Closed luisfra19 closed 3 years ago
Do you need more information or should I present it in a different way?
Could you update the full log here?
Hello!
The full log for the COCO dataset with
bash tools/dist_train.sh configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k.py 1
is
The module torch.distributed.launch is deprecated and going to be removed in future.Migrate to torch.distributed.run
WARNING:torch.distributed.run:--use_env is deprecated and will be removed in future releases.
Please read local_rank from `os.environ('LOCAL_RANK')` instead.
INFO:torch.distributed.launcher.api:Starting elastic_operator with launch configs:
entrypoint : tools/train.py
min_nodes : 1
max_nodes : 1
nproc_per_node : 1
run_id : none
rdzv_backend : static
rdzv_endpoint : 127.0.0.1:29500
rdzv_configs : {'rank': 0, 'timeout': 900}
max_restarts : 3
monitor_interval : 5
log_dir : None
metrics_cfg : {}
INFO:torch.distributed.elastic.agent.server.local_elastic_agent:log directory set to: /tmp/torchelastic_605fbb0c/none_zf1_bgu7
INFO:torch.distributed.elastic.agent.server.api:[default] starting workers for entrypoint: python
INFO:torch.distributed.elastic.agent.server.api:[default] Rendezvous'ing worker group
/home/luisfra/anaconda3/envs/soft_teacher/lib/python3.6/site-packages/torch/distributed/elastic/utils/store.py:53: FutureWarning: This is an experimental API and will be changed in future.
"This is an experimental API and will be changed in future.", FutureWarning
INFO:torch.distributed.elastic.agent.server.api:[default] Rendezvous complete for workers. Result:
restart_count=0
master_addr=127.0.0.1
master_port=29500
group_rank=0
group_world_size=1
local_ranks=[0]
role_ranks=[0]
global_ranks=[0]
role_world_sizes=[1]
global_world_sizes=[1]
INFO:torch.distributed.elastic.agent.server.api:[default] Starting worker group
INFO:torch.distributed.elastic.multiprocessing:Setting worker0 reply file to: /tmp/torchelastic_605fbb0c/none_zf1_bgu7/attempt_0/0/error.json
2021-10-08 14:50:40,608 - mmdet.ssod - INFO - [<StreamHandler <stderr> (INFO)>, <FileHandler /mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/work_dirs/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k/20211008_145040.log (INFO)>]
2021-10-08 14:50:40,609 - mmdet.ssod - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.6.13 |Anaconda, Inc.| (default, Jun 4 2021, 14:25:59) [GCC 7.5.0]
CUDA available: True
GPU 0: NVIDIA GeForce GTX 1650 with Max-Q Design
CUDA_HOME: /usr/local/cuda-10.2
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
PyTorch: 1.9.0
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 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 10.2
- 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_37,code=compute_37
- CuDNN 7.6.5
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, 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
OpenCV: 4.5.3
MMCV: 1.3.9
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.2
MMDetection: 2.17.0+aacbef2
------------------------------------------------------------
2021-10-08 14:50:45,014 - mmdet.ssod - INFO - Distributed training: True
2021-10-08 14:50:49,217 - 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=0.01,
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 = 'data/coco/'
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=8,
workers_per_gpu=8,
train=dict(
type='SemiDataset',
sup=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_train2017.json',
img_prefix='data/coco/train2017/',
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='data/coco/annotations/instances_unlabeled2017.json',
img_prefix='data/coco/unlabeled2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='PseudoSamples', with_bbox=True),
dict(
type='MultiBranch',
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),
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_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)
],
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='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
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='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
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=7330)))
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=[480000, 640000])
runner = dict(type='IterBasedRunner', max_iters=720000)
checkpoint_config = dict(interval=4000, by_epoch=False, max_keep_ckpts=20)
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
dict(
type='WandbLoggerHook',
init_kwargs=dict(
project='pre_release',
name='soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k',
config=dict(
work_dirs=
'./work_dirs/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k',
total_step=720000)),
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_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),
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_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)
],
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')
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)
/mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/thirdparty/mmdetection/mmdet/core/anchor/builder.py:17: UserWarning: ``build_anchor_generator`` would be deprecated soon, please use ``build_prior_generator``
'``build_anchor_generator`` would be deprecated soon, please use '
2021-10-08 14:50:50,422 - mmcv - INFO - load model from: open-mmlab://detectron2/resnet50_caffe
2021-10-08 14:50:50,422 - mmcv - INFO - Use load_from_openmmlab loader
2021-10-08 14:50:50,803 - mmcv - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: conv1.bias
2021-10-08 14:50:51,177 - mmcv - INFO - load model from: open-mmlab://detectron2/resnet50_caffe
2021-10-08 14:50:51,177 - mmcv - INFO - Use load_from_openmmlab loader
2021-10-08 14:50:51,254 - mmcv - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: conv1.bias
loading annotations into memory...
