Lightrun
question-mark
Stuck on an issue?

Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.

And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.

When I try my dataset on instance SCNet ,FileNotFoundError: [Errno 2] No such file or directory: 'data/coco/stuffthingmaps/train2017/JPEGImages/DJI_0252.png'

See original GitHub issue

Firstly, I have ran correctly on maskrcnn.And I found in scnet config train Collect ,there is a extra keys 'gt_semantic_seg' and specify the path seg_prefix='data/coco/stuffthingmaps/train2017/',.But my instance dataset not has the stuffthingmaps.

my config is that.

# 这个新的配置文件继承自一个原始配置文件，只需要突出必要的修改部分即可
_base_ = '../configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py'

_delete_=True
# 我们需要对头中的类别数量进行修改来匹配数据集的标注
model = dict(
    roi_head=dict(
#         bbox_head=[dict(num_classes=1)],        # 除了maskrcnn之外的网络，bbox_head和mask_head都是列表格式，maskrcnn是字典格式
         bbox_head=[
            dict(
                type='SCNetBBoxHead',
                num_shared_fcs=2,
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.1, 0.1, 0.2, 0.2]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='SCNetBBoxHead',
                num_shared_fcs=2,
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.05, 0.05, 0.1, 0.1]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='SCNetBBoxHead',
                num_shared_fcs=2,
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.033, 0.033, 0.067, 0.067]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
        ],
        
        mask_head=dict(num_classes=1),
        glbctx_head=dict(num_classes=1)))


runner = runner = dict(type='EpochBasedRunner', max_epochs=20)

# 修改数据集相关设置
dataset_type = 'CocoDataset'
classes = ('building',)

data = dict(
    train=dict(
        img_prefix='first_test_dataset/train',
        classes=classes,
        ann_file='first_test_dataset/train/annotations.json'),
    val=dict(
        img_prefix='first_test_dataset/val',
        classes=classes,
        ann_file='first_test_dataset/val/annotations.json'),
    test=dict(
        img_prefix='first_test_dataset/val',
        classes=classes,
        ann_file='first_test_dataset/val/annotations.json'))

# 我们可以使用预训练的 Mask R-CNN 来获取更好的性能
load_from = 'checkpoints/scnet/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth'

and the error info is that.

/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/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,
WARNING:torch.distributed.run:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
*****************************************
/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/utils/setup_env.py:43: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
  f'Setting MKL_NUM_THREADS environment variable for each process '
/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/utils/setup_env.py:43: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
  f'Setting MKL_NUM_THREADS environment variable for each process '
/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/utils/setup_env.py:43: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
  f'Setting MKL_NUM_THREADS environment variable for each process '
/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/utils/setup_env.py:43: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
  f'Setting MKL_NUM_THREADS environment variable for each process '
fatal: not a git repository (or any parent up to mount point /)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
fatal: not a git repository (or any parent up to mount point /)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
2022-04-09 20:10:50,153 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0]
CUDA available: True
GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti
CUDA_HOME: /usr/local/cuda-10.1
NVCC: Cuda compilation tools, release 10.1, V10.1.105
GCC: gcc (Ubuntu 8.4.0-1ubuntu1~18.04) 8.4.0
PyTorch: 1.11.0
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.3
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.2
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, 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-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=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

TorchVision: 0.12.0
OpenCV: 4.5.5
MMCV: 1.4.8
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.3
MMDetection: 2.23.0+
------------------------------------------------------------

