture | 222 |
| counter | 52 |
| desk | 127 |
| curtain | 67 |
| refrigerator | 57 |
| showercurtrain | 28 |
| toilet | 58 |
| sink | 98 |
| bathtub | 31 |
| garbagebin | 530 |
+----------------+--------+
04/09 21:08:06 - mmengine - WARNING - The prefix is not set in metric class IndoorMetric.
Loads checkpoint by local backend from path: checkpoints/votenet_8x8_scannet-3d-18class_20210823_234503-cf8134fa.pth
04/09 21:08:07 - mmengine - INFO - Load checkpoint from checkpoints/votenet_8x8_scannet-3d-18class_20210823_234503-cf8134fa.pth
Traceback (most recent call last):
File "tools/test.py", line 128, in <module>
main()
File "tools/test.py", line 124, in main
runner.test()
File "/home/miniconda3/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1752, in test
metrics = self.test_loop.run() # type: ignore
File "/home/miniconda3/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 435, in run
self.run_iter(idx, data_batch)
File "/home/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "/home/miniconda3/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 456, in run_iter
self.runner.call_hook(
File "/home/miniconda3/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1768, in call_hook
getattr(hook, fn_name)(self, **kwargs)
File "/opt/data/private/mmdetection3d-dev-1.x/mmdet3d/engine/hooks/visualization_hook.py", line 164, in after_test_iter
out_file = osp.basename(img_path)
UnboundLocalError: local variable 'img_path' referenced before assignment