Done (t=23.36s)
creating index...
index created!
loading annotations into memory...
Done (t=0.67s)
creating index...
index created!
Traceback (most recent call last):
File "tools/train.py", line 198, in <module>
main()
File "tools/train.py", line 193, in main
meta=meta,
File "/mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/ssod/apis/train.py", line 93, in train_detector
find_unused_parameters=find_unused_parameters,
File "/home/luisfra/anaconda3/envs/soft_teacher/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 496, in __init__
dist._verify_model_across_ranks(self.process_group, parameters)
RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1623448233824/work/torch/lib/c10d/ProcessGroupNCCL.cpp:911, unhandled system error, NCCL version 2.7.8
ncclSystemError: System call (socket, malloc, munmap, etc) failed.
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 574) of binary: /home/luisfra/anaconda3/envs/soft_teacher/bin/python
ERROR:torch.distributed.elastic.agent.server.local_elastic_agent:[default] Worker group failed
INFO:torch.distributed.elastic.agent.server.api:[default] Worker group FAILED. 3/3 attempts left; will restart worker group
INFO:torch.distributed.elastic.agent.server.api:[default] Stopping worker group
INFO:torch.distributed.elastic.agent.server.api:[default] Rendezvous'ing worker group
INFO:torch.distributed.elastic.agent.server.api:[default] Rendezvous complete for workers. Result:
restart_count=1
master_addr=127.0.0.1
master_port=29500
group_rank=0
group_world_size=1
local_ranks=[0]
role_ranks=[0]
global_ranks=[0]
role_world_sizes=[1]
global_world_sizes=[1]
INFO:torch.distributed.elastic.agent.server.api:[default] Starting worker group
INFO:torch.distributed.elastic.multiprocessing:Setting worker0 reply file to: /tmp/torchelastic_605fbb0c/none_zf1_bgu7/attempt_1/0/error.json
Could you run it with NCCL_DEBUG=INFO bash tools/dist_train.sh configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k.py 1
and post the log here?
The log seems similar to the one before:
The module torch.distributed.launch is deprecated and going to be removed in future.Migrate to torch.distributed.run
WARNING:torch.distributed.run:--use_env is deprecated and will be removed in future releases.
Please read local_rank from `os.environ('LOCAL_RANK')` instead.
INFO:torch.distributed.launcher.api:Starting elastic_operator with launch configs:
entrypoint : tools/train.py
min_nodes : 1
max_nodes : 1
nproc_per_node : 1
run_id : none
rdzv_backend : static
rdzv_endpoint : 127.0.0.1:29500
rdzv_configs : {'rank': 0, 'timeout': 900}
max_restarts : 3
monitor_interval : 5
log_dir : None
metrics_cfg : {}
INFO:torch.distributed.elastic.agent.server.local_elastic_agent:log directory set to: /tmp/torchelastic__46hns9u/none_h7wx1pxb
INFO:torch.distributed.elastic.agent.server.api:[default] starting workers for entrypoint: python
INFO:torch.distributed.elastic.agent.server.api:[default] Rendezvous'ing worker group
/home/luisfra/anaconda3/envs/soft_teacher/lib/python3.6/site-packages/torch/distributed/elastic/utils/store.py:53: FutureWarning: This is an experimental API and will be changed in future.
