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KeyError: 'SemiBalanceSampler is not in the sampler registry'
See original GitHub issueI try to run in Google Colab with 1 gpu but get error. My command for run:
python train.py /content/SoftTeacher/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k.py \
--launcher none --cfg-options percent=10 fond=1 --gpus 1
Traceback:
2021-11-02 09:31:04,029 - mmdet.ssod - INFO - [<StreamHandler <stderr> (INFO)>, <FileHandler /content/SoftTeacher/tools/work_dirs/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k/20211102_093103.log (INFO)>]
2021-11-02 09:31:04,029 - mmdet.ssod - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.12 (default, Sep 10 2021, 00:21:48) [GCC 7.5.0]
CUDA available: True
GPU 0: Tesla T4
CUDA_HOME: /usr/local/cuda
NVCC: Build cuda_11.1.TC455_06.29190527_0
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.9.0+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 v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CPU capability usage: AVX2
- 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 -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=1.9.0, 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: 0.10.0+cu111
OpenCV: 4.1.2
MMCV: 1.3.16
MMCV Compiler: GCC 7.5
MMCV CUDA Compiler: 11.1
MMDetection: 2.18.0+2f5b0df
------------------------------------------------------------
2021-11-02 09:31:06,848 - mmdet.ssod - INFO - Distributed training: False
2021-11-02 09:31:09,585 - mmdet.ssod - INFO - Config:
model = dict(
type='SoftTeacher',
model=dict(
type='FasterRCNN',
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='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
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='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
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=80,
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='L1Loss', loss_weight=1.0))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
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_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=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)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100))),
train_cfg=dict(
use_teacher_proposal=False,
pseudo_label_initial_score_thr=0.5,
rpn_pseudo_threshold=0.9,
cls_pseudo_threshold=0.9,
reg_pseudo_threshold=0.02,
jitter_times=10,
jitter_scale=0.06,
min_pseduo_box_size=0,
unsup_weight=2.0),
test_cfg=dict(inference_on='student'))
dataset_type = 'CocoDataset'
data_root = '/content/data/'
img_norm_cfg = dict(
mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Sequential',
transforms=[
dict(
type='RandResize',
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandFlip', flip_ratio=0.5),
dict(
type='OneOf',
transforms=[
dict(type='Identity'),
dict(type='AutoContrast'),
dict(type='RandEqualize'),
dict(type='RandSolarize'),
dict(type='RandColor'),
dict(type='RandContrast'),
dict(type='RandBrightness'),
dict(type='RandSharpness'),
dict(type='RandPosterize')
])
],
record=True),
dict(type='Pad', size_divisor=32),
dict(
type='Normalize',
mean=[103.53, 116.28, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(type='ExtraAttrs', tag='sup'),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg',
'pad_shape', 'scale_factor', 'tag'))
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[103.53, 116.28, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=2,
train=dict(
type='SemiDataset',
sup=dict(
type='CocoDataset',
ann_file='/content/data/labels/train/train.json',
img_prefix='/content/data/images/train',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Sequential',
transforms=[
dict(
type='RandResize',
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandFlip', flip_ratio=0.5),
dict(
type='OneOf',
transforms=[
dict(type='Identity'),
dict(type='AutoContrast'),
dict(type='RandEqualize'),
dict(type='RandSolarize'),
dict(type='RandColor'),
dict(type='RandContrast'),
dict(type='RandBrightness'),
dict(type='RandSharpness'),
dict(type='RandPosterize')
])
],
record=True),
dict(type='Pad', size_divisor=32),
dict(
type='Normalize',
