The model and loaded state dict do not match exactly

你好，请问运行CUDA_VISIBLE_DEVICES="0" python tools/trainval.py configs/trainval/retinanet/retinanet.py时出现以下信息之后就一直不动了，是什么原因，找了半天没找出原因
`
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
2020-12-19 19:12:36,718 - vedadet - INFO - Loading weights from torchvision://resnet50
2020-12-19 19:12:36,919 - vedadet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: backbone.fc.weight, backbone.fc.bias
missing keys in source state_dict: neck.lateral_convs.0.conv.weight, neck.lateral_convs.0.conv.bias, neck.lateral_convs.1.conv.weight, neck.lateral_convs.1.conv.bias, neck.lateral_convs.2.conv.weight, neck.lateral_convs.2.conv.bias, neck.fpn_convs.0.conv.weight, neck.fpn_convs.0.conv.bias, neck.fpn_convs.1.conv.weight, neck.fpn_convs.1.conv.bias, neck.fpn_convs.2.conv.weight, neck.fpn_convs.2.conv.bias, neck.fpn_convs.3.conv.weight, neck.fpn_convs.3.conv.bias, neck.fpn_convs.4.conv.weight, neck.fpn_convs.4.conv.bias, bbox_head.cls_convs.0.conv.weight, bbox_head.cls_convs.0.conv.bias, bbox_head.cls_convs.1.conv.weight, bbox_head.cls_convs.1.conv.bias, bbox_head.cls_convs.2.conv.weight, bbox_head.cls_convs.2.conv.bias, bbox_head.cls_convs.3.conv.weight, bbox_head.cls_convs.3.conv.bias, bbox_head.reg_convs.0.conv.weight, bbox_head.reg_convs.0.conv.bias, bbox_head.reg_convs.1.conv.weight, bbox_head.reg_convs.1.conv.bias, bbox_head.reg_convs.2.conv.weight, bbox_head.reg_convs.2.conv.bias, bbox_head.reg_convs.3.conv.weight, bbox_head.reg_convs.3.conv.bias, bbox_head.retina_cls.weight, bbox_head.retina_cls.bias, bbox_head.retina_reg.weight, bbox_head.retina_reg.bias
2020-12-19 19:12:37,126 - vedadet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: backbone.fc.weight, backbone.fc.bias
missing keys in source state_dict: neck.lateral_convs.0.conv.weight, neck.lateral_convs.0.conv.bias, neck.lateral_convs.1.conv.weight, neck.lateral_convs.1.conv.bias, neck.lateral_convs.2.conv.weight, neck.lateral_convs.2.conv.bias, neck.fpn_convs.0.conv.weight, neck.fpn_convs.0.conv.bias, neck.fpn_convs.1.conv.weight, neck.fpn_convs.1.conv.bias, neck.fpn_convs.2.conv.weight, neck.fpn_convs.2.conv.bias, neck.fpn_convs.3.conv.weight, neck.fpn_convs.3.conv.bias, neck.fpn_convs.4.conv.weight, neck.fpn_convs.4.conv.bias, bbox_head.cls_convs.0.conv.weight, bbox_head.cls_convs.0.conv.bias, bbox_head.cls_convs.1.conv.weight, bbox_head.cls_convs.1.conv.bias, bbox_head.cls_convs.2.conv.weight, bbox_head.cls_convs.2.conv.bias, bbox_head.cls_convs.3.conv.weight, bbox_head.cls_convs.3.conv.bias, bbox_head.reg_convs.0.conv.weight, bbox_head.reg_convs.0.conv.bias, bbox_head.reg_convs.1.conv.weight, bbox_head.reg_convs.1.conv.bias, bbox_head.reg_convs.2.conv.weight, bbox_head.reg_convs.2.conv.bias, bbox_head.reg_convs.3.conv.weight, bbox_head.reg_convs.3.conv.bias, bbox_head.retina_cls.weight, bbox_head.retina_cls.bias, bbox_head.retina_reg.weight, bbox_head.retina_reg.bias
`

还有这个问题
CUDA_VISIBLE_DEVICES="0" python tools/trainval.py configs/trainval/tinaface/tinaface.py  2020-12-19 22:32:14,872 - vedadet - WARNING - EvalHook is not in modes ['train'] 2020-12-19 22:32:14,873 - vedadet - INFO - Loading weights from torchvision://resnet50 2020-12-19 22:32:15,102 - vedadet - WARNING - The model and loaded state dict do not match exactly

还有这个问题
CUDA_VISIBLE_DEVICES="0" python tools/trainval.py configs/trainval/tinaface/tinaface.py
2020-12-19 22:32:14,872 - vedadet - WARNING - EvalHook is not in modes ['train'] 2020-12-19 22:32:14,873 - vedadet - INFO - Loading weights from torchvision://resnet50 2020-12-19 22:32:15,102 - vedadet - WARNING - The model and loaded state dict do not match exactly

@juliusyang97 This is a warning means that model only load backbone weight and others are not loaded.

@juliusyang97 This is a warning means that model only load backbone weight and others are not loaded.

您好，那这个运行时出现missing keys in source state_dict: neck.lateral_convs.0.conv.weight......一长串之后就一直不动，没反应，怎么回事啊，希望能得到您的解答

Hi @juliusyang97,
In order to speed up the training process, the default setting of logger's interval is 100 that may cause a long wait for printing the info. So you can modify this line 'dict(typename='LoggerHook', interval=100)' of hooks in config, e.g 'dict(typename='LoggerHook', interval=1)'

In order to speed up the training process, the default setting of logger's interval is 100 that may cause a long wait for printing the info. So you can modify this line 'dict(typename='LoggerHook', interval=100)' of hooks in config, e.g 'dict(typename='LoggerHook', interval=1)'

好的好的，
谢谢我试一试

@juliusyang97, if it is still stuck, please provide you config.

您好，我现在还有这个问题

Hi @juliusyang97,
In order to speed up the training process, the default setting of logger's interval is 100 that may cause a long wait for printing the info. So you can modify this line 'dict(typename='LoggerHook', interval=100)' of hooks in config, e.g 'dict(typename='LoggerHook', interval=1)'