fatal: not a git repository (or any parent up to mount point /)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
fatal: not a git repository (or any parent up to mount point /)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
2022-04-09 20:10:50,544 - mmdet - INFO - Distributed training: True
2022-04-09 20:10:50,916 - mmdet - INFO - Config:
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='SegRescale', scale_factor=0.125),
    dict(type='DefaultFormatBundle'),
    dict(
        type='Collect',
        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='CocoDataset',
        ann_file='first_test_dataset/train/annotations.json',
        img_prefix='first_test_dataset/train',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=True,
                with_seg=True),
            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='SegRescale', scale_factor=0.125),
            dict(type='DefaultFormatBundle'),
            dict(
                type='Collect',
                keys=[
                    'img', 'gt_bboxes', 'gt_labels', 'gt_masks',
                    'gt_semantic_seg'
                ])
        ],
        seg_prefix='data/coco/stuffthingmaps/train2017/',
        classes=('building', )),
    val=dict(
        type='CocoDataset',
        ann_file='first_test_dataset/val/annotations.json',
        img_prefix='first_test_dataset/val',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip', flip_ratio=0.5),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('building', )),
    test=dict(
        type='CocoDataset',
        ann_file='first_test_dataset/val/annotations.json',
        img_prefix='first_test_dataset/val',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip', flip_ratio=0.5),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('building', )))
evaluation = dict(metric=['bbox', 'segm'])
optimizer = dict(type='SGD', lr=0.02, 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=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'checkpoints/scnet/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth'
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
model = dict(
    type='SCNet',
    backbone=dict(
        type='ResNeXt',
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(
            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'),
        groups=64,
        base_width=4),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(
            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
    roi_head=dict(
        type='SCNetRoIHead',
        num_stages=3,
        stage_loss_weights=[1, 0.5, 0.25],
        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='SCNetBBoxHead',
                num_shared_fcs=2,
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                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=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='SCNetBBoxHead',
                num_shared_fcs=2,
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.05, 0.05, 0.1, 0.1]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='SCNetBBoxHead',
                num_shared_fcs=2,
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.033, 0.033, 0.067, 0.067]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
        ],
        mask_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        mask_head=dict(
            type='SCNetMaskHead',
            num_convs=12,
            in_channels=256,
            conv_out_channels=256,
            num_classes=1,
            conv_to_res=True,
            loss_mask=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
        semantic_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[8]),
        semantic_head=dict(
            type='SCNetSemanticHead',
            num_ins=5,
            fusion_level=1,
            num_convs=4,
            in_channels=256,
            conv_out_channels=256,
            num_classes=183,
            loss_seg=dict(
                type='CrossEntropyLoss', ignore_index=255, loss_weight=0.2),
            conv_to_res=True),
        glbctx_head=dict(
            type='GlobalContextHead',
            num_convs=4,
            in_channels=256,
            conv_out_channels=256,
            num_classes=1,
            loss_weight=3.0,
            conv_to_res=True),
        feat_relay_head=dict(
            type='FeatureRelayHead',
            in_channels=1024,
            out_conv_channels=256,
            roi_feat_size=7,
            scale_factor=2)),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=0,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=2000,
            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,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                mask_size=28,
                pos_weight=-1,
                debug=False),
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.6,
                    neg_iou_thr=0.6,
                    min_pos_iou=0.6,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                mask_size=28,
                pos_weight=-1,
                debug=False),
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.7,
                    neg_iou_thr=0.7,
                    min_pos_iou=0.7,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                mask_size=28,
                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=0.001,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100,
            mask_thr_binary=0.5)))
_delete_ = True
classes = ('building', )
work_dir = './work_dirs/scnet_x101_64x4d_fpn_20e_coco_building'
auto_resume = False
gpu_ids = range(0, 4)