mean=[103.53, 116.28, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(type='ExtraAttrs', tag='sup'),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_shape', 'img_shape',
'img_norm_cfg', 'pad_shape', 'scale_factor',
'tag'))
]),
unsup=dict(
type='CocoDataset',
ann_file='/content/data/labels/train/train.json',
img_prefix='/content/data/images/train',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='PseudoSamples', with_bbox=True),
dict(
type='MultiBranch',
unsup_student=[
dict(
type='Sequential',
transforms=[
dict(
type='RandResize',
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandFlip', flip_ratio=0.5),
dict(
type='ShuffledSequential',
transforms=[
dict(
type='OneOf',
transforms=[
dict(type='Identity'),
dict(type='AutoContrast'),
dict(type='RandEqualize'),
dict(type='RandSolarize'),
dict(type='RandColor'),
dict(type='RandContrast'),
dict(type='RandBrightness'),
dict(type='RandSharpness'),
dict(type='RandPosterize')
]),
dict(
type='OneOf',
transforms=[{
'type': 'RandTranslate',
'x': (-0.1, 0.1)
}, {
'type': 'RandTranslate',
'y': (-0.1, 0.1)
}, {
'type': 'RandRotate',
'angle': (-30, 30)
},
[{
'type':
'RandShear',
'x': (-30, 30)
}, {
'type':
'RandShear',
'y': (-30, 30)
}]])
]),
dict(
type='RandErase',
n_iterations=(1, 5),
size=[0, 0.2],
squared=True)
],
record=True),
dict(type='Pad', size_divisor=32),
dict(
type='Normalize',
mean=[103.53, 116.28, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(type='ExtraAttrs', tag='unsup_student'),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_shape', 'img_shape',
'img_norm_cfg', 'pad_shape',
'scale_factor', 'tag',
'transform_matrix'))
],
unsup_teacher=[
dict(
type='Sequential',
transforms=[
dict(
type='RandResize',
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandFlip', flip_ratio=0.5)
],
record=True),
dict(type='Pad', size_divisor=32),
dict(
type='Normalize',
mean=[103.53, 116.28, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(type='ExtraAttrs', tag='unsup_teacher'),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_shape', 'img_shape',
'img_norm_cfg', 'pad_shape',
'scale_factor', 'tag',
'transform_matrix'))
])
],
filter_empty_gt=False)),
val=dict(
type='CocoDataset',
ann_file='/content/data/labels/val/val.json',
img_prefix='/content/data/images/val',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[103.53, 116.28, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='CocoDataset',
ann_file='/content/data/labels/test/test.json',
img_prefix='/content/data/images/test',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[103.53, 116.28, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
sampler=dict(
train=dict(
type='SemiBalanceSampler',
sample_ratio=[1, 1],
by_prob=True,
epoch_length=200)))
evaluation = dict(interval=4000, metric='bbox', type='SubModulesDistEvalHook')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[3000, 4000])
runner = dict(type='IterBasedRunner', max_iters=5000)
checkpoint_config = dict(interval=1000, by_epoch=False, max_keep_ckpts=2)
log_config = dict(
interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
custom_hooks = [
dict(type='NumClassCheckHook'),
dict(type='WeightSummary'),
dict(type='MeanTeacher', momentum=0.999, interval=1, warm_up=0)
]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
mmdet_base = '../../thirdparty/mmdetection/configs/_base_'
strong_pipeline = [
dict(
type='Sequential',
transforms=[
dict(
type='RandResize',
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandFlip', flip_ratio=0.5),
dict(
type='ShuffledSequential',
transforms=[
dict(
type='OneOf',
transforms=[
dict(type='Identity'),
dict(type='AutoContrast'),
dict(type='RandEqualize'),
dict(type='RandSolarize'),
dict(type='RandColor'),
dict(type='RandContrast'),
dict(type='RandBrightness'),
dict(type='RandSharpness'),
dict(type='RandPosterize')
]),
dict(
type='OneOf',
transforms=[{
'type': 'RandTranslate',
'x': (-0.1, 0.1)
}, {
'type': 'RandTranslate',
'y': (-0.1, 0.1)
}, {
'type': 'RandRotate',
'angle': (-30, 30)
},
[{
'type': 'RandShear',
'x': (-30, 30)
}, {
'type': 'RandShear',
'y': (-30, 30)
}]])
]),
dict(
type='RandErase',
n_iterations=(1, 5),
size=[0, 0.2],
squared=True)
],
record=True),
dict(type='Pad', size_divisor=32),
dict(
type='Normalize',