您好，我现在还是有这个问题
`CUDA_VISIBLE_DEVICES="0" python tools/trainval.py configs/trainval/tinaface/tinaface.py
2020-12-29 18:09:20,558 - vedadet - WARNING - EvalHook is not in modes ['train']
2020-12-29 18:09:20,559 - vedadet - INFO - Loading weights from torchvision://resnet50
2020-12-29 18:09:20,843 - vedadet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.weight, backbone.bn1.bias, backbone.fc.weight, backbone.fc.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn3.running_mean, backbone.layer2.0.bn3.running_var, backbone.layer2.0.bn3.weight, backbone.layer2.0.bn3.bias, backbone.layer2.0.downsample.1.running_mean, backbone.layer2.0.downsample.1.running_var, backbone.layer2.1.bn1.running_mean, backbone.layer2.1.bn1.running_var, backbone.layer2.1.bn1.weight, backbone.layer2.1.bn1.bias, backbone.layer2.1.bn2.running_mean, backbone.layer2.1.bn2.running_var, backbone.layer2.1.bn2.weight, backbone.layer2.1.bn2.bias, backbone.layer2.1.bn3.running_mean, backbone.layer2.1.bn3.running_var, backbone.layer2.1.bn3.weight, backbone.layer2.1.bn3.bias, backbone.layer2.2.bn1.running_mean, backbone.layer2.2.bn1.running_var, backbone.layer2.2.bn1.weight, backbone.layer2.2.bn1.bias, backbone.layer2.2.bn2.running_mean, backbone.layer2.2.bn2.running_var, backbone.layer2.2.bn2.weight, backbone.layer2.2.bn2.bias, backbone.layer2.2.bn3.running_mean, backbone.layer2.2.bn3.running_var, backbone.layer2.2.bn3.weight, backbone.layer2.2.bn3.bias, backbone.layer2.3.bn1.running_mean, backbone.layer2.3.bn1.running_var, backbone.layer2.3.bn1.weight, backbone.layer2.3.bn1.bias, backbone.layer2.3.bn2.running_mean, backbone.layer2.3.bn2.running_var, backbone.layer2.3.bn2.weight, backbone.layer2.3.bn2.bias, backbone.layer2.3.bn3.running_mean, backbone.layer2.3.bn3.running_var, backbone.layer2.3.bn3.weight, backbone.layer2.3.bn3.bias, backbone.layer3.0.bn1.running_mean, backbone.layer3.0.bn1.running_var, backbone.layer3.0.bn1.weight, backbone.layer3.0.bn1.bias, backbone.layer3.0.bn2.running_mean, backbone.layer3.0.bn2.running_var, backbone.layer3.0.bn2.weight, backbone.layer3.0.bn2.bias, backbone.layer3.0.bn3.running_mean, backbone.layer3.0.bn3.running_var, backbone.layer3.0.bn3.weight, backbone.layer3.0.bn3.bias, backbone.layer3.0.downsample.1.running_mean, backbone.layer3.0.downsample.1.running_var, backbone.layer3.1.bn1.running_mean, backbone.layer3.1.bn1.running_var, backbone.layer3.1.bn1.weight, backbone.layer3.1.bn1.bias, backbone.layer3.1.bn2.running_mean, backbone.layer3.1.bn2.running_var, backbone.layer3.1.bn2.weight, backbone.layer3.1.bn2.bias, backbone.layer3.1.bn3.running_mean, backbone.layer3.1.bn3.running_var, backbone.layer3.1.bn3.weight, backbone.layer3.1.bn3.bias, backbone.layer3.2.bn1.running_mean, backbone.layer3.2.bn1.running_var, backbone.layer3.2.bn1.weight, backbone.layer3.2.bn1.bias, backbone.layer3.2.bn2.running_mean, backbone.layer3.2.bn2.running_var, backbone.layer3.2.bn2.weight, backbone.layer3.2.bn2.bias, backbone.layer3.2.bn3.running_mean, backbone.layer3.2.bn3.running_var, backbone.layer3.2.bn3.weight, backbone.layer3.2.bn3.bias, backbone.layer3.3.bn1.running_mean, backbone.layer3.3.bn1.running_var, backbone.layer3.3.bn1.weight, backbone.layer3.3.bn1.bias, backbone.layer3.3.bn2.running_mean, backbone.layer3.3.bn2.running_var, backbone.layer3.3.bn2.weight, backbone.layer3.3.bn2.bias, backbone.layer3.3.bn3.running_mean, backbone.layer3.3.bn3.running_var, backbone.layer3.3.bn3.weight, backbone.layer3.3.bn3.bias, backbone.layer3.4.bn1.running_mean, backbone.layer3.4.bn1.running_var, backbone.layer3.4.bn1.weight, backbone.layer3.4.bn1.bias, backbone.layer3.4.bn2.running_mean, backbone.layer3.4.bn2.running_var, backbone.layer3.4.bn2.weight, backbone.layer3.4.bn2.bias, backbone.layer3.4.bn3.running_mean, backbone.layer3.4.bn3.running_var, backbone.layer3.4.bn3.weight, backbone.layer3.4.bn3.bias, backbone.layer3.5.bn1.running_mean, backbone.layer3.5.bn1.running_var, backbone.layer3.5.bn1.weight, backbone.layer3.5.bn1.bias, backbone.layer3.5.bn2.running_mean, backbone.layer3.5.bn2.running_var, backbone.layer3.5.bn2.weight, backbone.layer3.5.bn2.bias, backbone.layer3.5.bn3.running_mean, backbone.layer3.5.bn3.running_var, backbone.layer3.5.bn3.weight, backbone.layer3.5.bn3.bias, backbone.layer4.0.bn1.running_mean, backbone.layer4.0.bn1.running_var, backbone.layer4.0.bn1.weight, backbone.layer4.0.bn1.bias, backbone.layer4.0.bn2.running_mean, backbone.layer4.0.bn2.running_var, backbone.layer4.0.bn2.weight, backbone.layer4.0.bn2.bias, backbone.layer4.0.bn3.running_mean, backbone.layer4.0.bn3.running_var, backbone.layer4.0.bn3.weight, backbone.layer4.0.bn3.bias, backbone.layer4.0.downsample.1.running_mean, backbone.layer4.0.downsample.1.running_var, backbone.layer4.1.bn1.running_mean, backbone.layer4.1.bn1.running_var, backbone.layer4.1.bn1.weight, backbone.layer4.1.bn1.bias, backbone.layer4.1.bn2.running_mean, backbone.layer4.1.bn2.running_var, backbone.layer4.1.bn2.weight, backbone.layer4.1.bn2.bias, backbone.layer4.1.bn3.running_mean, backbone.layer4.1.bn3.running_var, backbone.layer4.1.bn3.weight, backbone.layer4.1.bn3.bias, backbone.layer4.2.bn1.running_mean, backbone.layer4.2.bn1.running_var, backbone.layer4.2.bn1.weight, backbone.layer4.2.bn1.bias, backbone.layer4.2.bn2.running_mean, backbone.layer4.2.bn2.running_var, backbone.layer4.2.bn2.weight, backbone.layer4.2.bn2.bias, backbone.layer4.2.bn3.running_mean, backbone.layer4.2.bn3.running_var, backbone.layer4.2.bn3.weight, backbone.layer4.2.bn3.bias
missing keys in source state_dict: backbone.gn1.weight, backbone.gn1.bias, backbone.layer1.0.gn1.weight, backbone.layer1.0.gn1.bias, backbone.layer1.0.gn2.weight, backbone.layer1.0.gn2.bias, backbone.layer1.0.gn3.weight, backbone.layer1.0.gn3.bias, backbone.layer1.1.gn1.weight, backbone.layer1.1.gn1.bias, backbone.layer1.1.gn2.weight, backbone.layer1.1.gn2.bias, backbone.layer1.1.gn3.weight, backbone.layer1.1.gn3.bias, backbone.layer1.2.gn1.weight, backbone.layer1.2.gn1.bias, backbone.layer1.2.gn2.weight, backbone.layer1.2.gn2.bias, backbone.layer1.2.gn3.weight, backbone.layer1.2.gn3.bias, backbone.layer2.0.gn1.weight, backbone.layer2.0.gn1.bias, backbone.layer2.0.gn2.weight, backbone.layer2.0.gn2.bias, backbone.layer2.0.gn3.weight, backbone.layer2.0.gn3.bias, backbone.layer2.1.gn1.weight, backbone.layer2.1.gn1.bias, backbone.layer2.1.gn2.weight, backbone.layer2.1.gn2.bias, backbone.layer2.1.gn3.weight, backbone.layer2.1.gn3.bias, backbone.layer2.2.gn1.weight, backbone.layer2.2.gn1.bias, backbone.layer2.2.gn2.weight, backbone.layer2.2.gn2.bias, backbone.layer2.2.gn3.weight, backbone.layer2.2.gn3.bias, backbone.layer2.3.gn1.weight, backbone.layer2.3.gn1.bias, backbone.layer2.3.gn2.weight, backbone.layer2.3.gn2.bias, backbone.layer2.3.gn3.weight, backbone.layer2.3.gn3.bias, backbone.layer3.0.gn1.weight, backbone.layer3.0.gn1.bias, backbone.layer3.0.conv2.conv_offset.weight, backbone.layer3.0.conv2.conv_offset.bias, backbone.layer3.0.gn2.weight, backbone.layer3.0.gn2.bias, backbone.layer3.0.gn3.weight, backbone.layer3.0.gn3.bias, backbone.layer3.1.gn1.weight, backbone.layer3.1.gn1.bias, backbone.layer3.1.conv2.conv_offset.weight, backbone.layer3.1.conv2.conv_offset.bias, backbone.layer3.1.gn2.weight, backbone.layer3.1.gn2.bias, backbone.layer3.1.gn3.weight, backbone.layer3.1.gn3.bias, backbone.layer3.2.gn1.weight, backbone.layer3.2.gn1.bias, backbone.layer3.2.conv2.conv_offset.weight, backbone.layer3.2.conv2.conv_offset.bias, backbone.layer3.2.gn2.weight, backbone.layer3.2.gn2.bias, backbone.layer3.2.gn3.weight, backbone.layer3.2.gn3.bias, backbone.layer3.3.gn1.weight, backbone.layer3.3.gn1.bias, backbone.layer3.3.conv2.conv_offset.weight, backbone.layer3.3.conv2.conv_offset.bias, backbone.layer3.3.gn2.weight, backbone.layer3.3.gn2.bias, backbone.layer3.3.gn3.weight, backbone.layer3.3.gn3.bias, backbone.layer3.4.gn1.weight, backbone.layer3.4.gn1.bias, backbone.layer3.4.conv2.conv_offset.weight, backbone.layer3.4.conv2.conv_offset.bias, backbone.layer3.4.gn2.weight, backbone.layer3.4.gn2.bias, backbone.layer3.4.gn3.weight, backbone.layer3.4.gn3.bias, backbone.layer3.5.gn1.weight, backbone.layer3.5.gn1.bias, backbone.layer3.5.conv2.conv_offset.weight, backbone.layer3.5.conv2.conv_offset.bias, backbone.layer3.5.gn2.weight, backbone.layer3.5.gn2.bias, backbone.layer3.5.gn3.weight, backbone.layer3.5.gn3.bias, backbone.layer4.0.gn1.weight, backbone.layer4.0.gn1.bias, backbone.layer4.0.conv2.conv_offset.weight, backbone.layer4.0.conv2.conv_offset.bias, backbone.layer4.0.gn2.weight, backbone.layer4.0.gn2.bias, backbone.layer4.0.gn3.weight, backbone.layer4.0.gn3.bias, backbone.layer4.1.gn1.weight, backbone.layer4.1.gn1.bias, backbone.layer4.1.conv2.conv_offset.weight, backbone.layer4.1.conv2.conv_offset.bias, backbone.layer4.1.gn2.weight, backbone.layer4.1.gn2.bias, backbone.layer4.1.gn3.weight, backbone.layer4.1.gn3.bias, backbone.layer4.2.gn1.weight, backbone.layer4.2.gn1.bias, backbone.layer4.2.conv2.conv_offset.weight, backbone.layer4.2.conv2.conv_offset.bias, backbone.layer4.2.gn2.weight, backbone.layer4.2.gn2.bias, backbone.layer4.2.gn3.weight, backbone.layer4.2.gn3.bias, neck.0.lateral_convs.0.conv.weight, neck.0.lateral_convs.0.gn.weight, neck.0.lateral_convs.0.gn.bias, neck.0.lateral_convs.1.conv.weight, neck.0.lateral_convs.1.gn.weight, neck.0.lateral_convs.1.gn.bias, neck.0.lateral_convs.2.conv.weight, neck.0.lateral_convs.2.gn.weight, neck.0.lateral_convs.2.gn.bias, neck.0.lateral_convs.3.conv.weight, neck.0.lateral_convs.3.gn.weight, neck.0.lateral_convs.3.gn.bias, neck.0.fpn_convs.0.conv.weight, neck.0.fpn_convs.0.gn.weight, neck.0.fpn_convs.0.gn.bias, neck.0.fpn_convs.1.conv.weight, neck.0.fpn_convs.1.gn.weight, neck.0.fpn_convs.1.gn.bias, neck.0.fpn_convs.2.conv.weight, neck.0.fpn_convs.2.gn.weight, neck.0.fpn_convs.2.gn.bias, neck.0.fpn_convs.3.conv.weight, neck.0.fpn_convs.3.gn.weight, neck.0.fpn_convs.3.gn.bias, neck.0.fpn_convs.4.conv.weight, neck.0.fpn_convs.4.gn.weight, neck.0.fpn_convs.4.gn.bias, neck.0.fpn_convs.5.conv.weight, neck.0.fpn_convs.5.gn.weight, neck.0.fpn_convs.5.gn.bias, neck.1.level_convs.0.0.conv.weight, neck.1.level_convs.0.0.gn.weight, neck.1.level_convs.0.0.gn.bias, neck.1.level_convs.0.1.conv.weight, neck.1.level_convs.0.1.gn.weight, neck.1.level_convs.0.1.gn.bias, neck.1.level_convs.0.2.conv.weight, neck.1.level_convs.0.2.gn.weight, neck.1.level_convs.0.2.gn.bias, neck.1.level_convs.0.3.conv.weight, neck.1.level_convs.0.3.gn.weight, neck.1.level_convs.0.3.gn.bias, neck.1.level_convs.0.4.conv.weight, neck.1.level_convs.0.4.gn.weight, neck.1.level_convs.0.4.gn.bias, bbox_head.cls_convs.0.conv.weight, bbox_head.cls_convs.0.gn.weight, bbox_head.cls_convs.0.gn.bias, bbox_head.cls_convs.1.conv.weight, bbox_head.cls_convs.1.gn.weight, bbox_head.cls_convs.1.gn.bias, bbox_head.cls_convs.2.conv.weight, bbox_head.cls_convs.2.gn.weight, bbox_head.cls_convs.2.gn.bias, bbox_head.cls_convs.3.conv.weight, bbox_head.cls_convs.3.gn.weight, bbox_head.cls_convs.3.gn.bias, bbox_head.reg_convs.0.conv.weight, bbox_head.reg_convs.0.gn.weight, bbox_head.reg_convs.0.gn.bias, bbox_head.reg_convs.1.conv.weight, bbox_head.reg_convs.1.gn.weight, bbox_head.reg_convs.1.gn.bias, bbox_head.reg_convs.2.conv.weight, bbox_head.reg_convs.2.gn.weight, bbox_head.reg_convs.2.gn.bias, bbox_head.reg_convs.3.conv.weight, bbox_head.reg_convs.3.gn.weight, bbox_head.reg_convs.3.gn.bias, bbox_head.retina_cls.weight, bbox_head.retina_cls.bias, bbox_head.retina_reg.weight, bbox_head.retina_reg.bias, bbox_head.retina_iou.weight, bbox_head.retina_iou.bias
卡在这里不动 我的环境配置：addict                 2.4.0
albumentations         0.5.2
certifi                2020.12.5
cycler                 0.10.0
Cython                 0.29.21
decorator              4.4.2
imagecorruptions       1.1.2
imageio                2.9.0
imgaug                 0.4.0
kiwisolver             1.3.1
matplotlib             3.3.3
mmpycocotools          12.0.3
networkx               2.5
numpy                  1.19.4
opencv-python          <IP_ADDRESS>
opencv-python-headless <IP_ADDRESS>
Pillow                 8.0.1
pip                    20.3.3
pyparsing              2.4.7
python-dateutil        2.8.1
PyWavelets             1.1.1
PyYAML                 5.3.1
scikit-image           0.18.0
scipy                  1.5.4
setuptools             51.0.0.post20201207
Shapely                1.7.1
six                    1.15.0
terminaltables         3.1.0
tifffile               2020.12.8
torch                  1.7.1
torchvision            0.8.2
typing-extensions      <IP_ADDRESS>
vedadet                0.1.0               /home/user/yjq/vedadet
wheel                  0.36.2
yapf                   0.30.0 nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130 +-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.57       Driver Version: 450.57       CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  TITAN Xp            Off  | 00000000:02:00.0 Off |                  N/A |
| 19%   31C    P0    61W / 250W |      0MiB / 12196MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   1  TITAN Xp            Off  | 00000000:03:00.0 Off |                  N/A |
| 17%   36C    P0    60W / 250W |      0MiB / 12196MiB |      0%      Default |
|                               |                      |                  N/A |`
请您帮我看看吧

您好，我现在还有这个问题

Hi @juliusyang97,
In order to speed up the training process, the default setting of logger's interval is 100 that may cause a long wait for printing the info. So you can modify this line 'dict(typename='LoggerHook', interval=100)' of hooks in config, e.g 'dict(typename='LoggerHook', interval=1)'

您好，我现在还是有这个问题
`CUDA_VISIBLE_DEVICES="0" python tools/trainval.py configs/trainval/tinaface/tinaface.py
2020-12-29 18:09:20,558 - vedadet - WARNING - EvalHook is not in modes ['train']
2020-12-29 18:09:20,559 - vedadet - INFO - Loading weights from torchvision://resnet50
2020-12-29 18:09:20,843 - vedadet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.weight, backbone.bn1.bias, backbone.fc.weight, backbone.fc.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn3.running_mean, backbone.layer2.0.bn3.running_var, backbone.layer2.0.bn3.weight, backbone.layer2.0.bn3.bias, backbone.layer2.0.downsample.1.running_mean, backbone.layer2.0.downsample.1.running_var, backbone.layer2.1.bn1.running_mean, backbone.layer2.1.bn1.running_var, backbone.layer2.1.bn1.weight, backbone.layer2.1.bn1.bias, backbone.layer2.1.bn2.running_mean, backbone.layer2.1.bn2.running_var, backbone.layer2.1.bn2.weight, backbone.layer2.1.bn2.bias, backbone.layer2.1.bn3.running_mean, backbone.layer2.1.bn3.running_var, backbone.layer2.1.bn3.weight, backbone.layer2.1.bn3.bias, backbone.layer2.2.bn1.running_mean, backbone.layer2.2.bn1.running_var, backbone.layer2.2.bn1.weight, backbone.layer2.2.bn1.bias, backbone.layer2.2.bn2.running_mean, backbone.layer2.2.bn2.running_var, backbone.layer2.2.bn2.weight, backbone.layer2.2.bn2.bias, backbone.layer2.2.bn3.running_mean, backbone.layer2.2.bn3.running_var, backbone.layer2.2.bn3.weight, backbone.layer2.2.bn3.bias, backbone.layer2.3.bn1.running_mean, backbone.layer2.3.bn1.running_var, backbone.layer2.3.bn1.weight, backbone.layer2.3.bn1.bias, backbone.layer2.3.bn2.running_mean, backbone.layer2.3.bn2.running_var, backbone.layer2.3.bn2.weight, backbone.layer2.3.bn2.bias, backbone.layer2.3.bn3.running_mean, backbone.layer2.3.bn3.running_var, backbone.layer2.3.bn3.weight, backbone.layer2.3.bn3.bias, backbone.layer3.0.bn1.running_mean, backbone.layer3.0.bn1.running_var, backbone.layer3.0.bn1.weight, backbone.layer3.0.bn1.bias, backbone.layer3.0.bn2.running_mean, backbone.layer3.0.bn2.running_var, backbone.layer3.0.bn2.weight, backbone.layer3.0.bn2.bias, backbone.layer3.0.bn3.running_mean, backbone.layer3.0.bn3.running_var, backbone.layer3.0.bn3.weight, backbone.layer3.0.bn3.bias, backbone.layer3.0.downsample.1.running_mean, backbone.layer3.0.downsample.1.running_var, backbone.layer3.1.bn1.running_mean, backbone.layer3.1.bn1.running_var, backbone.layer3.1.bn1.weight, backbone.layer3.1.bn1.bias, backbone.layer3.1.bn2.running_mean, backbone.layer3.1.bn2.running_var, backbone.layer3.1.bn2.weight, backbone.layer3.1.bn2.bias, backbone.layer3.1.bn3.running_mean, backbone.layer3.1.bn3.running_var, backbone.layer3.1.bn3.weight, backbone.layer3.1.bn3.bias, backbone.layer3.2.bn1.running_mean, backbone.layer3.2.bn1.running_var, backbone.layer3.2.bn1.weight, backbone.layer3.2.bn1.bias, backbone.layer3.2.bn2.running_mean, backbone.layer3.2.bn2.running_var, backbone.layer3.2.bn2.weight, backbone.layer3.2.bn2.bias, backbone.layer3.2.bn3.running_mean, backbone.layer3.2.bn3.running_var, backbone.layer3.2.bn3.weight, backbone.layer3.2.bn3.bias, backbone.layer3.3.bn1.running_mean, backbone.layer3.3.bn1.running_var, backbone.layer3.3.bn1.weight, backbone.layer3.3.bn1.bias, backbone.layer3.3.bn2.running_mean, backbone.layer3.3.bn2.running_var, backbone.layer3.3.bn2.weight, backbone.layer3.3.bn2.bias, backbone.layer3.3.bn3.running_mean, backbone.layer3.3.bn3.running_var, backbone.layer3.3.bn3.weight, backbone.layer3.3.bn3.bias, backbone.layer3.4.bn1.running_mean, backbone.layer3.4.bn1.running_var, backbone.layer3.4.bn1.weight, backbone.layer3.4.bn1.bias, backbone.layer3.4.bn2.running_mean, backbone.layer3.4.bn2.running_var, backbone.layer3.4.bn2.weight, backbone.layer3.4.bn2.bias, backbone.layer3.4.bn3.running_mean, backbone.layer3.4.bn3.running_var, backbone.layer3.4.bn3.weight, backbone.layer3.4.bn3.bias, backbone.layer3.5.bn1.running_mean, backbone.layer3.5.bn1.running_var, backbone.layer3.5.bn1.weight, backbone.layer3.5.bn1.bias, backbone.layer3.5.bn2.running_mean, backbone.layer3.5.bn2.running_var, backbone.layer3.5.bn2.weight, backbone.layer3.5.bn2.bias, backbone.layer3.5.bn3.running_mean, backbone.layer3.5.bn3.running_var, backbone.layer3.5.bn3.weight, backbone.layer3.5.bn3.bias, backbone.layer4.0.bn1.running_mean, backbone.layer4.0.bn1.running_var, backbone.layer4.0.bn1.weight, backbone.layer4.0.bn1.bias, backbone.layer4.0.bn2.running_mean, backbone.layer4.0.bn2.running_var, backbone.layer4.0.bn2.weight, backbone.layer4.0.bn2.bias, backbone.layer4.0.bn3.running_mean, backbone.layer4.0.bn3.running_var, backbone.layer4.0.bn3.weight, backbone.layer4.0.bn3.bias, backbone.layer4.0.downsample.1.running_mean, backbone.layer4.0.downsample.1.running_var, backbone.layer4.1.bn1.running_mean, backbone.layer4.1.bn1.running_var, backbone.layer4.1.bn1.weight, backbone.layer4.1.bn1.bias, backbone.layer4.1.bn2.running_mean, backbone.layer4.1.bn2.running_var, backbone.layer4.1.bn2.weight, backbone.layer4.1.bn2.bias, backbone.layer4.1.bn3.running_mean, backbone.layer4.1.bn3.running_var, backbone.layer4.1.bn3.weight, backbone.layer4.1.bn3.bias, backbone.layer4.2.bn1.running_mean, backbone.layer4.2.bn1.running_var, backbone.layer4.2.bn1.weight, backbone.layer4.2.bn1.bias, backbone.layer4.2.bn2.running_mean, backbone.layer4.2.bn2.running_var, backbone.layer4.2.bn2.weight, backbone.layer4.2.bn2.bias, backbone.layer4.2.bn3.running_mean, backbone.layer4.2.bn3.running_var, backbone.layer4.2.bn3.weight, backbone.layer4.2.bn3.bias
missing keys in source state_dict: backbone.gn1.weight, backbone.gn1.bias, backbone.layer1.0.gn1.weight, backbone.layer1.0.gn1.bias, backbone.layer1.0.gn2.weight, backbone.layer1.0.gn2.bias, backbone.layer1.0.gn3.weight, backbone.layer1.0.gn3.bias, backbone.layer1.1.gn1.weight, backbone.layer1.1.gn1.bias, backbone.layer1.1.gn2.weight, backbone.layer1.1.gn2.bias, backbone.layer1.1.gn3.weight, backbone.layer1.1.gn3.bias, backbone.layer1.2.gn1.weight, backbone.layer1.2.gn1.bias, backbone.layer1.2.gn2.weight, backbone.layer1.2.gn2.bias, backbone.layer1.2.gn3.weight, backbone.layer1.2.gn3.bias, backbone.layer2.0.gn1.weight, backbone.layer2.0.gn1.bias, backbone.layer2.0.gn2.weight, backbone.layer2.0.gn2.bias, backbone.layer2.0.gn3.weight, backbone.layer2.0.gn3.bias, backbone.layer2.1.gn1.weight, backbone.layer2.1.gn1.bias, backbone.layer2.1.gn2.weight, backbone.layer2.1.gn2.bias, backbone.layer2.1.gn3.weight, backbone.layer2.1.gn3.bias, backbone.layer2.2.gn1.weight, backbone.layer2.2.gn1.bias, backbone.layer2.2.gn2.weight, backbone.layer2.2.gn2.bias, backbone.layer2.2.gn3.weight, backbone.layer2.2.gn3.bias, backbone.layer2.3.gn1.weight, backbone.layer2.3.gn1.bias, backbone.layer2.3.gn2.weight, backbone.layer2.3.gn2.bias, backbone.layer2.3.gn3.weight, backbone.layer2.3.gn3.bias, backbone.layer3.0.gn1.weight, backbone.layer3.0.gn1.bias, backbone.layer3.0.conv2.conv_offset.weight, backbone.layer3.0.conv2.conv_offset.bias, backbone.layer3.0.gn2.weight, backbone.layer3.0.gn2.bias, backbone.layer3.0.gn3.weight, backbone.layer3.0.gn3.bias, backbone.layer3.1.gn1.weight, backbone.layer3.1.gn1.bias, backbone.layer3.1.conv2.conv_offset.weight, backbone.layer3.1.conv2.conv_offset.bias, backbone.layer3.1.gn2.weight, backbone.layer3.1.gn2.bias, backbone.layer3.1.gn3.weight, backbone.layer3.1.gn3.bias, backbone.layer3.2.gn1.weight, backbone.layer3.2.gn1.bias, backbone.layer3.2.conv2.conv_offset.weight, backbone.layer3.2.conv2.conv_offset.bias, backbone.layer3.2.gn2.weight, backbone.layer3.2.gn2.bias, backbone.layer3.2.gn3.weight, backbone.layer3.2.gn3.bias, backbone.layer3.3.gn1.weight, backbone.layer3.3.gn1.bias, backbone.layer3.3.conv2.conv_offset.weight, backbone.layer3.3.conv2.conv_offset.bias, backbone.layer3.3.gn2.weight, backbone.layer3.3.gn2.bias, backbone.layer3.3.gn3.weight, backbone.layer3.3.gn3.bias, backbone.layer3.4.gn1.weight, backbone.layer3.4.gn1.bias, backbone.layer3.4.conv2.conv_offset.weight, backbone.layer3.4.conv2.conv_offset.bias, backbone.layer3.4.gn2.weight, backbone.layer3.4.gn2.bias, backbone.layer3.4.gn3.weight, backbone.layer3.4.gn3.bias, backbone.layer3.5.gn1.weight, backbone.layer3.5.gn1.bias, backbone.layer3.5.conv2.conv_offset.weight, backbone.layer3.5.conv2.conv_offset.bias, backbone.layer3.5.gn2.weight, backbone.layer3.5.gn2.bias, backbone.layer3.5.gn3.weight, backbone.layer3.5.gn3.bias, backbone.layer4.0.gn1.weight, backbone.layer4.0.gn1.bias, backbone.layer4.0.conv2.conv_offset.weight, backbone.layer4.0.conv2.conv_offset.bias, backbone.layer4.0.gn2.weight, backbone.layer4.0.gn2.bias, backbone.layer4.0.gn3.weight, backbone.layer4.0.gn3.bias, backbone.layer4.1.gn1.weight, backbone.layer4.1.gn1.bias, backbone.layer4.1.conv2.conv_offset.weight, backbone.layer4.1.conv2.conv_offset.bias, backbone.layer4.1.gn2.weight, backbone.layer4.1.gn2.bias, backbone.layer4.1.gn3.weight, backbone.layer4.1.gn3.bias, backbone.layer4.2.gn1.weight, backbone.layer4.2.gn1.bias, backbone.layer4.2.conv2.conv_offset.weight, backbone.layer4.2.conv2.conv_offset.bias, backbone.layer4.2.gn2.weight, backbone.layer4.2.gn2.bias, backbone.layer4.2.gn3.weight, backbone.layer4.2.gn3.bias, neck.0.lateral_convs.0.conv.weight, neck.0.lateral_convs.0.gn.weight, neck.0.lateral_convs.0.gn.bias, neck.0.lateral_convs.1.conv.weight, neck.0.lateral_convs.1.gn.weight, neck.0.lateral_convs.1.gn.bias, neck.0.lateral_convs.2.conv.weight, neck.0.lateral_convs.2.gn.weight, neck.0.lateral_convs.2.gn.bias, neck.0.lateral_convs.3.conv.weight, neck.0.lateral_convs.3.gn.weight, neck.0.lateral_convs.3.gn.bias, neck.0.fpn_convs.0.conv.weight, neck.0.fpn_convs.0.gn.weight, neck.0.fpn_convs.0.gn.bias, neck.0.fpn_convs.1.conv.weight, neck.0.fpn_convs.1.gn.weight, neck.0.fpn_convs.1.gn.bias, neck.0.fpn_convs.2.conv.weight, neck.0.fpn_convs.2.gn.weight, neck.0.fpn_convs.2.gn.bias, neck.0.fpn_convs.3.conv.weight, neck.0.fpn_convs.3.gn.weight, neck.0.fpn_convs.3.gn.bias, neck.0.fpn_convs.4.conv.weight, neck.0.fpn_convs.4.gn.weight, neck.0.fpn_convs.4.gn.bias, neck.0.fpn_convs.5.conv.weight, neck.0.fpn_convs.5.gn.weight, neck.0.fpn_convs.5.gn.bias, neck.1.level_convs.0.0.conv.weight, neck.1.level_convs.0.0.gn.weight, neck.1.level_convs.0.0.gn.bias, neck.1.level_convs.0.1.conv.weight, neck.1.level_convs.0.1.gn.weight, neck.1.level_convs.0.1.gn.bias, neck.1.level_convs.0.2.conv.weight, neck.1.level_convs.0.2.gn.weight, neck.1.level_convs.0.2.gn.bias, neck.1.level_convs.0.3.conv.weight, neck.1.level_convs.0.3.gn.weight, neck.1.level_convs.0.3.gn.bias, neck.1.level_convs.0.4.conv.weight, neck.1.level_convs.0.4.gn.weight, neck.1.level_convs.0.4.gn.bias, bbox_head.cls_convs.0.conv.weight, bbox_head.cls_convs.0.gn.weight, bbox_head.cls_convs.0.gn.bias, bbox_head.cls_convs.1.conv.weight, bbox_head.cls_convs.1.gn.weight, bbox_head.cls_convs.1.gn.bias, bbox_head.cls_convs.2.conv.weight, bbox_head.cls_convs.2.gn.weight, bbox_head.cls_convs.2.gn.bias, bbox_head.cls_convs.3.conv.weight, bbox_head.cls_convs.3.gn.weight, bbox_head.cls_convs.3.gn.bias, bbox_head.reg_convs.0.conv.weight, bbox_head.reg_convs.0.gn.weight, bbox_head.reg_convs.0.gn.bias, bbox_head.reg_convs.1.conv.weight, bbox_head.reg_convs.1.gn.weight, bbox_head.reg_convs.1.gn.bias, bbox_head.reg_convs.2.conv.weight, bbox_head.reg_convs.2.gn.weight, bbox_head.reg_convs.2.gn.bias, bbox_head.reg_convs.3.conv.weight, bbox_head.reg_convs.3.gn.weight, bbox_head.reg_convs.3.gn.bias, bbox_head.retina_cls.weight, bbox_head.retina_cls.bias, bbox_head.retina_reg.weight, bbox_head.retina_reg.bias, bbox_head.retina_iou.weight, bbox_head.retina_iou.bias
卡在这里不动 我的环境配置：addict 2.4.0
albumentations 0.5.2
certifi 2020.12.5
cycler 0.10.0
Cython 0.29.21
decorator 4.4.2
imagecorruptions 1.1.2
imageio 2.9.0
imgaug 0.4.0
kiwisolver 1.3.1
matplotlib 3.3.3
mmpycocotools 12.0.3
networkx 2.5
numpy 1.19.4
opencv-python <IP_ADDRESS>
opencv-python-headless <IP_ADDRESS>
Pillow 8.0.1
pip 20.3.3
pyparsing 2.4.7
python-dateutil 2.8.1
PyWavelets 1.1.1
PyYAML 5.3.1
scikit-image 0.18.0
scipy 1.5.4
setuptools 51.0.0.post20201207
Shapely 1.7.1
six 1.15.0
terminaltables 3.1.0
tifffile 2020.12.8
torch 1.7.1
torchvision 0.8.2
typing-extensions <IP_ADDRESS>
vedadet 0.1.0 /home/user/yjq/vedadet
wheel 0.36.2
yapf 0.30.0 nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130 +-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.57 Driver Version: 450.57 CUDA Version: 11.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 TITAN Xp Off | 00000000:02:00.0 Off | N/A |
| 19% 31C P0 61W / 250W | 0MiB / 12196MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 TITAN Xp Off | 00000000:03:00.0 Off | N/A |
| 17% 36C P0 60W / 250W | 0MiB / 12196MiB | 0% Default |
| | | N/A |`
请您帮我看看吧

`# packages in environment at /home/user/miniconda3/envs/vedadet:

Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
addict                    2.4.0                    pypi_0    pypi
albumentations            0.5.2                    pypi_0    pypi
ca-certificates           2020.12.8            h06a4308_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
certifi                   2020.12.5        py37h06a4308_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
cycler                    0.10.0                   pypi_0    pypi
cython                    0.29.21                  pypi_0    pypi
decorator                 4.4.2                    pypi_0    pypi
imagecorruptions          1.1.2                    pypi_0    pypi
imageio                   2.9.0                    pypi_0    pypi
imgaug                    0.4.0                    pypi_0    pypi
kiwisolver                1.3.1                    pypi_0    pypi
ld_impl_linux-64          2.33.1               h53a641e_7    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
libedit                   3.1.20191231         h14c3975_1    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
libffi                    3.3                  he6710b0_2    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
libgcc-ng                 9.1.0                hdf63c60_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
libstdcxx-ng              9.1.0                hdf63c60_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
matplotlib                3.3.3                    pypi_0    pypi
mmpycocotools             12.0.3                   pypi_0    pypi
ncurses                   6.2                  he6710b0_1    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
networkx                  2.5                      pypi_0    pypi
numpy                     1.19.4                   pypi_0    pypi
opencv-python             <IP_ADDRESS>                 pypi_0    pypi
opencv-python-headless    <IP_ADDRESS>                 pypi_0    pypi
openssl                   1.1.1i               h27cfd23_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
pillow                    8.0.1                    pypi_0    pypi
pip                       20.3.3           py37h06a4308_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
pyparsing                 2.4.7                    pypi_0    pypi
python                    3.7.9                h7579374_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
python-dateutil           2.8.1                    pypi_0    pypi
pywavelets                1.1.1                    pypi_0    pypi
pyyaml                    5.3.1                    pypi_0    pypi
readline                  8.0                  h7b6447c_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
scikit-image              0.18.0                   pypi_0    pypi
scipy                     1.5.4                    pypi_0    pypi
setuptools                51.0.0           py37h06a4308_2    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
shapely                   1.7.1                    pypi_0    pypi
six                       1.15.0                   pypi_0    pypi
sqlite                    3.33.0               h62c20be_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
terminaltables            3.1.0                    pypi_0    pypi
tifffile                  2020.12.8                pypi_0    pypi
tk                        8.6.10               hbc83047_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
torch                     1.7.1                    pypi_0    pypi
torchvision               0.8.2                    pypi_0    pypi
typing-extensions         <IP_ADDRESS>                  pypi_0    pypi
vedadet                   0.1.0                     dev_0
wheel                     0.36.2             pyhd3eb1b0_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
xz                        5.2.5                h7b6447c_0    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
yapf                      0.30.0                   pypi_0    pypi
zlib                      1.2.11               h7b6447c_3    https://mirrors.ustc.edu.cn/anaconda/pkgs/main
`

Did you use your own dataset?

Did you use your own dataset?

yes, I used my own dataset

Did you use your own dataset?

yes, I used my own dataset

请问这是和我数据集的格式或者数据集相关的其他的东西有关系吗，您这么一说我好像意识到我对数据集的格式没怎么仔细检查过

please check #20

I have encountered the identical issue when trying to train tinaface, but at the end it seems to be working:
2021-01-20 13:22:11,689 - vedadet - WARNING - EvalHook is not in modes ['train']
2021-01-20 13:22:11,689 - vedadet - INFO - Loading weights from torchvision://resnet50
2021-01-20 13:22:11,811 - vedadet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.weight, backbone.bn1.bias, backbone.fc.weight, backbone.fc.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn3.running_mean, backbone.layer2.0.bn3.running_var, backbone.layer2.0.bn3.weight, backbone.layer2.0.bn3.bias, backbone.layer2.0.downsample.1.running_mean, backbone.layer2.0.downsample.1.running_var, backbone.layer2.1.bn1.running_mean, backbone.layer2.1.bn1.running_var, backbone.layer2.1.bn1.weight, backbone.layer2.1.bn1.bias, backbone.layer2.1.bn2.running_mean, backbone.layer2.1.bn2.running_var, backbone.layer2.1.bn2.weight, backbone.layer2.1.bn2.bias, backbone.layer2.1.bn3.running_mean, backbone.layer2.1.bn3.running_var, backbone.layer2.1.bn3.weight, backbone.layer2.1.bn3.bias, backbone.layer2.2.bn1.running_mean, backbone.layer2.2.bn1.running_var, backbone.layer2.2.bn1.weight, backbone.layer2.2.bn1.bias, backbone.layer2.2.bn2.running_mean, backbone.layer2.2.bn2.running_var, backbone.layer2.2.bn2.weight, backbone.layer2.2.bn2.bias, backbone.layer2.2.bn3.running_mean, backbone.layer2.2.bn3.running_var, backbone.layer2.2.bn3.weight, backbone.layer2.2.bn3.bias, backbone.layer2.3.bn1.running_mean, backbone.layer2.3.bn1.running_var, backbone.layer2.3.bn1.weight, backbone.layer2.3.bn1.bias, backbone.layer2.3.bn2.running_mean, backbone.layer2.3.bn2.running_var, backbone.layer2.3.bn2.weight, backbone.layer2.3.bn2.bias, backbone.layer2.3.bn3.running_mean, backbone.layer2.3.bn3.running_var, backbone.layer2.3.bn3.weight, backbone.layer2.3.bn3.bias, backbone.layer3.0.bn1.running_mean, backbone.layer3.0.bn1.running_var, backbone.layer3.0.bn1.weight, backbone.layer3.0.bn1.bias, backbone.layer3.0.bn2.running_mean, backbone.layer3.0.bn2.running_var, backbone.layer3.0.bn2.weight, backbone.layer3.0.bn2.bias, backbone.layer3.0.bn3.running_mean, backbone.layer3.0.bn3.running_var, backbone.layer3.0.bn3.weight, backbone.layer3.0.bn3.bias, backbone.layer3.0.downsample.1.running_mean, backbone.layer3.0.downsample.1.running_var, backbone.layer3.1.bn1.running_mean, backbone.layer3.1.bn1.running_var, backbone.layer3.1.bn1.weight, backbone.layer3.1.bn1.bias, backbone.layer3.1.bn2.running_mean, backbone.layer3.1.bn2.running_var, backbone.layer3.1.bn2.weight, backbone.layer3.1.bn2.bias, backbone.layer3.1.bn3.running_mean, backbone.layer3.1.bn3.running_var, backbone.layer3.1.bn3.weight, backbone.layer3.1.bn3.bias, backbone.layer3.2.bn1.running_mean, backbone.layer3.2.bn1.running_var, backbone.layer3.2.bn1.weight, backbone.layer3.2.bn1.bias, backbone.layer3.2.bn2.running_mean, backbone.layer3.2.bn2.running_var, backbone.layer3.2.bn2.weight, backbone.layer3.2.bn2.bias, backbone.layer3.2.bn3.running_mean, backbone.layer3.2.bn3.running_var, backbone.layer3.2.bn3.weight, backbone.layer3.2.bn3.bias, backbone.layer3.3.bn1.running_mean, backbone.layer3.3.bn1.running_var, backbone.layer3.3.bn1.weight, backbone.layer3.3.bn1.bias, backbone.layer3.3.bn2.running_mean, backbone.layer3.3.bn2.running_var, backbone.layer3.3.bn2.weight, backbone.layer3.3.bn2.bias, backbone.layer3.3.bn3.running_mean, backbone.layer