"This is an experimental API and will be changed in future.", FutureWarning
INFO:torch.distributed.elastic.agent.server.api:[default] Rendezvous complete for workers. Result:
restart_count=0
master_addr=127.0.0.1
master_port=29500
group_rank=0
group_world_size=1
local_ranks=[0]
role_ranks=[0]
global_ranks=[0]
role_world_sizes=[1]
global_world_sizes=[1]
INFO:torch.distributed.elastic.agent.server.api:[default] Starting worker group
INFO:torch.distributed.elastic.multiprocessing:Setting worker0 reply file to: /tmp/torchelastic__46hns9u/none_h7wx1pxb/attempt_0/0/error.json
2021-10-08 16:16:34,770 - mmdet.ssod - INFO - [<StreamHandler <stderr> (INFO)>, <FileHandler /mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/work_dirs/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k/20211008_161634.log (INFO)>]
2021-10-08 16:16:34,771 - mmdet.ssod - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.6.13 |Anaconda, Inc.| (default, Jun 4 2021, 14:25:59) [GCC 7.5.0]
CUDA available: True
GPU 0: NVIDIA GeForce GTX 1650 with Max-Q Design
CUDA_HOME: /usr/local/cuda-10.2
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
PyTorch: 1.9.0
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 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 10.2
- 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_37,code=compute_37
- CuDNN 7.6.5
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, 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
OpenCV: 4.5.3
MMCV: 1.3.9
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.2
MMDetection: 2.17.0+aacbef2
------------------------------------------------------------
2021-10-08 16:16:38,413 - mmdet.ssod - INFO - Distributed training: True
2021-10-08 16:16:42,556 - 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=0.01,
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 = 'data/coco/'
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=8,
workers_per_gpu=8,
train=dict(
type='SemiDataset',
sup=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_train2017.json',
img_prefix='data/coco/train2017/',
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='data/coco/annotations/instances_unlabeled2017.json',
img_prefix='data/coco/unlabeled2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='PseudoSamples', with_bbox=True),
dict(
type='MultiBranch',
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),
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_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)
],
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='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
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='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
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=7330)))
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=[480000, 640000])
runner = dict(type='IterBasedRunner', max_iters=720000)
checkpoint_config = dict(interval=4000, by_epoch=False, max_keep_ckpts=20)
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
dict(
type='WandbLoggerHook',
init_kwargs=dict(
project='pre_release',
name='soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k',
config=dict(
work_dirs=
'./work_dirs/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k',
total_step=720000)),
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_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),
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_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)
],
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')
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)
/mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/thirdparty/mmdetection/mmdet/core/anchor/builder.py:17: UserWarning: ``build_anchor_generator`` would be deprecated soon, please use ``build_prior_generator``
'``build_anchor_generator`` would be deprecated soon, please use '
2021-10-08 16:16:45,821 - mmcv - INFO - load model from: open-mmlab://detectron2/resnet50_caffe
2021-10-08 16:16:45,821 - mmcv - INFO - Use load_from_openmmlab loader
2021-10-08 16:16:46,404 - mmcv - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: conv1.bias
2021-10-08 16:16:46,638 - mmcv - INFO - load model from: open-mmlab://detectron2/resnet50_caffe
2021-10-08 16:16:46,638 - mmcv - INFO - Use load_from_openmmlab loader
2021-10-08 16:16:46,725 - mmcv - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: conv1.bias
loading annotations into memory...
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -9) local_rank: 0 (pid: 2259) of binary: /home/luisfra/anaconda3/envs/soft_teacher/bin/python
ERROR:torch.distributed.elastic.agent.server.local_elastic_agent:[default] Worker group failed
INFO:torch.distributed.elastic.agent.server.api:[default] Worker group FAILED. 3/3 attempts left; will restart worker group
INFO:torch.distributed.elastic.agent.server.api:[default] Stopping worker group
INFO:torch.distributed.elastic.agent.server.api:[default] Rendezvous'ing worker group
INFO:torch.distributed.elastic.agent.server.api:[default] Rendezvous complete for workers. Result:
restart_count=1
master_addr=127.0.0.1
master_port=29500
group_rank=0
group_world_size=1
local_ranks=[0]
role_ranks=[0]
global_ranks=[0]
role_world_sizes=[1]
global_world_sizes=[1]
INFO:torch.distributed.elastic.agent.server.api:[default] Starting worker group
INFO:torch.distributed.elastic.multiprocessing:Setting worker0 reply file to: /tmp/torchelastic__46hns9u/none_h7wx1pxb/attempt_1/0/error.json
I should also add that I'm working with WSL.
Should it relate to this issue?https://github.com/NVIDIA/nccl/issues/442#issuecomment-761064724
Is it possible to train only with CPU?
It will be very slow, I think. Maybe you can use docker to run the job.
Error in full training:
tools/train.py FAILED
Root Cause: [0]: time: 2021-10-04_17:02:18 rank: 0 (local_rank: 0) exitcode: 1 (pid: 921) error_file: <N/A> msg: "Process failed with exitcode 1"
Other Failures: