TypeError: EMQuasiDenseFasterRCNN: __init__() missing 3 required positional arguments: 'channels', 'proto_num', and 'stage_num'
Zachein opened this issue · 13 comments
Zachein commented
Thanks for your excellent work. While I was running the train.py , I ran into an error
Traceback (most recent call last):
File "D:\ProgramData\Anaconda3\envs\PCAN\lib\site-packages\mmcv\utils\registry.py", line 179, in build_from_cfg
return obj_cls(**args)
TypeError: __init__() missing 3 required positional arguments: 'channels', 'proto_num', and 'stage_num'
During handling of the above exception, another exception occurred:
raise type(e)(f'{obj_cls.__name__}: {e}')
TypeError: EMQuasiDenseFasterRCNN: __init__() missing 3 required positional arguments: 'channels', 'proto_num', and 'stage_num'
What is the possible solution for the problem?
lkeab commented
Do you use the right config file? Try to use "bash scripts/train_pcan.sh".
Zachein commented
Do you use the right config file? Try to use "bash scripts/train_pcan.sh".
I don't find this folder bash scripts. There is a file path named scripts/train_pcan.sh. I try to use it, and the same problem happened again.
lkeab commented
Have you installed pcan following our provided instructions? The error indicate the config file is wrongly used.
Zachein commented
Have you installed pcan following our provided instructions? The error indicate the config file is wrongly used.
I have installed pcan, here is the version information.
pcan 0.1.0+650969 dev_0 <develop>
lkeab commented
Could you also paste your command for introducing this error?
Zachein commented
Could you also paste your command for introducing this error?
Here is the process and result of my command line run. I am not sure what you needed to see, so I put it all up.
$ python tools/train.py configs/qdtrack-frcnn_r50_fpn_12e_bdd100k.py
D:\ProgramData\Anaconda3\envs\PCAN\lib\site-packages\mmcv\utils\registry.py:71: UserWarning: The old API of register_module(module, force=False) is deprecated and will be removed, please use the new API register_module(name=None, force=False, module=None) instead.
warnings.warn(
2022-02-22 00:51:52,898 - pcan - INFO - Environment info:
------------------------------------------------------------
sys.platform: win32
Python: 3.9.10 | packaged by conda-forge | (main, Feb 1 2022, 21:21:54) [MSC v.1929 64 bit (AMD64)]
CUDA available: True
CUDA_HOME: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0
NVCC: Build cuda_11.0_bu.relgpu_drvr445TC445_37.28540450_0
GPU 0: GeForce RTX 3090
GCC: gcc (MinGW.org GCC Build-2) 9.2.0
Copyright (C) 2019 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
PyTorch: 1.10.2
PyTorch compiling details: PyTorch built with:
- C++ Version: 199711
- MSVC 192829337
- Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
- OpenMP 2019
- LAPACK is enabled (usually provided by MKL)
- CPU capability usage: AVX512
- CUDA Runtime 11.3
- 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 8.2
- Magma 2.5.4
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=C:/cb/pytorch_1000000000000/work/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj -DUSE_PTHREADPOOL -openmp:experimental -IC:/cb/pytorch_1000000000000/work/mkl/include -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.2, 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=OFF, USE_NNPACK=OFF, USE_OPENMP=ON,
TorchVision: 0.11.3
OpenCV: 4.5.5
MMCV: 1.2.7
pcan: 0.1.0+0650969
------------------------------------------------------------
2022-02-22 00:51:52,904 - pcan - INFO - Distributed training: False
2022-02-22 00:51:53,586 - pcan - INFO - Config:
model = dict(
type='EMQuasiDenseFasterRCNN',
pretrained='torchvision://resnet50',
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=True),
norm_eval=True,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
roi_head=dict(
type='QuasiDenseRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=8,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
track_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
track_head=dict(
type='QuasiDenseEmbedHead',
num_convs=4,
num_fcs=1,
embed_channels=256,
norm_cfg=dict(type='GN', num_groups=32),
loss_track=dict(type='MultiPosCrossEntropyLoss', loss_weight=0.25),
loss_track_aux=dict(
type='L2Loss',
neg_pos_ub=3,
pos_margin=0,
neg_margin=0.3,
hard_mining=True,
loss_weight=1.0))),
tracker=dict(
type='QuasiDenseEmbedTracker',
init_score_thr=0.7,
obj_score_thr=0.3,
match_score_thr=0.5,
memo_tracklet_frames=10,
memo_backdrop_frames=1,
memo_momentum=0.8,
nms_conf_thr=0.5,
nms_backdrop_iou_thr=0.3,
nms_class_iou_thr=0.7,
with_cats=True,
match_metric='bisoftmax'),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False),
embed=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='CombinedSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=3,
add_gt_as_proposals=True,
pos_sampler=dict(type='InstanceBalancedPosSampler'),
neg_sampler=dict(
type='IoUBalancedNegSampler',
floor_thr=-1,
floor_fraction=0,
num_bins=3)))),
test_cfg=dict(
rpn=dict(
nms_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.5,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
dataset_type = 'BDDVideoDataset'
data_root = 'data/bdd/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadMultiImagesFromFile'),
dict(type='SeqLoadAnnotations', with_bbox=True, with_ins_id=True),