3.3.bn3.running_var, backbone.layer3.3.bn3.weight, backbone.layer3.3.bn3.bias, backbone.layer3.4.bn1.running_mean, backbone.layer3.4.bn1.running_var, backbone.layer3.4.bn1.weight, backbone.layer3.4.bn1.bias, backbone.layer3.4.bn2.running_mean, backbone.layer3.4.bn2.running_var, backbone.layer3.4.bn2.weight, backbone.layer3.4.bn2.bias, backbone.layer3.4.bn3.running_mean, backbone.layer3.4.bn3.running_var, backbone.layer3.4.bn3.weight, backbone.layer3.4.bn3.bias, backbone.layer3.5.bn1.running_mean, backbone.layer3.5.bn1.running_var, backbone.layer3.5.bn1.weight, backbone.layer3.5.bn1.bias, backbone.layer3.5.bn2.running_mean, backbone.layer3.5.bn2.running_var, backbone.layer3.5.bn2.weight, backbone.layer3.5.bn2.bias, backbone.layer3.5.bn3.running_mean, backbone.layer3.5.bn3.running_var, backbone.layer3.5.bn3.weight, backbone.layer3.5.bn3.bias, backbone.layer4.0.bn1.running_mean, backbone.layer4.0.bn1.running_var, backbone.layer4.0.bn1.weight, backbone.layer4.0.bn1.bias, backbone.layer4.0.bn2.running_mean, backbone.layer4.0.bn2.running_var, backbone.layer4.0.bn2.weight, backbone.layer4.0.bn2.bias, backbone.layer4.0.bn3.running_mean, backbone.layer4.0.bn3.running_var, backbone.layer4.0.bn3.weight, backbone.layer4.0.bn3.bias, backbone.layer4.0.downsample.1.running_mean, backbone.layer4.0.downsample.1.running_var, backbone.layer4.1.bn1.running_mean, backbone.layer4.1.bn1.running_var, backbone.layer4.1.bn1.weight, backbone.layer4.1.bn1.bias, backbone.layer4.1.bn2.running_mean, backbone.layer4.1.bn2.running_var, backbone.layer4.1.bn2.weight, backbone.layer4.1.bn2.bias, backbone.layer4.1.bn3.running_mean, backbone.layer4.1.bn3.running_var, backbone.layer4.1.bn3.weight, backbone.layer4.1.bn3.bias, backbone.layer4.2.bn1.running_mean, backbone.layer4.2.bn1.running_var, backbone.layer4.2.bn1.weight, backbone.layer4.2.bn1.bias, backbone.layer4.2.bn2.running_mean, backbone.layer4.2.bn2.running_var, backbone.layer4.2.bn2.weight, backbone.layer4.2.bn2.bias, backbone.layer4.2.bn3.running_mean, backbone.layer4.2.bn3.running_var, backbone.layer4.2.bn3.weight, backbone.layer4.2.bn3.bias
missing keys in source state_dict: backbone.gn1.weight, backbone.gn1.bias, backbone.layer1.0.gn1.weight, backbone.layer1.0.gn1.bias, backbone.layer1.0.gn2.weight, backbone.layer1.0.gn2.bias, backbone.layer1.0.gn3.weight, backbone.layer1.0.gn3.bias, backbone.layer1.1.gn1.weight, backbone.layer1.1.gn1.bias, backbone.layer1.1.gn2.weight, backbone.layer1.1.gn2.bias, backbone.layer1.1.gn3.weight, backbone.layer1.1.gn3.bias, backbone.layer1.2.gn1.weight, backbone.layer1.2.gn1.bias, backbone.layer1.2.gn2.weight, backbone.layer1.2.gn2.bias, backbone.layer1.2.gn3.weight, backbone.layer1.2.gn3.bias, backbone.layer2.0.gn1.weight, backbone.layer2.0.gn1.bias, backbone.layer2.0.gn2.weight, backbone.layer2.0.gn2.bias, backbone.layer2.0.gn3.weight, backbone.layer2.0.gn3.bias, backbone.layer2.1.gn1.weight, backbone.layer2.1.gn1.bias, backbone.layer2.1.gn2.weight, backbone.layer2.1.gn2.bias, backbone.layer2.1.gn3.weight, backbone.layer2.1.gn3.bias, backbone.layer2.2.gn1.weight, backbone.layer2.2.gn1.bias, backbone.layer2.2.gn2.weight, backbone.layer2.2.gn2.bias, backbone.layer2.2.gn3.weight, backbone.layer2.2.gn3.bias, backbone.layer2.3.gn1.weight, backbone.layer2.3.gn1.bias, backbone.layer2.3.gn2.weight, backbone.layer2.3.gn2.bias, backbone.layer2.3.gn3.weight, backbone.layer2.3.gn3.bias, backbone.layer3.0.gn1.weight, backbone.layer3.0.gn1.bias, backbone.layer3.0.conv2.conv_offset.weight, backbone.layer3.0.conv2.conv_offset.bias, backbone.layer3.0.gn2.weight, backbone.layer3.0.gn2.bias, backbone.layer3.0.gn3.weight, backbone.layer3.0.gn3.bias, backbone.layer3.1.gn1.weight, backbone.layer3.1.gn1.bias, backbone.layer3.1.conv2.conv_offset.weight, backbone.layer3.1.conv2.conv_offset.bias, backbone.layer3.1.gn2.weight, backbone.layer3.1.gn2.bias, backbone.layer3.1.gn3.weight, backbone.layer3.1.gn3.bias, backbone.layer3.2.gn1.weight, backbone.layer3.2.gn1.bias, backbone.layer3.2.conv2.conv_offset.weight, backbone.layer3.2.conv2.conv_offset.bias, backbone.layer3.2.gn2.weight, backbone.layer3.2.gn2.bias, backbone.layer3.2.gn3.weight, backbone.layer3.2.gn3.bias, backbone.layer3.3.gn1.weight, backbone.layer3.3.gn1.bias, backbone.layer3.3.conv2.conv_offset.weight, backbone.layer3.3.conv2.conv_offset.bias, backbone.layer3.3.gn2.weight, backbone.layer3.3.gn2.bias, backbone.layer3.3.gn3.weight, backbone.layer3.3.gn3.bias, backbone.layer3.4.gn1.weight, backbone.layer3.4.gn1.bias, backbone.layer3.4.conv2.conv_offset.weight, backbone.layer3.4.conv2.conv_offset.bias, backbone.layer3.4.gn2.weight, backbone.layer3.4.gn2.bias, backbone.layer3.4.gn3.weight, backbone.layer3.4.gn3.bias, backbone.layer3.5.gn1.weight, backbone.layer3.5.gn1.bias, backbone.layer3.5.conv2.conv_offset.weight, backbone.layer3.5.conv2.conv_offset.bias, backbone.layer3.5.gn2.weight, backbone.layer3.5.gn2.bias, backbone.layer3.5.gn3.weight, backbone.layer3.5.gn3.bias, backbone.layer4.0.gn1.weight, backbone.layer4.0.gn1.bias, backbone.layer4.0.conv2.conv_offset.weight, backbone.layer4.0.conv2.conv_offset.bias, backbone.layer4.0.gn2.weight, backbone.layer4.0.gn2.bias, backbone.layer4.0.gn3.weight, backbone.layer4.0.gn3.bias, backbone.layer4.1.gn1.weight, backbone.layer4.1.gn1.bias, backbone.layer4.1.conv2.conv_offset.weight, backbone.layer4.1.conv2.conv_offset.bias, backbone.layer4.1.gn2.weight, backbone.layer4.1.gn2.bias, backbone.layer4.1.gn3.weight, backbone.layer4.1.gn3.bias, backbone.layer4.2.gn1.weight, backbone.layer4.2.gn1.bias, backbone.layer4.2.conv2.conv_offset.weight, backbone.layer4.2.conv2.conv_offset.bias, backbone.layer4.2.gn2.weight, backbone.layer4.2.gn2.bias, backbone.layer4.2.gn3.weight, backbone.layer4.2.gn3.bias, neck.0.lateral_convs.0.conv.weight, neck.0.lateral_convs.0.gn.weight, neck.0.lateral_convs.0.gn.bias, neck.0.lateral_convs.1.conv.weight, neck.0.lateral_convs.1.gn.weight, neck.0.lateral_convs.1.gn.bias, neck.0.lateral_convs.2.conv.weight, neck.0.lateral_convs.2.gn.weight, neck.0.lateral_convs.2.gn.bias, 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bbox_head.retina_cls.bias, bbox_head.retina_reg.weight, bbox_head.retina_reg.bias, bbox_head.retina_iou.weight, bbox_head.retina_iou.bias
/home/zhuang/miniconda3/envs/vedadet/lib/python3.6/site-packages/torch/nn/functional.py:3121: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
/home/zhuang/scripts/code for facetracking/vedadet/vedadet/criteria/iou_bbox_anchor_criterion.py:329: UserWarning: This overload of nonzero is deprecated:
nonzero()
Consider using one of the following signatures instead:
nonzero(*, bool as_tuple) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.)
iou_weights[(bbox_weights.sum(axis=1) > 0).nonzero()] = 1.
2021-01-20 13:22:50,511 - vedadet - INFO - Saved weights, optimizer, meta at epoch 1, iter 70 as ./workdir/tinaface/epoch_1
2021-01-20 13:23:29,618 - vedadet - INFO - Saved weights, optimizer, meta at epoch 2, iter 140 as ./workdir/tinaface/epoch_2
2021-01-20 13:24:08,643 - vedadet - INFO - Saved weights, optimizer, meta at epoch 3, iter 210 as ./workdir/tinaface/epoch_3
2021-01-20 13:24:48,695 - vedadet - INFO - Saved weights, optimizer, meta at epoch 4, iter 280 as ./workdir/tinaface/epoch_4
2021-01-20 13:25:29,062 - vedadet - INFO - Saved weights, optimizer, meta at epoch 5, iter 350 as ./workdir/tinaface/epoch_5
2021-01-20 13:26:09,527 - vedadet - INFO - Saved weights, optimizer, meta at epoch 6, iter 420 as ./workdir/tinaface/epoch_6
2021-01-20 13:26:50,262 - vedadet - INFO - Saved weights, optimizer, meta at epoch 7, iter 490 as ./workdir/tinaface/epoch_7
However, is this normal? it seems that the programe is saving weights at each epoch, but it should not be this quick..

I have encountered the identical issue when trying to train tinaface, but at the end it seems to be working:
2021-01-20 13:22:11,689 - vedadet - WARNING - EvalHook is not in modes ['train']
2021-01-20 13:22:11,689 - vedadet - INFO - Loading weights from torchvision://resnet50
2021-01-20 13:22:11,811 - vedadet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.weight, backbone.bn1.bias, backbone.fc.weight, backbone.fc.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn3.running_mean, backbone.layer2.0.bn3.running_var, backbone.layer2.0.bn3.weight, backbone.layer2.0.bn3.bias, backbone.layer2.0.downsample.1.running_mean, backbone.layer2.0.downsample.1.running_var, backbone.layer2.1.bn1.running_mean, backbone.layer2.1.bn1.running_var, backbone.layer2.1.bn1.weight, backbone.layer2.1.bn1.bias, backbone.layer2.1.bn2.running_mean, backbone.layer2.1.bn2.running_var, backbone.layer2.1.bn2.weight, backbone.layer2.1.bn2.bias, backbone.layer2.1.bn3.running_mean, backbone.layer2.1.bn3.running_var, backbone.layer2.1.bn3.weight, backbone.layer2.1.bn3.bias, backbone.layer2.2.bn1.running_mean, backbone.layer2.2.bn1.running_var, backbone.layer2.2.bn1.weight, backbone.layer2.2.bn1.bias, backbone.layer2.2.bn2.running_mean, backbone.layer2.2.bn2.running_var, backbone.layer2.2.bn2.weight, backbone.layer2.2.bn2.bias, backbone.layer2.2.bn3.running_mean, backbone.layer2.2.bn3.running_var, backbone.layer2.2.bn3.weight, backbone.layer2.2.bn3.bias, backbone.layer2.3.bn1.running_mean, backbone.layer2.3.bn1.running_var, backbone.layer2.3.bn1.weight, backbone.layer2.3.bn1.bias, backbone.layer2.3.bn2.running_mean, backbone.layer2.3.bn2.running_var, backbone.layer2.3.bn2.weight, backbone.layer2.3.bn2.bias, backbone.layer2.3.bn3.running_mean, 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backbone.layer3.2.bn1.running_var, backbone.layer3.2.bn1.weight, backbone.layer3.2.bn1.bias, backbone.layer3.2.bn2.running_mean, backbone.layer3.2.bn2.running_var, backbone.layer3.2.bn2.weight, backbone.layer3.2.bn2.bias, backbone.layer3.2.bn3.running_mean, backbone.layer3.2.bn3.running_var, backbone.layer3.2.bn3.weight, backbone.layer3.2.bn3.bias, backbone.layer3.3.bn1.running_mean, backbone.layer3.3.bn1.running_var, backbone.layer3.3.bn1.weight, backbone.layer3.3.bn1.bias, backbone.layer3.3.bn2.running_mean, backbone.layer3.3.bn2.running_var, backbone.layer3.3.bn2.weight, backbone.layer3.3.bn2.bias, backbone.layer3.3.bn3.running_mean, backbone.layer3.3.bn3.running_var, backbone.layer3.3.bn3.weight, backbone.layer3.3.bn3.bias, backbone.layer3.4.bn1.running_mean, backbone.layer3.4.bn1.running_var, backbone.layer3.4.bn1.weight, backbone.layer3.4.bn1.bias, backbone.layer3.4.bn2.running_mean, backbone.layer3.4.bn2.running_var, backbone.layer3.4.bn2.weight, backbone.layer3.4.bn2.bias, backbone.layer3.4.bn3.running_mean, backbone.layer3.4.bn3.running_var, backbone.layer3.4.bn3.weight, backbone.layer3.4.bn3.bias, backbone.layer3.5.bn1.running_mean, backbone.layer3.5.bn1.running_var, backbone.layer3.5.bn1.weight, backbone.layer3.5.bn1.bias, backbone.layer3.5.bn2.running_mean, backbone.layer3.5.bn2.running_var, backbone.layer3.5.bn2.weight, backbone.layer3.5.bn2.bias, backbone.layer3.5.bn3.running_mean, backbone.layer3.5.bn3.running_var, backbone.layer3.5.bn3.weight, backbone.layer3.5.bn3.bias, backbone.layer4.0.bn1.running_mean, backbone.layer4.0.bn1.running_var, backbone.layer4.0.bn1.weight, backbone.layer4.0.bn1.bias, backbone.layer4.0.bn2.running_mean, backbone.layer4.0.bn2.running_var, backbone.layer4.0.bn2.weight, backbone.layer4.0.bn2.bias, backbone.layer4.0.bn3.running_mean, backbone.layer4.0.bn3.running_var, backbone.layer4.0.bn3.weight, backbone.layer4.0.bn3.bias, backbone.layer4.0.downsample.1.running_mean, backbone.layer4.0.downsample.1.running_var, 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missing keys in source state_dict: backbone.gn1.weight, backbone.gn1.bias, backbone.layer1.0.gn1.weight, backbone.layer1.0.gn1.bias, backbone.layer1.0.gn2.weight, backbone.layer1.0.gn2.bias, backbone.layer1.0.gn3.weight, backbone.layer1.0.gn3.bias, backbone.layer1.1.gn1.weight, backbone.layer1.1.gn1.bias, backbone.layer1.1.gn2.weight, backbone.layer1.1.gn2.bias, backbone.layer1.1.gn3.weight, backbone.layer1.1.gn3.bias, backbone.layer1.2.gn1.weight, backbone.layer1.2.gn1.bias, backbone.layer1.2.gn2.weight, backbone.layer1.2.gn2.bias, backbone.layer1.2.gn3.weight, backbone.layer1.2.gn3.bias, backbone.layer2.0.gn1.weight, backbone.layer2.0.gn1.bias, backbone.layer2.0.gn2.weight, backbone.layer2.0.gn2.bias, backbone.layer2.0.gn3.weight, backbone.layer2.0.gn3.bias, backbone.layer2.1.gn1.weight, backbone.layer2.1.gn1.bias, backbone.layer2.1.gn2.weight, backbone.layer2.1.gn2.bias, backbone.layer2.1.gn3.weight, backbone.layer2.1.gn3.bias, backbone.layer2.2.gn1.weight, 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backbone.layer4.0.conv2.conv_offset.bias, backbone.layer4.0.gn2.weight, backbone.layer4.0.gn2.bias, backbone.layer4.0.gn3.weight, backbone.layer4.0.gn3.bias, backbone.layer4.1.gn1.weight, backbone.layer4.1.gn1.bias, backbone.layer4.1.conv2.conv_offset.weight, backbone.layer4.1.conv2.conv_offset.bias, backbone.layer4.1.gn2.weight, backbone.layer4.1.gn2.bias, backbone.layer4.1.gn3.weight, backbone.layer4.1.gn3.bias, backbone.layer4.2.gn1.weight, backbone.layer4.2.gn1.bias, backbone.layer4.2.conv2.conv_offset.weight, backbone.layer4.2.conv2.conv_offset.bias, backbone.layer4.2.gn2.weight, backbone.layer4.2.gn2.bias, backbone.layer4.2.gn3.weight, backbone.layer4.2.gn3.bias, neck.0.lateral_convs.0.conv.weight, neck.0.lateral_convs.0.gn.weight, neck.0.lateral_convs.0.gn.bias, neck.0.lateral_convs.1.conv.weight, neck.0.lateral_convs.1.gn.weight, neck.0.lateral_convs.1.gn.bias, neck.0.lateral_convs.2.conv.weight, neck.0.lateral_convs.2.gn.weight, neck.0.lateral_convs.2.gn.bias, 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bbox_head.retina_cls.bias, bbox_head.retina_reg.weight, bbox_head.retina_reg.bias, bbox_head.retina_iou.weight, bbox_head.retina_iou.bias
/home/zhuang/miniconda3/envs/vedadet/lib/python3.6/site-packages/torch/nn/functional.py:3121: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
/home/zhuang/scripts/code for facetracking/vedadet/vedadet/criteria/iou_bbox_anchor_criterion.py:329: UserWarning: This overload of nonzero is deprecated:
nonzero()
Consider using one of the following signatures instead:
nonzero(*, bool as_tuple) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.)
iou_weights[(bbox_weights.sum(axis=1) > 0).nonzero()] = 1.
2021-01-20 13:22:50,511 - vedadet - INFO - Saved weights, optimizer, meta at epoch 1, iter 70 as ./workdir/tinaface/epoch_1
2021-01-20 13:23:29,618 - vedadet - INFO - Saved weights, optimizer, meta at epoch 2, iter 140 as ./workdir/tinaface/epoch_2
2021-01-20 13:24:08,643 - vedadet - INFO - Saved weights, optimizer, meta at epoch 3, iter 210 as ./workdir/tinaface/epoch_3
2021-01-20 13:24:48,695 - vedadet - INFO - Saved weights, optimizer, meta at epoch 4, iter 280 as ./workdir/tinaface/epoch_4
2021-01-20 13:25:29,062 - vedadet - INFO - Saved weights, optimizer, meta at epoch 5, iter 350 as ./workdir/tinaface/epoch_5
2021-01-20 13:26:09,527 - vedadet - INFO - Saved weights, optimizer, meta at epoch 6, iter 420 as ./workdir/tinaface/epoch_6
2021-01-20 13:26:50,262 - vedadet - INFO - Saved weights, optimizer, meta at epoch 7, iter 490 as ./workdir/tinaface/epoch_7
However, is this normal? it seems that the programe is saving weights at each epoch, but it should not be this quick..

