unable to train on custom dataset

[luisfra19]

Hello, after installing all the recommended packages and successfully runnning inference on demo images with the original scripts, I face a training obstruction.

Old issue similar to #https://github.com/microsoft/SoftTeacher/issues/87#issuecomment-965242334

I train with bash tools/dist_train_partially.sh baseline 1 1 1

+ TYPE=baseline
+ FOLD=1
+ PERCENT=1
+ GPUS=1
+ PORT=29500
++ dirname tools/dist_train_partially.sh
+ PYTHONPATH=tools/..:
+ [[ baseline == \b\a\s\e\l\i\n\e ]]
++ dirname tools/dist_train_partially.sh
+ python -m torch.distributed.launch --nproc_per_node=1 --master_port=29500 tools/train.py configs/baseline/faster_rcnn_r50_caffe_fpn_coco_partial_180k.py --launcher pytorch --cfg-options fold=1 percent=1
/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/torch/distributed/launch.py:186: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See 
https://pytorch.org/docs/stable/distributed.html#launch-utility for 
further instructions

  FutureWarning,
/mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/thirdparty/mmdetection/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting.
  warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be '
2022-04-20 15:52:00,103 - mmdet.ssod - INFO - [<StreamHandler <stderr> (INFO)>, <FileHandler /mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/work_dirs/faster_rcnn_r50_caffe_fpn_coco_partial_180k/1/1/20220420_155159.log (INFO)>]
2022-04-20 15:52:00,104 - 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: None
GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
PyTorch: 1.10.2
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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: 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 -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.2, 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=ON, USE_NNPACK=ON, USE_OPENMP=ON, 

TorchVision: 0.11.3
OpenCV: 4.5.4-dev
MMCV: 1.4.7
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.2
MMDetection: 2.23.0+bef9a25
------------------------------------------------------------

2022-04-20 15:52:00,864 - mmdet.ssod - INFO - Distributed training: True
2022-04-20 15:52:01,603 - mmdet.ssod - INFO - Config:
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)))
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')
                ])
        ]),
    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=1,
    train=dict(
        type='CocoDataset',
        ann_file=
        'data/coco/annotations/semi_supervised/instances_train2017.1@1.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')
                        ])
                ]),
            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'))
        ]),
    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'])
                ])
        ]))
evaluation = dict(interval=4000, metric='bbox')
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=[120000, 160000])
runner = dict(type='IterBasedRunner', max_iters=180000)
checkpoint_config = dict(interval=4000, by_epoch=False, max_keep_ckpts=10)
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(
            type='WandbLoggerHook',
            init_kwargs=dict(
                project='pre_release',
                name='faster_rcnn_r50_caffe_fpn_coco_partial_180k',
                config=dict(
                    fold=1,
                    percent=1,
                    work_dirs='work_dirs/${cfg_name}/${percent}/${fold}',
                    total_step=180000)),
            by_epoch=False)
    ])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
mmdet_base = '../../thirdparty/mmdetection/configs/_base_'
fp16 = dict(loss_scale='dynamic')
fold = 1
percent = 1
work_dir = 'work_dirs/faster_rcnn_r50_caffe_fpn_coco_partial_180k/1/1'
cfg_name = 'faster_rcnn_r50_caffe_fpn_coco_partial_180k'
gpu_ids = range(0, 1)

2022-04-20 15:52:02,392 - mmdet.ssod - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'open-mmlab://detectron2/resnet50_caffe'}
2022-04-20 15:52:02,394 - mmcv - INFO - load model from: open-mmlab://detectron2/resnet50_caffe
2022-04-20 15:52:02,395 - mmcv - INFO - load checkpoint from openmmlab path: open-mmlab://detectron2/resnet50_caffe
2022-04-20 15:52:03,208 - mmcv - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: conv1.bias