mean=[103.53, 116.28, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(type='ExtraAttrs', tag='unsup_student'),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg',
'pad_shape', 'scale_factor', 'tag', 'transform_matrix'))
]
weak_pipeline = [
dict(
type='Sequential',
transforms=[
dict(
type='RandResize',
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandFlip', flip_ratio=0.5)
],
record=True),
dict(type='Pad', size_divisor=32),
dict(
type='Normalize',
mean=[103.53, 116.28, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(type='ExtraAttrs', tag='unsup_teacher'),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg',
'pad_shape', 'scale_factor', 'tag', 'transform_matrix'))
]
unsup_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='PseudoSamples', with_bbox=True),
dict(
type='MultiBranch',
unsup_student=[
dict(
type='Sequential',
transforms=[
dict(
type='RandResize',
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandFlip', flip_ratio=0.5),
dict(
type='ShuffledSequential',
transforms=[
dict(
type='OneOf',
transforms=[
dict(type='Identity'),
dict(type='AutoContrast'),
dict(type='RandEqualize'),
dict(type='RandSolarize'),
dict(type='RandColor'),
dict(type='RandContrast'),
dict(type='RandBrightness'),
dict(type='RandSharpness'),
dict(type='RandPosterize')
]),
dict(
type='OneOf',
transforms=[{
'type': 'RandTranslate',
'x': (-0.1, 0.1)
}, {
'type': 'RandTranslate',
'y': (-0.1, 0.1)
}, {
'type': 'RandRotate',
'angle': (-30, 30)
},
[{
'type': 'RandShear',
'x': (-30, 30)
}, {
'type': 'RandShear',
'y': (-30, 30)
}]])
]),
dict(
type='RandErase',
n_iterations=(1, 5),
size=[0, 0.2],
squared=True)
],
record=True),
dict(type='Pad', size_divisor=32),
dict(
type='Normalize',
mean=[103.53, 116.28, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(type='ExtraAttrs', tag='unsup_student'),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_shape', 'img_shape',
'img_norm_cfg', 'pad_shape', 'scale_factor', 'tag',
'transform_matrix'))
],
unsup_teacher=[
dict(
type='Sequential',
transforms=[
dict(
type='RandResize',
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandFlip', flip_ratio=0.5)
],
record=True),
dict(type='Pad', size_divisor=32),
dict(
type='Normalize',
mean=[103.53, 116.28, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(type='ExtraAttrs', tag='unsup_teacher'),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_shape', 'img_shape',
'img_norm_cfg', 'pad_shape', 'scale_factor', 'tag',
'transform_matrix'))
])
]
fp16 = dict(loss_scale='dynamic')
percent = 10
fond = 1
work_dir = './work_dirs/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k'
cfg_name = 'soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k'
gpu_ids = range(0, 1)
2021-11-02 09:31:10,382 - mmdet.ssod - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'open-mmlab://detectron2/resnet50_caffe'}
2021-11-02 09:31:10,382 - mmcv - INFO - load model from: open-mmlab://detectron2/resnet50_caffe
2021-11-02 09:31:10,382 - mmcv - INFO - Use load_from_openmmlab loader
2021-11-02 09:31:10,459 - mmcv - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: conv1.bias
2021-11-02 09:31:10,481 - mmdet.ssod - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2021-11-02 09:31:10,504 - mmdet.ssod - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}
2021-11-02 09:31:10,509 - mmdet.ssod - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]
2021-11-02 09:31:10,632 - mmdet.ssod - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'open-mmlab://detectron2/resnet50_caffe'}
2021-11-02 09:31:10,632 - mmcv - INFO - load model from: open-mmlab://detectron2/resnet50_caffe
2021-11-02 09:31:10,632 - mmcv - INFO - Use load_from_openmmlab loader
2021-11-02 09:31:10,688 - mmcv - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: conv1.bias
2021-11-02 09:31:10,709 - mmdet.ssod - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2021-11-02 09:31:10,731 - mmdet.ssod - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}
2021-11-02 09:31:10,736 - mmdet.ssod - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]
loading annotations into memory...
Done (t=0.03s)
creating index...
index created!
loading annotations into memory...
