microsoft / SoftTeacher

Semi-Supervised Learning, Object Detection, ICCV2021
MIT License
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Error in training #42

Closed luisfra19 closed 3 years ago

luisfra19 commented 3 years ago

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:

I am using only one GPU, get an error in full training with my own data converted to COCO. Firstly, I segmented the data with "bash tools/dataset/prepare_coco_data.sh conduct", then trained with "bash tools/dist_train.sh configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k.py 1 " I also trained as the readme file with the COCO data, and still obtain errors, in full or semi training. It gets stuck in: INFO:torch.distributed.elastic.agent.server.api:[default] Starting worker group INFO:torch.distributed.elastic.multiprocessing:Setting worker0 reply file to: /tmp/torchelastic_mhoa0vu3/none_y1enyj09/attempt_0/0/error.json
luisfra19 commented 3 years ago

Do you need more information or should I present it in a different way?

MendelXu commented 3 years ago

Could you update the full log here?

luisfra19 commented 3 years ago

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
MendelXu commented 3 years ago

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?

luisfra19 commented 3 years ago

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
luisfra19 commented 3 years ago

I should also add that I'm working with WSL.

MendelXu commented 3 years ago

Should it relate to this issue?https://github.com/NVIDIA/nccl/issues/442#issuecomment-761064724

luisfra19 commented 3 years ago

Is it possible to train only with CPU?

MendelXu commented 3 years ago

It will be very slow, I think. Maybe you can use docker to run the job.