2022-04-09 20:10:50,916 - mmdet - INFO - Set random seed to 0, deterministic: False
/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/models/losses/cross_entropy_loss.py:240: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.
  'Default ``avg_non_ignore`` is False, if you would like to '
/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/models/losses/cross_entropy_loss.py:240: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.
  'Default ``avg_non_ignore`` is False, if you would like to '
/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/models/losses/cross_entropy_loss.py:240: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.
  'Default ``avg_non_ignore`` is False, if you would like to '
/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/models/losses/cross_entropy_loss.py:240: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.
  'Default ``avg_non_ignore`` is False, if you would like to '
2022-04-09 20:10:52,330 - mmdet - INFO - initialize ResNeXt with init_cfg {'type': 'Pretrained', 'checkpoint': 'open-mmlab://resnext101_64x4d'}
2022-04-09 20:10:52,330 - mmcv - INFO - load model from: open-mmlab://resnext101_64x4d
2022-04-09 20:10:52,330 - mmcv - INFO - load checkpoint from openmmlab path: open-mmlab://resnext101_64x4d
2022-04-09 20:10:57,298 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-04-09 20:10:57,320 - mmdet - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}
2022-04-09 20:10:57,324 - mmdet - INFO - initialize SCNetBBoxHead 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'}]}]
2022-04-09 20:10:57,417 - mmdet - INFO - initialize SCNetBBoxHead 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'}]}]
2022-04-09 20:10:57,508 - mmdet - INFO - initialize SCNetBBoxHead 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'}]}]
2022-04-09 20:10:57,626 - mmdet - INFO - initialize SCNetSemanticHead with init_cfg {'type': 'Kaiming', 'override': {'name': 'conv_logits'}}
2022-04-09 20:10:57,633 - mmdet - INFO - initialize FeatureRelayHead with init_cfg {'type': 'Kaiming', 'layer': 'Linear'}
2022-04-09 20:10:57,707 - mmdet - INFO - initialize GlobalContextHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc'}}
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
fatal: not a git repository (or any parent up to mount point /)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
loading annotations into memory...
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
Done (t=0.01s)
creating index...
index created!
fatal: not a git repository (or any parent up to mount point /)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
fatal: not a git repository (or any parent up to mount point /)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
fatal: not a git repository (or any parent up to mount point /)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
loading annotations into memory...
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
Done (t=0.00s)
creating index...
index created!
2022-04-09 20:10:58,448 - mmdet - INFO - load checkpoint from local path: checkpoints/scnet/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth
2022-04-09 20:11:00,847 - mmdet - WARNING - The model and loaded state dict do not match exactly

size mismatch for roi_head.bbox_head.0.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([2, 1024]).
size mismatch for roi_head.bbox_head.0.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([2]).
size mismatch for roi_head.bbox_head.1.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([2, 1024]).
size mismatch for roi_head.bbox_head.1.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([2]).
size mismatch for roi_head.bbox_head.2.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([2, 1024]).
size mismatch for roi_head.bbox_head.2.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([2]).
size mismatch for roi_head.mask_head.conv_logits.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 256, 1, 1]).
size mismatch for roi_head.mask_head.conv_logits.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]).
size mismatch for roi_head.glbctx_head.fc.weight: copying a param with shape torch.Size([80, 256]) from checkpoint, the shape in current model is torch.Size([1, 256]).
size mismatch for roi_head.glbctx_head.fc.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]).
2022-04-09 20:11:00,864 - mmdet - INFO - Start running, host: qinhaobo@zkti, work_dir: /home/qinhaobo/proxyrecon/mmdetection-master/demo/work_dirs/scnet_x101_64x4d_fpn_20e_coco_building
2022-04-09 20:11:00,864 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) CheckpointHook                     
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) NumClassCheckHook                  
(NORMAL      ) DistSamplerSeedHook                
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_iter:
(VERY_HIGH   ) StepLrUpdaterHook                  
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
 -------------------- 
after_train_iter:
(ABOVE_NORMAL) OptimizerHook                      
(NORMAL      ) CheckpointHook                     
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_train_epoch:
(NORMAL      ) CheckpointHook                     
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) NumClassCheckHook                  
(NORMAL      ) DistSamplerSeedHook                
(LOW         ) IterTimerHook                      
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_epoch:
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_run:
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
2022-04-09 20:11:00,864 - mmdet - INFO - workflow: [('train', 1)], max: 20 epochs
2022-04-09 20:11:00,864 - mmdet - INFO - Checkpoints will be saved to /home/qinhaobo/proxyrecon/mmdetection-master/demo/work_dirs/scnet_x101_64x4d_fpn_20e_coco_building by HardDiskBackend.
Traceback (most recent call last):
  File "../tools/train.py", line 220, in <module>
    main()
  File "../tools/train.py", line 216, in main
    meta=meta)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/apis/train.py", line 208, in train_detector
    runner.run(data_loaders, cfg.workflow)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 47, in train
    for i, data_batch in enumerate(self.data_loader):
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 530, in __next__
    data = self._next_data()
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1224, in _next_data
    return self._process_data(data)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1250, in _process_data
    data.reraise()
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/_utils.py", line 457, in reraise
    raise exception
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/custom.py", line 218, in __getitem__
    data = self.prepare_train_img(idx)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/custom.py", line 241, in prepare_train_img
    return self.pipeline(results)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/compose.py", line 41, in __call__
    data = t(data)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 401, in __call__
    results = self._load_semantic_seg(results)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 375, in _load_semantic_seg
    img_bytes = self.file_client.get(filename)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/fileio/file_client.py", line 993, in get
    return self.client.get(filepath)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/fileio/file_client.py", line 518, in get
    with open(filepath, 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'data/coco/stuffthingmaps/train2017/JPEGImages/DJI_0252.png'