Done (t=0.03s)
creating index...
index created!
meta = {'env_info': 'sys.platform: linux\nPython: 3.7.12 (default, Sep 10 2021, 00:21:48) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla T4\nCUDA_HOME: /usr/local/cuda\nNVCC: Build cuda_11.1.TC455_06.29190527_0\nGCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\nPyTorch: 1.9.0+cu111\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.1\n - 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\n - CuDNN 8.0.5\n - Magma 2.5.2\n - 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 -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=1.9.0, 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, \n\nTorchVision: 0.10.0+cu111\nOpenCV: 4.1.2\nMMCV: 1.3.16\nMMCV Compiler: GCC 7.5\nMMCV CUDA Compiler: 11.1\nMMDetection: 2.18.0+2f5b0df', 'config': "model = dict(\n type='SoftTeacher',\n model=dict(\n type='FasterRCNN',\n backbone=dict(\n type='ResNet',\n depth=50,\n num_stages=4,\n out_indices=(0, 1, 2, 3),\n frozen_stages=1,\n norm_cfg=dict(type='BN', requires_grad=False),\n norm_eval=True,\n style='caffe',\n init_cfg=dict(\n type='Pretrained',\n checkpoint='open-mmlab://detectron2/resnet50_caffe')),\n neck=dict(\n type='FPN',\n in_channels=[256, 512, 1024, 2048],\n out_channels=256,\n num_outs=5),\n rpn_head=dict(\n type='RPNHead',\n in_channels=256,\n feat_channels=256,\n anchor_generator=dict(\n type='AnchorGenerator',\n scales=[8],\n ratios=[0.5, 1.0, 2.0],\n strides=[4, 8, 16, 32, 64]),\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[1.0, 1.0, 1.0, 1.0]),\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n roi_head=dict(\n type='StandardRoIHead',\n bbox_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(\n type='RoIAlign', output_size=7, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n bbox_head=dict(\n type='Shared2FCBBoxHead',\n in_channels=256,\n fc_out_channels=1024,\n roi_feat_size=7,\n num_classes=80,\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[0.1, 0.1, 0.2, 0.2]),\n reg_class_agnostic=False,\n loss_cls=dict(\n type='CrossEntropyLoss',\n use_sigmoid=False,\n loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0))),\n train_cfg=dict(\n rpn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.7,\n neg_iou_thr=0.3,\n min_pos_iou=0.3,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.5,\n neg_pos_ub=-1,\n add_gt_as_proposals=False),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n rpn_proposal=dict(\n nms_pre=2000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.5,\n min_pos_iou=0.5,\n match_low_quality=False,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=512,\n pos_fraction=0.25,\n neg_pos_ub=-1,\n add_gt_as_proposals=True),\n pos_weight=-1,\n debug=False)),\n test_cfg=dict(\n rpn=dict(\n nms_pre=1000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n score_thr=0.05,\n nms=dict(type='nms', iou_threshold=0.5),\n max_per_img=100))),\n train_cfg=dict(\n use_teacher_proposal=False,\n pseudo_label_initial_score_thr=0.5,\n rpn_pseudo_threshold=0.9,\n cls_pseudo_threshold=0.9,\n reg_pseudo_threshold=0.02,\n jitter_times=10,\n jitter_scale=0.06,\n min_pseduo_box_size=0,\n unsup_weight=2.0),\n test_cfg=dict(inference_on='student'))\ndataset_type = 'CocoDataset'\ndata_root = '/content/data/'\nimg_norm_cfg = dict(\n mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True),\n dict(\n type='Sequential',\n transforms=[\n dict(\n type='RandResize',\n img_scale=[(1333, 400), (1333, 1200)],\n multiscale_mode='range',\n keep_ratio=True),\n dict(type='RandFlip', flip_ratio=0.5),\n dict(\n type='OneOf',\n transforms=[\n dict(type='Identity'),\n dict(type='AutoContrast'),\n dict(type='RandEqualize'),\n dict(type='RandSolarize'),\n dict(type='RandColor'),\n dict(type='RandContrast'),\n dict(type='RandBrightness'),\n dict(type='RandSharpness'),\n dict(type='RandPosterize')\n ])\n ],\n