dict(type='SeqResize', img_scale=(1296, 720), keep_ratio=True),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
dict(
type='SeqNormalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='SeqPad', size_divisor=32),
dict(type='SeqDefaultFormatBundle'),
dict(
type='SeqCollect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_match_indices'],
ref_prefix='ref')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1296, 720),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='VideoCollect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=[
dict(
type='BDDVideoDataset',
ann_file=
'data/bdd/labels/box_track_20/box_track_train_cocofmt.json',
img_prefix='data/bdd/images/track/train/',
key_img_sampler=dict(interval=1),
ref_img_sampler=dict(num_ref_imgs=1, scope=3, method='uniform'),
pipeline=[
dict(type='LoadMultiImagesFromFile'),
dict(
type='SeqLoadAnnotations',
with_bbox=True,
with_ins_id=True),
dict(type='SeqResize', img_scale=(1296, 720), keep_ratio=True),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
dict(
type='SeqNormalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='SeqPad', size_divisor=32),
dict(type='SeqDefaultFormatBundle'),
dict(
type='SeqCollect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_match_indices'],
ref_prefix='ref')
]),
dict(
type='BDDVideoDataset',
load_as_video=False,
ann_file='data/bdd/labels/det_20/det_train_cocofmt.json',
img_prefix='data/bdd/images/100k/train/',
pipeline=[
dict(type='LoadMultiImagesFromFile'),
dict(
type='SeqLoadAnnotations',
with_bbox=True,
with_ins_id=True),
dict(type='SeqResize', img_scale=(1296, 720), keep_ratio=True),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
dict(
type='SeqNormalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='SeqPad', size_divisor=32),
dict(type='SeqDefaultFormatBundle'),
dict(
type='SeqCollect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_match_indices'],
ref_prefix='ref')
])
],
val=dict(
type='BDDVideoDataset',
ann_file='data/bdd/labels/box_track_20/box_track_val_cocofmt.json',
img_prefix='data/bdd/images/track/val/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1296, 720),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='VideoCollect', keys=['img'])
])
]),
test=dict(
type='BDDVideoDataset',
ann_file='data/bdd/labels/box_track_20/box_track_val_cocofmt.json',
img_prefix='data/bdd/images/track/val/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1296, 720),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='VideoCollect', keys=['img'])
])
]))
optimizer = dict(type='SGD', lr=0.04, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.001,
step=[8, 11])
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
evaluation = dict(metric=['bbox', 'track'], interval=2)
work_dir = './work_dirs\qdtrack-frcnn_r50_fpn_12e_bdd100k'
gpu_ids = range(0, 1)
Traceback (most recent call last):
File "D:\ProgramData\Anaconda3\envs\PCAN\lib\site-packages\mmcv\utils\registry.py", line 179, in build_from_cfg
return obj_cls(**args)
TypeError: __init__() missing 3 required positional arguments: 'channels', 'proto_num', and 'stage_num'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "D:\pcan-main\tools\train.py", line 168, in <module>
main()
File "D:\pcan-main\tools\train.py", line 137, in main
model = build_model(
File "d:\pcan-main\pcan\models\builder.py", line 15, in build_model
return build(cfg, MODELS, dict(train_cfg=train_cfg, test_cfg=test_cfg))
File "D:\ProgramData\Anaconda3\envs\PCAN\lib\site-packages\mmdet-2.10.0-py3.9.egg\mmdet\models\builder.py", line 34, in build
return build_from_cfg(cfg, registry, default_args)
File "D:\ProgramData\Anaconda3\envs\PCAN\lib\site-packages\mmcv\utils\registry.py", line 182, in build_from_cfg
raise type(e)(f'{obj_cls.__name__}: {e}')
TypeError: EMQuasiDenseFasterRCNN: __init__() missing 3 required positional arguments: 'channels', 'proto_num', and 'stage_num'
lkeab commented
what if try this one "bash ./tools/dist_train.sh configs/segtrack-frcnn_r50_fpn_12e_bdd10k_fixed_pcan.py 4 --work-dir ./pcan_training_result/" or "python tools/train.py configs/qdtrack-frcnn_r50_fpn_12e_bdd10k_fixed_pcan.py"?
Zachein commented
I am sorry that I don't find this file configs/qdtrack-frcnn_r50_fpn_12e_bdd10k_fixed_pcan.py. I have tried five configuration files in the CONFIG folder, and both files prefixed with qdtrack reported the same error.
lkeab commented
it's my typo error, you should use this configs/segtrack-frcnn_r50_fpn_12e_bdd10k_fixed_pcan.py to run. UseThe config file prefixed with segtrack.
lkeab commented
You mentioned for trying five configs, how about this configs/segtrack-frcnn_r50_fpn_12e_bdd10k_fixed_pcan.py. What should be run is QuasiDenseMaskRCNN not EMQuasiDenseFasterRCNN.
Zachein commented
The training programm is running correctly when I use the config configs/segtrack-frcnn_r50_fpn_12e_bdd10k_fixed_pcan.py. But I don't change EMQuasiDenseFasterRCNN to QuasiDenseMaskRCNN. How will this affect the results? and the program is running now, should I stop the training process and change it?
lkeab commented
You use the wrong config file for training. So that happens. You could have an analysis on the config dependency relations. If the program is running, then just keep it.
Zachein commented
Thanks for your guidance, hope u get better achievement.