*****************************************
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. 
*****************************************
2020-10-29 21:24:04,114 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.5 (default, Sep  4 2020, 07:30:14) [GCC 7.3.0]
CUDA available: True
GPU 0: P106-100
GPU 1: GeForce GTX 1060 6GB
CUDA_HOME: /usr
NVCC: Build cuda_11.0_bu.TC445_37.28845127_0
GCC: gcc (Ubuntu 10.2.0-13ubuntu1) 10.2.0
PyTorch: 1.7.0
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 v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
  - 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=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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: 0.8.1
OpenCV: 4.4.0
MMCV: 1.1.6
MMCV Compiler: GCC 10.2
MMCV CUDA Compiler: 11.0
MMDetection: 2.5.0+ac20ffe
------------------------------------------------------------

2020-10-29 21:24:04,456 - mmdet - INFO - Distributed training: True
2020-10-29 21:24:04,794 - mmdet - INFO - Config:
model = dict(
    type='FasterRCNN',
    pretrained='torchvision://resnet50',
    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=True),
        norm_eval=True,
        style='pytorch'),
    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=2,
            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_across_levels=False,
        nms_pre=2000,
        nms_post=1000,
        max_num=1000,
        nms_thr=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_across_levels=False,
        nms_pre=1000,
        nms_post=1000,
        max_num=1000,
        nms_thr=0.7,
        min_bbox_size=0),
    rcnn=dict(
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.5),
        max_per_img=100))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
    mean=[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),
    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='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
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=[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='data/coco/annotations/instances_train2017.json',
        img_prefix='data/coco/train2017/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=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='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ]),
    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=[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'])
                ])
        ]),
    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=[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'])
                ])
        ]))
evaluation = dict(interval=1, metric='bbox')
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=[8, 11])
total_epochs = 12
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
work_dir = './work_dirs/faster_rcnn_r50_fpn_1x_coco'
gpu_ids = range(0, 1)

2020-10-29 21:24:05,218 - mmdet - INFO - load model from: torchvision://resnet50
2020-10-29 21:24:05,519 - mmdet - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: fc.weight, fc.bias

loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
loading annotations into memory...
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
Done (t=0.00s)
creating index...
index created!
2020-10-29 21:24:06,086 - mmdet - INFO - Start running, host: lizhiyuan@lizhiyuan-Server, work_dir: /mnt/Data/workspace/mmdetection/work_dirs/faster_rcnn_r50_fpn_1x_coco
2020-10-29 21:24:06,086 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
2020-10-29 21:24:49,980 - mmdet - INFO - Epoch [1][50/146]	lr: 1.978e-03, eta: 0:24:49, time: 0.875, data_time: 0.125, memory: 3324, loss_rpn_cls: 0.3634, loss_rpn_bbox: 0.0203, loss_cls: 0.2062, acc: 95.9912, loss_bbox: 0.0038, loss: 0.5936
2020-10-29 21:25:28,628 - mmdet - INFO - Epoch [1][100/146]	lr: 3.976e-03, eta: 0:22:41, time: 0.773, data_time: 0.008, memory: 3325, loss_rpn_cls: 0.1206, loss_rpn_bbox: 0.0171, loss_cls: 0.0500, acc: 99.2227, loss_bbox: 0.0144, loss: 0.2021
2020-10-29 21:26:04,092 - mmdet - INFO - Saving checkpoint at 1 epochs
[                                                  ] 0/80, elapsed: 0s, ETA:Traceback (most recent call last):
  File "./tools/train.py", line 178, in <module>
    main()
  File "./tools/train.py", line 167, in main
    train_detector(
  File "/mnt/Data/workspace/mmdetection/mmdet/apis/train.py", line 150, in train_detector
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 125, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 54, in train
    self.call_hook('after_train_epoch')
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/mmcv/runner/base_runner.py", line 307, in call_hook
    getattr(hook, fn_name)(self)
  File "/mnt/Data/workspace/mmdetection/mmdet/core/evaluation/eval_hooks.py", line 125, in after_train_epoch
    results = multi_gpu_test(
  File "/mnt/Data/workspace/mmdetection/mmdet/apis/test.py", line 97, in multi_gpu_test
    result = model(return_loss=False, rescale=True, **data)
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 606, in forward
    if self.reducer._rebuild_buckets():
RuntimeError: replicas_[0].size() == rebuilt_param_indices_.size() INTERNAL ASSERT FAILED at "/opt/conda/conda-bld/pytorch_1603729062494/work/torch/csrc/distributed/c10d/reducer.cpp":1326, please report a bug to PyTorch. rebuilt parameter indices size is not same as original model parameters size.156 versus 22776
Traceback (most recent call last):
  File "./tools/train.py", line 178, in <module>
    main()
  File "./tools/train.py", line 167, in main
    train_detector(
  File "/mnt/Data/workspace/mmdetection/mmdet/apis/train.py", line 150, in train_detector
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 125, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 54, in train
    self.call_hook('after_train_epoch')
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/mmcv/runner/base_runner.py", line 307, in call_hook
    getattr(hook, fn_name)(self)
  File "/mnt/Data/workspace/mmdetection/mmdet/core/evaluation/eval_hooks.py", line 125, in after_train_epoch
    results = multi_gpu_test(
  File "/mnt/Data/workspace/mmdetection/mmdet/apis/test.py", line 97, in multi_gpu_test
    result = model(return_loss=False, rescale=True, **data)
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 606, in forward
    if self.reducer._rebuild_buckets():
RuntimeError: replicas_[0].size() == rebuilt_param_indices_.size() INTERNAL ASSERT FAILED at "/opt/conda/conda-bld/pytorch_1603729062494/work/torch/csrc/distributed/c10d/reducer.cpp":1326, please report a bug to PyTorch. rebuilt parameter indices size is not same as original model parameters size.156 versus 22776
Traceback (most recent call last):
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/torch/distributed/launch.py", line 260, in <module>
    main()
  File "/home/lizhiyuan/anaconda3/envs/mmcv/lib/python3.8/site-packages/torch/distributed/launch.py", line 255, in main
    raise subprocess.CalledProcessError(returncode=process.returncode,
subprocess.CalledProcessError: Command '['/home/lizhiyuan/anaconda3/envs/mmcv/bin/python', '-u', './tools/train.py', '--local_rank=1', 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py', '--launcher', 'pytorch']' returned non-zero exit status 1.

wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py