record=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='Normalize',\n mean=[103.53, 116.28, 123.675],\n std=[1.0, 1.0, 1.0],\n to_rgb=False),\n dict(type='ExtraAttrs', tag='sup'),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels'],\n meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg',\n 'pad_shape', 'scale_factor', 'tag'))\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[103.53, 116.28, 123.675],\n std=[1.0, 1.0, 1.0],\n to_rgb=False),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=1,\n workers_per_gpu=2,\n train=dict(\n type='SemiDataset',\n sup=dict(\n type='CocoDataset',\n ann_file='/content/data/labels/train/train.json',\n img_prefix='/content/data/images/train',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True),\n dict(\n type='Sequential',\n transforms=[\n dict(\n type='RandResize',\n img_scale=[(1333, 400), (1333, 1200)],\n multiscale_mode='range',\n keep_ratio=True),\n dict(type='RandFlip', flip_ratio=0.5),\n dict(\n type='OneOf',\n transforms=[\n dict(type='Identity'),\n dict(type='AutoContrast'),\n dict(type='RandEqualize'),\n dict(type='RandSolarize'),\n dict(type='RandColor'),\n dict(type='RandContrast'),\n dict(type='RandBrightness'),\n dict(type='RandSharpness'),\n dict(type='RandPosterize')\n ])\n ],\n record=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='Normalize',\n mean=[103.53, 116.28, 123.675],\n std=[1.0, 1.0, 1.0],\n to_rgb=False),\n dict(type='ExtraAttrs', tag='sup'),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels'],\n meta_keys=('filename', 'ori_shape', 'img_shape',\n 'img_norm_cfg', 'pad_shape', 'scale_factor',\n 'tag'))\n ]),\n unsup=dict(\n type='CocoDataset',\n ann_file='/content/data/labels/train/train.json',\n img_prefix='/content/data/images/train',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='PseudoSamples', with_bbox=True),\n dict(\n type='MultiBranch',\n unsup_student=[\n dict(\n type='Sequential',\n transforms=[\n dict(\n type='RandResize',\n img_scale=[(1333, 400), (1333, 1200)],\n multiscale_mode='range',\n keep_ratio=True),\n dict(type='RandFlip', flip_ratio=0.5),\n dict(\n type='ShuffledSequential',\n transforms=[\n dict(\n type='OneOf',\n transforms=[\n dict(type='Identity'),\n dict(type='AutoContrast'),\n dict(type='RandEqualize'),\n dict(type='RandSolarize'),\n dict(type='RandColor'),\n dict(type='RandContrast'),\n dict(type='RandBrightness'),\n dict(type='RandSharpness'),\n dict(type='RandPosterize')\n ]),\n dict(\n type='OneOf',\n transforms=[{\n 'type': 'RandTranslate',\n 'x': (-0.1, 0.1)\n }, {\n 'type': 'RandTranslate',\n 'y': (-0.1, 0.1)\n }, {\n 'type': 'RandRotate',\n 'angle': (-30, 30)\n },\n [{\n 'type':\n 'RandShear',\n 'x': (-30, 30)\n }, {\n 'type':\n 'RandShear',\n 'y': (-30, 30)\n }]])\n ]),\n dict(\n type='RandErase',\n n_iterations=(1, 5),\n size=[0, 0.2],\n squared=True)\n ],\n record=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='Normalize',\n mean=[103.53, 116.28, 123.675],\n std=[1.0, 1.0, 1.0],\n to_rgb=False),\n dict(type='ExtraAttrs', tag='unsup_student'),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels'],\n meta_keys=('filename', 'ori_shape', 'img_shape',\n 'img_norm_cfg', 'pad_shape',\n 'scale_factor', 'tag',\n 'transform_matrix'))\n ],\n unsup_teacher=[\n dict(\n type='Sequential',\n transforms=[\n dict(\n type='RandResize',\n img_scale=[(1333, 400), (1333, 1200)],\n multiscale_mode='range',\n keep_ratio=True),\n dict(type='RandFlip', flip_ratio=0.5)\n ],\n record=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='Normalize',\n mean=[103.53, 116.28, 123.675],\n std=[1.0, 1.0, 1.0],\n to_rgb=False),\n dict(type='ExtraAttrs', tag='unsup_teacher'),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels'],\n meta_keys=('filename', 'ori_shape', 'img_shape',\n 'img_norm_cfg', 'pad_shape',\n 'scale_factor', 'tag',\n 'transform_matrix'))\n ])\n ],\n