2022-04-20 15:52:03,236 - mmdet.ssod - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-04-20 15:52:03,275 - mmdet.ssod - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}
2022-04-20 15:52:03,284 - 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', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
2022-04-20 15:52:10,631 - mmdet.ssod - INFO - Start running, host: lfgp@LAPTOP-VI0T98FT, work_dir: /mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/work_dirs/faster_rcnn_r50_caffe_fpn_coco_partial_180k/1/1
2022-04-20 15:52:10,633 - mmdet.ssod - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) StepLrUpdaterHook                  
(ABOVE_NORMAL) Fp16OptimizerHook                  
(NORMAL      ) CheckpointHook                     
(80          ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) WandbLoggerHook                    
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) NumClassCheckHook                  
(LOW         ) IterTimerHook                      
(80          ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) WandbLoggerHook                    
 -------------------- 
before_train_iter:
(VERY_HIGH   ) StepLrUpdaterHook                  
(LOW         ) IterTimerHook                      
(80          ) DistEvalHook                       
 -------------------- 
after_train_iter:
(ABOVE_NORMAL) Fp16OptimizerHook                  
(NORMAL      ) CheckpointHook                     
(LOW         ) IterTimerHook                      
(80          ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) WandbLoggerHook                    
 -------------------- 
after_train_epoch:
(NORMAL      ) CheckpointHook                     
(80          ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) WandbLoggerHook                    
 -------------------- 
before_val_epoch:
(NORMAL      ) NumClassCheckHook                  
(LOW         ) IterTimerHook                      
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) WandbLoggerHook                    
 -------------------- 
before_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_epoch:
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) WandbLoggerHook                    
 -------------------- 
after_run:
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) WandbLoggerHook                    
 -------------------- 
2022-04-20 15:52:10,635 - mmdet.ssod - INFO - workflow: [('train', 1)], max: 180000 iters
2022-04-20 15:52:10,638 - mmdet.ssod - INFO - Checkpoints will be saved to /mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/work_dirs/faster_rcnn_r50_caffe_fpn_coco_partial_180k/1/1 by HardDiskBackend.
wandb: Currently logged in as: lfgp (use `wandb login --relogin` to force relogin)
wandb: wandb version 0.12.14 is available!  To upgrade, please run:
wandb:  $ pip install wandb --upgrade
wandb: Tracking run with wandb version 0.10.31
wandb: Syncing run faster_rcnn_r50_caffe_fpn_coco_partial_180k
wandb: ⭐️ View project at https://wandb.ai/lfgp/pre_release
wandb: 🚀 View run at https://wandb.ai/lfgp/pre_release/runs/2c7vaz2t
wandb: Run data is saved locally in /mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/wandb/run-20220420_155211-2c7vaz2t
wandb: Run `wandb offline` to turn off syncing.

Traceback (most recent call last):
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 32, in __next__
    data = next(self.iter_loader)
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
    data = self._next_data()
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1176, in _next_data
    raise StopIteration
StopIteration

During handling of the above exception, another exception occurred:

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 206, in train_detector
    runner.run(data_loaders, cfg.workflow)
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 134, in run
    iter_runner(iter_loaders[i], **kwargs)
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 59, in train
    data_batch = next(data_loader)
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 39, in __next__
    data = next(self.iter_loader)
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
    data = self._next_data()
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1176, in _next_data
    raise StopIteration
StopIteration

wandb: Waiting for W&B process to finish, PID 1648
wandb: Program failed with code 1.  Press ctrl-c to abort syncing.
wandb:                                                                                
wandb: Find user logs for this run at: /mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/wandb/run-20220420_155211-2c7vaz2t/logs/debug.log
wandb: Find internal logs for this run at: /mnt/c/Users/Francisco Pereira/Desktop/IST/10º semestre/algoritmos/SoftTeacher/wandb/run-20220420_155211-2c7vaz2t/logs/debug-internal.log
wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: 
wandb: Synced faster_rcnn_r50_caffe_fpn_coco_partial_180k: https://wandb.ai/lfgp/pre_release/runs/2c7vaz2t
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 1480) of binary: /home/lfgp/anaconda3/envs/soft/bin/python
Traceback (most recent call last):
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/torch/distributed/launch.py", line 193, in <module>
    main()
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/torch/distributed/launch.py", line 189, in main
    launch(args)
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/torch/distributed/launch.py", line 174, in launch
    run(args)
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/torch/distributed/run.py", line 713, in run
    )(*cmd_args)
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/home/lfgp/anaconda3/envs/soft/lib/python3.6/site-packages/torch/distributed/launcher/api.py", line 261, in launch_agent
    failures=result.failures,
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
tools/train.py FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2022-04-20_15:52:43
  host      : LAPTOP-VI0T98FT.
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 1480)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================`

[farouk09]

Any updates ? i'm having the same problem using mmseg