Traceback (most recent call last):
  File "../tools/train.py", line 220, in <module>
    main()
  File "../tools/train.py", line 216, in main
Traceback (most recent call last):
    meta=meta)
  File "../tools/train.py", line 220, in <module>
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/apis/train.py", line 208, in train_detector
    runner.run(data_loaders, cfg.workflow)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
    main()
  File "../tools/train.py", line 216, in main
    meta=meta)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/apis/train.py", line 208, in train_detector
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 47, in train
    runner.run(data_loaders, cfg.workflow)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
    for i, data_batch in enumerate(self.data_loader):
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 530, in __next__
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 47, in train
    for i, data_batch in enumerate(self.data_loader):
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 530, in __next__
    data = self._next_data()
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1224, in _next_data
    data = self._next_data()
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1224, in _next_data
    return self._process_data(data)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1250, in _process_data
    data.reraise()
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/_utils.py", line 457, in reraise
    return self._process_data(data)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1250, in _process_data
    raise exception
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/custom.py", line 218, in __getitem__
    data = self.prepare_train_img(idx)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/custom.py", line 241, in prepare_train_img
    return self.pipeline(results)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/compose.py", line 41, in __call__
    data = t(data)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 401, in __call__
    results = self._load_semantic_seg(results)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 375, in _load_semantic_seg
    img_bytes = self.file_client.get(filename)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/fileio/file_client.py", line 993, in get
    return self.client.get(filepath)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/fileio/file_client.py", line 518, in get
    with open(filepath, 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'data/coco/stuffthingmaps/train2017/JPEGImages/DJI_0728.png'

    data.reraise()
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/_utils.py", line 457, in reraise
    raise exception
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/custom.py", line 218, in __getitem__
    data = self.prepare_train_img(idx)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/custom.py", line 241, in prepare_train_img
    return self.pipeline(results)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/compose.py", line 41, in __call__
    data = t(data)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 401, in __call__
    results = self._load_semantic_seg(results)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 375, in _load_semantic_seg
    img_bytes = self.file_client.get(filename)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/fileio/file_client.py", line 993, in get
    return self.client.get(filepath)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/fileio/file_client.py", line 518, in get
    with open(filepath, 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'data/coco/stuffthingmaps/train2017/JPEGImages/DJI_0584.png'

Traceback (most recent call last):
  File "../tools/train.py", line 220, in <module>
    main()
  File "../tools/train.py", line 216, in main
    meta=meta)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/apis/train.py", line 208, in train_detector
    runner.run(data_loaders, cfg.workflow)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 47, in train
    for i, data_batch in enumerate(self.data_loader):
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 530, in __next__
    data = self._next_data()
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1224, in _next_data
    return self._process_data(data)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1250, in _process_data
    data.reraise()
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/_utils.py", line 457, in reraise
    raise exception
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/custom.py", line 218, in __getitem__
    data = self.prepare_train_img(idx)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/custom.py", line 241, in prepare_train_img
    return self.pipeline(results)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/compose.py", line 41, in __call__
    data = t(data)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 401, in __call__
    results = self._load_semantic_seg(results)
  File "/home/qinhaobo/proxyrecon/mmdetection-master/mmdet/datasets/pipelines/loading.py", line 375, in _load_semantic_seg
    img_bytes = self.file_client.get(filename)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/fileio/file_client.py", line 993, in get
    return self.client.get(filepath)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/fileio/file_client.py", line 518, in get
    with open(filepath, 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'data/coco/stuffthingmaps/train2017/JPEGImages/13x00029.png'

ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 661922) of binary: /home/qinhaobo/anaconda3/envs/open-mmlab/bin/python
Traceback (most recent call last):
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/distributed/launch.py", line 193, in <module>
    main()
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/distributed/launch.py", line 189, in main
    launch(args)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/distributed/launch.py", line 174, in launch
    run(args)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/distributed/run.py", line 718, in run
    )(*cmd_args)
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/home/qinhaobo/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 247, in launch_agent
    failures=result.failures,
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
../tools/train.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2022-04-09_20:11:09
  host      : zkti
  rank      : 1 (local_rank: 1)
  exitcode  : 1 (pid: 661923)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
  time      : 2022-04-09_20:11:09
  host      : zkti
  rank      : 2 (local_rank: 2)
  exitcode  : 1 (pid: 661924)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[3]:
  time      : 2022-04-09_20:11:09
  host      : zkti
  rank      : 3 (local_rank: 3)
  exitcode  : 1 (pid: 661925)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2022-04-09_20:11:09
  host      : zkti
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 661922)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================

Issue Analytics

  • State:closed
  • Created a year ago
  • Comments:9

github_iconTop GitHub Comments

1reaction
TalonX1commented, May 4, 2022

How did you remove ‘gt_semantic_seg’ in keys?

https://github.com/open-mmlab/mmdetection/issues/7683#issuecomment-1094065710

You should rewrite the config like this,the link had offered the correct config which can use directly.

0reactions
David-Biggscommented, May 6, 2022

Thanks for your response @TalonX1 . I am currently using a config for DetectoRS. I have managed to set both semantic_head and semantic_roi_extractor = None. The screenshot below shows how I did that (not sure if that is the correct way) image However, I am unsure as to the further changes which need to be made so I can use DetectoRS for instance segmentation. My costume dataset only has bounding box and instance segmentation annotations. Basically, my questions are:

  1. What do I need to change/remove so I can just do instance segmentation and object detection (no panoptic segmentation)
  2. Is it necessary to remove ‘gt_semantic_seg’ for keys? If so, then how should I do that?

Many thanks

Have you ever set ‘with_seg’ to False? The keywords are train_pipeline and data/train/pipeline in config file. I set semantic_roi_extractor=None, semantic_head=None, delete the keywords seg_prefix, set two with_seg=False, like TalonX1 comment, and then it’s work.

Thanks for the response @wjw6692353.

Yes, I set both ‘with_seg’ = False. I also set semantic_roi_extractor=None, semantic_head=None However, I don’t know how to delete the keywords ‘seg_prefix’. At the moment I set ‘seg_prefix’ = None. My code to do this is shown: image

However: -> ‘gt_semantic_seg’ is still in keys… -> And I get the following error:

image

Thanks again! Much appreciated

Read more comments on GitHub >

github_iconTop Results From Across the Web

FileNotFoundError: [Errno 2] No such file or directory [duplicate]
I am trying to open a CSV file but for some reason python cannot locate it. Here is my code (it's just a...
Read more >
How to fix FileNotFoundError Errno 2 no such file or directory
There might be times when your files won't not exist in the current directory. These are called relative paths and are the most...
Read more >
Python FileNotFoundError: [Errno 2] No such file or directory ...
This error tells you that you are trying to access a file or folder that does not exist. To fix this error, check...
Read more >
FileNotFoundError: [Errno 2] No such file or directory
No such file or directory " is telling you that there is no file of that name in the working directory. So, try...
Read more >
[Errno 2] No such file or directory: '/dbfs/FileStore/tables ...
i am trying to read csv file using databricks, i am getting error like ......FileNotFoundError: [Errno 2] No such file or directory: ...
Read more >

github_iconTop Related Medium Post

No results found

github_iconTop Related StackOverflow Question

No results found

github_iconTroubleshoot Live Code

Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start Free

github_iconTop Related Reddit Thread

No results found

github_iconTop Related Hackernoon Post

No results found

github_iconTop Related Tweet

No results found

github_iconTop Related Dev.to Post

No results found

github_iconTop Related Hashnode Post

No results found
Previous page

Got an error when I was trying to train swin-t on my customized data:list index out of range

Next page

💡 Dedicated WandbLogger for MMDetection (Weights and Biases Integration)