filter_empty_gt=False)),\n val=dict(\n type='CocoDataset',\n ann_file='/content/data/labels/val/val.json',\n img_prefix='/content/data/images/val',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[103.53, 116.28, 123.675],\n std=[1.0, 1.0, 1.0],\n to_rgb=False),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='CocoDataset',\n ann_file='/content/data/labels/test/test.json',\n img_prefix='/content/data/images/test',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[103.53, 116.28, 123.675],\n std=[1.0, 1.0, 1.0],\n to_rgb=False),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n sampler=dict(\n train=dict(\n type='SemiBalanceSampler',\n sample_ratio=[1, 1],\n by_prob=True,\n epoch_length=200)))\nevaluation = dict(interval=4000, metric='bbox', type='SubModulesDistEvalHook')\noptimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=0.001,\n step=[3000, 4000])\nrunner = dict(type='IterBasedRunner', max_iters=5000)\ncheckpoint_config = dict(interval=1000, by_epoch=False, max_keep_ckpts=2)\nlog_config = dict(\n interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])\ncustom_hooks = [\n dict(type='NumClassCheckHook'),\n dict(type='WeightSummary'),\n dict(type='MeanTeacher', momentum=0.999, interval=1, warm_up=0)\n]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nmmdet_base = '../../thirdparty/mmdetection/configs/_base_'\nstrong_pipeline = [\n dict(\n type='Sequential',\n transforms=[\n dict(\n type='RandResize',\n img_scale=[(1333, 400), (1333, 1200)],\n multiscale_mode='range',\n keep_ratio=True),\n dict(type='RandFlip', flip_ratio=0.5),\n dict(\n type='ShuffledSequential',\n transforms=[\n dict(\n type='OneOf',\n transforms=[\n dict(type='Identity'),\n dict(type='AutoContrast'),\n dict(type='RandEqualize'),\n dict(type='RandSolarize'),\n dict(type='RandColor'),\n dict(type='RandContrast'),\n dict(type='RandBrightness'),\n dict(type='RandSharpness'),\n dict(type='RandPosterize')\n ]),\n dict(\n type='OneOf',\n transforms=[{\n 'type': 'RandTranslate',\n 'x': (-0.1, 0.1)\n }, {\n 'type': 'RandTranslate',\n 'y': (-0.1, 0.1)\n }, {\n 'type': 'RandRotate',\n 'angle': (-30, 30)\n },\n [{\n 'type': 'RandShear',\n 'x': (-30, 30)\n }, {\n 'type': 'RandShear',\n 'y': (-30, 30)\n }]])\n ]),\n dict(\n type='RandErase',\n n_iterations=(1, 5),\n size=[0, 0.2],\n squared=True)\n ],\n record=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='Normalize',\n mean=[103.53, 116.28, 123.675],\n std=[1.0, 1.0, 1.0],\n to_rgb=False),\n dict(type='ExtraAttrs', tag='unsup_student'),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels'],\n meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg',\n 'pad_shape', 'scale_factor', 'tag', 'transform_matrix'))\n]\nweak_pipeline = [\n dict(\n type='Sequential',\n transforms=[\n dict(\n type='RandResize',\n img_scale=[(1333, 400), (1333, 1200)],\n multiscale_mode='range',\n keep_ratio=True),\n dict(type='RandFlip', flip_ratio=0.5)\n ],\n record=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='Normalize',\n mean=[103.53, 116.28, 123.675],\n std=[1.0, 1.0, 1.0],\n to_rgb=False),\n dict(type='ExtraAttrs', tag='unsup_teacher'),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels'],\n meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg',\n 'pad_shape', 'scale_factor', 'tag', 'transform_matrix'))\n]\nunsup_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='PseudoSamples', with_bbox=True),\n dict(\n type='MultiBranch',\n unsup_student=[\n dict(\n type='Sequential',\n transforms=[\n dict(\n type='RandResize',\n img_scale=[(1333, 400), (1333, 1200)],\n multiscale_mode='range',\n keep_ratio=True),\n dict(type='RandFlip', flip_ratio=0.5),\n dict(\n type='ShuffledSequential',\n transforms=[\n dict(\n type='OneOf',\n transforms=[\n dict(type='Identity'),\n dict(type='AutoContrast'),\n dict(type='RandEqualize'),\n dict(type='RandSolarize'),\n dict(type='RandColor'),\n dict(type='RandContrast'),\n dict(type='RandBrightness'),\n dict(type='RandSharpness'),\n dict(type='RandPosterize')\n ]),\n dict(\n type='OneOf',\n transforms=[{\n 'type': 'RandTranslate',\n 'x': (-0.1, 0.1)\n }, {\n 'type': 'RandTranslate',\n 'y': (-0.1, 0.1)\n }, {\n 'type': 'RandRotate',\n 'angle': (-30, 30)\n },\n [{\n 'type': 'RandShear',\n 'x': (-30, 30)\n }, {\n 'type': 'RandShear',\n 'y': (-30, 30)\n }]])\n ]),\n dict(\n type='RandErase',\n n_iterations=(1, 5),\n size=[0, 0.2],\n squared=True)\n ],\n record=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='Normalize',\n mean=[103.53, 116.28, 123.675],\n std=[1.0, 1.0, 1.0],\n to_rgb=False),\n dict(type='ExtraAttrs', tag='unsup_student'),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels'],\n meta_keys=('filename', 'ori_shape', 'img_shape',\n 'img_norm_cfg', 'pad_shape', 'scale_factor', 'tag',\n 'transform_matrix'))\n ],\n unsup_teacher=[\n dict(\n type='Sequential',\n transforms=[\n dict(\n type='RandResize',\n img_scale=[(1333, 400), (1333, 1200)],\n multiscale_mode='range',\n keep_ratio=True),\n dict(type='RandFlip', flip_ratio=0.5)\n ],\n record=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='Normalize',\n mean=[103.53, 116.28, 123.675],\n std=[1.0, 1.0, 1.0],\n to_rgb=False),\n dict(type='ExtraAttrs', tag='unsup_teacher'),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels'],\n meta_keys=('filename', 'ori_shape', 'img_shape',\n 'img_norm_cfg', 'pad_shape', 'scale_factor', 'tag',\n 'transform_matrix'))\n ])\n]\nfp16 = dict(loss_scale='dynamic')\npercent = 10\nfond = 1\nwork_dir = './work_dirs/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k'\ncfg_name = 'soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k'\ngpu_ids = range(0, 1)\n", 'seed': None, 'exp_name': 'soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k.py'}
build sampler = SemiBalanceSampler
group = False
dist = False
end sampler_type = SemiBalanceSampler
Traceback (most recent call last):
File "train.py", line 199, in <module>
main()
File "train.py", line 194, in main
meta=meta,
File "/content/SoftTeacher/ssod/apis/train.py", line 81, in train_detector
for ds in dataset
File "/content/SoftTeacher/ssod/apis/train.py", line 81, in <listcomp>
for ds in dataset
File "/content/SoftTeacher/ssod/datasets/builder.py", line 74, in build_dataloader
if shuffle
File "/content/SoftTeacher/ssod/datasets/builder.py", line 45, in build_sampler
return build_from_cfg(cfg, SAMPLERS, default_args)
File "/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py", line 45, in build_from_cfg
f'{obj_type} is not in the {registry.name} registry')
KeyError: 'SemiBalanceSampler is not in the sampler registry'
Issue Analytics
- State:
- Created 2 years ago
- Comments:15
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unable to debug train.py on my custom dataset - Issues Antenna
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Did you read https://github.com/microsoft/SoftTeacher/issues/87#issuecomment-1074357271 carefully?
Solution:
KeyError: 'SemiBalanceSampler is not in the sampler registry'(duplicate of https://github.com/microsoft/SoftTeacher/issues/58)If you are seeing the error above, you are using an invalid sampler configuration in your config file which has not implemented for your launch mode. You need to override or change the existing config to fix this problem.
Create a new configuration using below content and use it in train command. (you might need to change the
_base_)But if you are trying to train on a single GPU, below command should work as mention in https://github.com/microsoft/SoftTeacher/issues/55#issuecomment-950300155 and you don’t need to turn off distributed mode.
bash tools/dist_train.sh configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k.py@MendelXu consider closing this thread.
Greetings!