请问您是如何使他成功的呢，我为什么一直在那里不动，您等待了多长时间

杨军奇
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发件人<EMAIL_ADDRESS>发送时间: 2021年1月20日(星期三) 中午1:32
收件人<EMAIL_ADDRESS>抄送<EMAIL_ADDRESS><EMAIL_ADDRESS>主题: Re: [Media-Smart/vedadet] The model and loaded state dict do not match exactly (#21)
I have encountered the identical issue when trying to train tinaface, but at the end it seems to be working:
2021-01-20 13:22:11,689 - vedadet - WARNING - EvalHook is not in modes ['train']
2021-01-20 13:22:11,689 - vedadet - INFO - Loading weights from torchvision://resnet50
2021-01-20 13:22:11,811 - vedadet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.weight, backbone.bn1.bias, backbone.fc.weight, backbone.fc.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn3.running_mean, backbone.layer2.0.bn3.running_var, backbone.layer2.0.bn3.weight, backbone.layer2.0.bn3.bias, backbone.layer2.0.downsample.1.running_mean, backbone.layer2.0.downsample.1.running_var, backbone.layer2.1.bn1.running_mean, backbone.layer2.1.bn1.running_var, backbone.layer2.1.bn1.weight, backbone.layer2.1.bn1.bias, backbone.layer2.1.bn2.running_mean, backbone.layer2.1.bn2.running_var, backbone.layer2.1.bn2.weight, backbone.layer2.1.bn2.bias, backbone.layer2.1.bn3.running_mean, backbone.layer2.1.bn3.running_var, backbone.layer2.1.bn3.weight, backbone.layer2.1.bn3.bias, backbone.layer2.2.bn1.running_mean, backbone.layer2.2.bn1.running_var, backbone.layer2.2.bn1.weight, backbone.layer2.2.bn1.bias, backbone.layer2.2.bn2.running_mean, backbone.layer2.2.bn2.running_var, backbone.layer2.2.bn2.weight, backbone.layer2.2.bn2.bias, backbone.layer2.2.bn3.running_mean, backbone.layer2.2.bn3.running_var, backbone.layer2.2.bn3.weight, backbone.layer2.2.bn3.bias, backbone.layer2.3.bn1.running_mean, backbone.layer2.3.bn1.running_var, backbone.layer2.3.bn1.weight, backbone.layer2.3.bn1.bias, backbone.layer2.3.bn2.running_mean, backbone.layer2.3.bn2.running_var, backbone.layer2.3.bn2.weight, backbone.layer2.3.bn2.bias, backbone.layer2.3.bn3.running_mean, 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backbone.layer3.4.bn3.running_mean, backbone.layer3.4.bn3.running_var, backbone.layer3.4.bn3.weight, backbone.layer3.4.bn3.bias, backbone.layer3.5.bn1.running_mean, backbone.layer3.5.bn1.running_var, backbone.layer3.5.bn1.weight, backbone.layer3.5.bn1.bias, backbone.layer3.5.bn2.running_mean, backbone.layer3.5.bn2.running_var, backbone.layer3.5.bn2.weight, backbone.layer3.5.bn2.bias, backbone.layer3.5.bn3.running_mean, backbone.layer3.5.bn3.running_var, backbone.layer3.5.bn3.weight, backbone.layer3.5.bn3.bias, backbone.layer4.0.bn1.running_mean, backbone.layer4.0.bn1.running_var, backbone.layer4.0.bn1.weight, backbone.layer4.0.bn1.bias, backbone.layer4.0.bn2.running_mean, backbone.layer4.0.bn2.running_var, backbone.layer4.0.bn2.weight, backbone.layer4.0.bn2.bias, backbone.layer4.0.bn3.running_mean, backbone.layer4.0.bn3.running_var, backbone.layer4.0.bn3.weight, backbone.layer4.0.bn3.bias, backbone.layer4.0.downsample.1.running_mean, backbone.layer4.0.downsample.1.running_var, backbone.layer4.1.bn1.running_mean, backbone.layer4.1.bn1.running_var, backbone.layer4.1.bn1.weight, backbone.layer4.1.bn1.bias, backbone.layer4.1.bn2.running_mean, backbone.layer4.1.bn2.running_var, backbone.layer4.1.bn2.weight, backbone.layer4.1.bn2.bias, backbone.layer4.1.bn3.running_mean, backbone.layer4.1.bn3.running_var, backbone.layer4.1.bn3.weight, backbone.layer4.1.bn3.bias, backbone.layer4.2.bn1.running_mean, backbone.layer4.2.bn1.running_var, backbone.layer4.2.bn1.weight, backbone.layer4.2.bn1.bias, backbone.layer4.2.bn2.running_mean, backbone.layer4.2.bn2.running_var, backbone.layer4.2.bn2.weight, backbone.layer4.2.bn2.bias, backbone.layer4.2.bn3.running_mean, backbone.layer4.2.bn3.running_var, backbone.layer4.2.bn3.weight, backbone.layer4.2.bn3.bias
missing keys in source state_dict: backbone.gn1.weight, backbone.gn1.bias, backbone.layer1.0.gn1.weight, backbone.layer1.0.gn1.bias, backbone.layer1.0.gn2.weight, backbone.layer1.0.gn2.bias, backbone.layer1.0.gn3.weight, backbone.layer1.0.gn3.bias, backbone.layer1.1.gn1.weight, backbone.layer1.1.gn1.bias, backbone.layer1.1.gn2.weight, backbone.layer1.1.gn2.bias, backbone.layer1.1.gn3.weight, backbone.layer1.1.gn3.bias, backbone.layer1.2.gn1.weight, backbone.layer1.2.gn1.bias, backbone.layer1.2.gn2.weight, backbone.layer1.2.gn2.bias, backbone.layer1.2.gn3.weight, backbone.layer1.2.gn3.bias, backbone.layer2.0.gn1.weight, backbone.layer2.0.gn1.bias, backbone.layer2.0.gn2.weight, backbone.layer2.0.gn2.bias, backbone.layer2.0.gn3.weight, backbone.layer2.0.gn3.bias, backbone.layer2.1.gn1.weight, backbone.layer2.1.gn1.bias, backbone.layer2.1.gn2.weight, backbone.layer2.1.gn2.bias, backbone.layer2.1.gn3.weight, backbone.layer2.1.gn3.bias, backbone.layer2.2.gn1.weight, backbone.layer2.2.gn1.bias, backbone.layer2.2.gn2.weight, backbone.layer2.2.gn2.bias, backbone.layer2.2.gn3.weight, backbone.layer2.2.gn3.bias, backbone.layer2.3.gn1.weight, backbone.layer2.3.gn1.bias, backbone.layer2.3.gn2.weight, backbone.layer2.3.gn2.bias, backbone.layer2.3.gn3.weight, backbone.layer2.3.gn3.bias, backbone.layer3.0.gn1.weight, backbone.layer3.0.gn1.bias, backbone.layer3.0.conv2.conv_offset.weight, backbone.layer3.0.conv2.conv_offset.bias, backbone.layer3.0.gn2.weight, backbone.layer3.0.gn2.bias, backbone.layer3.0.gn3.weight, backbone.layer3.0.gn3.bias, backbone.layer3.1.gn1.weight, backbone.layer3.1.gn1.bias, backbone.layer3.1.conv2.conv_offset.weight, backbone.layer3.1.conv2.conv_offset.bias, backbone.layer3.1.gn2.weight, backbone.layer3.1.gn2.bias, backbone.layer3.1.gn3.weight, backbone.layer3.1.gn3.bias, backbone.layer3.2.gn1.weight, backbone.layer3.2.gn1.bias, backbone.layer3.2.conv2.conv_offset.weight, backbone.layer3.2.conv2.conv_offset.bias, backbone.layer3.2.gn2.weight, backbone.layer3.2.gn2.bias, backbone.layer3.2.gn3.weight, backbone.layer3.2.gn3.bias, backbone.layer3.3.gn1.weight, backbone.layer3.3.gn1.bias, backbone.layer3.3.conv2.conv_offset.weight, backbone.layer3.3.conv2.conv_offset.bias, backbone.layer3.3.gn2.weight, backbone.layer3.3.gn2.bias, backbone.layer3.3.gn3.weight, backbone.layer3.3.gn3.bias, backbone.layer3.4.gn1.weight, backbone.layer3.4.gn1.bias, backbone.layer3.4.conv2.conv_offset.weight, backbone.layer3.4.conv2.conv_offset.bias, backbone.layer3.4.gn2.weight, backbone.layer3.4.gn2.bias, backbone.layer3.4.gn3.weight, backbone.layer3.4.gn3.bias, backbone.layer3.5.gn1.weight, backbone.layer3.5.gn1.bias, backbone.layer3.5.conv2.conv_offset.weight, backbone.layer3.5.conv2.conv_offset.bias, backbone.layer3.5.gn2.weight, backbone.layer3.5.gn2.bias, backbone.layer3.5.gn3.weight, backbone.layer3.5.gn3.bias, backbone.layer4.0.gn1.weight, backbone.layer4.0.gn1.bias, backbone.layer4.0.conv2.conv_offset.weight, backbone.layer4.0.conv2.conv_offset.bias, backbone.layer4.0.gn2.weight, backbone.layer4.0.gn2.bias, backbone.layer4.0.gn3.weight, backbone.layer4.0.gn3.bias, backbone.layer4.1.gn1.weight, backbone.layer4.1.gn1.bias, backbone.layer4.1.conv2.conv_offset.weight, backbone.layer4.1.conv2.conv_offset.bias, backbone.layer4.1.gn2.weight, backbone.layer4.1.gn2.bias, backbone.layer4.1.gn3.weight, backbone.layer4.1.gn3.bias, backbone.layer4.2.gn1.weight, backbone.layer4.2.gn1.bias, backbone.layer4.2.conv2.conv_offset.weight, backbone.layer4.2.conv2.conv_offset.bias, backbone.layer4.2.gn2.weight, backbone.layer4.2.gn2.bias, backbone.layer4.2.gn3.weight, backbone.layer4.2.gn3.bias, neck.0.lateral_convs.0.conv.weight, neck.0.lateral_convs.0.gn.weight, neck.0.lateral_convs.0.gn.bias, neck.0.lateral_convs.1.conv.weight, neck.0.lateral_convs.1.gn.weight, neck.0.lateral_convs.1.gn.bias, neck.0.lateral_convs.2.conv.weight, neck.0.lateral_convs.2.gn.weight, neck.0.lateral_convs.2.gn.bias, neck.0.lateral_convs.3.conv.weight, neck.0.lateral_convs.3.gn.weight, neck.0.lateral_convs.3.gn.bias, neck.0.fpn_convs.0.conv.weight, neck.0.fpn_convs.0.gn.weight, neck.0.fpn_convs.0.gn.bias, neck.0.fpn_convs.1.conv.weight, neck.0.fpn_convs.1.gn.weight, neck.0.fpn_convs.1.gn.bias, neck.0.fpn_convs.2.conv.weight, neck.0.fpn_convs.2.gn.weight, neck.0.fpn_convs.2.gn.bias, neck.0.fpn_convs.3.conv.weight, neck.0.fpn_convs.3.gn.weight, neck.0.fpn_convs.3.gn.bias, neck.0.fpn_convs.4.conv.weight, neck.0.fpn_convs.4.gn.weight, neck.0.fpn_convs.4.gn.bias, neck.0.fpn_convs.5.conv.weight, neck.0.fpn_convs.5.gn.weight, neck.0.fpn_convs.5.gn.bias, neck.1.level_convs.0.0.conv.weight, neck.1.level_convs.0.0.gn.weight, neck.1.level_convs.0.0.gn.bias, neck.1.level_convs.0.1.conv.weight, neck.1.level_convs.0.1.gn.weight, neck.1.level_convs.0.1.gn.bias, neck.1.level_convs.0.2.conv.weight, neck.1.level_convs.0.2.gn.weight, neck.1.level_convs.0.2.gn.bias, neck.1.level_convs.0.3.conv.weight, neck.1.level_convs.0.3.gn.weight, neck.1.level_convs.0.3.gn.bias, neck.1.level_convs.0.4.conv.weight, neck.1.level_convs.0.4.gn.weight, neck.1.level_convs.0.4.gn.bias, bbox_head.cls_convs.0.conv.weight, bbox_head.cls_convs.0.gn.weight, bbox_head.cls_convs.0.gn.bias, bbox_head.cls_convs.1.conv.weight, bbox_head.cls_convs.1.gn.weight, bbox_head.cls_convs.1.gn.bias, bbox_head.cls_convs.2.conv.weight, bbox_head.cls_convs.2.gn.weight, bbox_head.cls_convs.2.gn.bias, bbox_head.cls_convs.3.conv.weight, bbox_head.cls_convs.3.gn.weight, bbox_head.cls_convs.3.gn.bias, bbox_head.reg_convs.0.conv.weight, bbox_head.reg_convs.0.gn.weight, bbox_head.reg_convs.0.gn.bias, bbox_head.reg_convs.1.conv.weight, bbox_head.reg_convs.1.gn.weight, bbox_head.reg_convs.1.gn.bias, bbox_head.reg_convs.2.conv.weight, bbox_head.reg_convs.2.gn.weight, bbox_head.reg_convs.2.gn.bias, bbox_head.reg_convs.3.conv.weight, bbox_head.reg_convs.3.gn.weight, bbox_head.reg_convs.3.gn.bias, bbox_head.retina_cls.weight, bbox_head.retina_cls.bias, bbox_head.retina_reg.weight, bbox_head.retina_reg.bias, bbox_head.retina_iou.weight, bbox_head.retina_iou.bias
/home/zhuang/miniconda3/envs/vedadet/lib/python3.6/site-packages/torch/nn/functional.py:3121: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
/home/zhuang/scripts/code for facetracking/vedadet/vedadet/criteria/iou_bbox_anchor_criterion.py:329: UserWarning: This overload of nonzero is deprecated:
nonzero()
Consider using one of the following signatures instead:
nonzero(*, bool as_tuple) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.)
iou_weights[(bbox_weights.sum(axis=1) > 0).nonzero()] = 1.
2021-01-20 13:22:50,511 - vedadet - INFO - Saved weights, optimizer, meta at epoch 1, iter 70 as ./workdir/tinaface/epoch_1
2021-01-20 13:23:29,618 - vedadet - INFO - Saved weights, optimizer, meta at epoch 2, iter 140 as ./workdir/tinaface/epoch_2
2021-01-20 13:24:08,643 - vedadet - INFO - Saved weights, optimizer, meta at epoch 3, iter 210 as ./workdir/tinaface/epoch_3
2021-01-20 13:24:48,695 - vedadet - INFO - Saved weights, optimizer, meta at epoch 4, iter 280 as ./workdir/tinaface/epoch_4
2021-01-20 13:25:29,062 - vedadet - INFO - Saved weights, optimizer, meta at epoch 5, iter 350 as ./workdir/tinaface/epoch_5
2021-01-20 13:26:09,527 - vedadet - INFO - Saved weights, optimizer, meta at epoch 6, iter 420 as ./workdir/tinaface/epoch_6
2021-01-20 13:26:50,262 - vedadet - INFO - Saved weights, optimizer, meta at epoch 7, iter 490 as ./workdir/tinaface/epoch_7
However, is this normal? it seems that the programe is saving weights at each epoch, but it should not be this quick..
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请问您是如何使他成功的呢，我为什么一直在那里不动，您等待了多长时间

杨军奇
<EMAIL_ADDRESS>
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发件人<EMAIL_ADDRESS>发送时间: 2021年1月20日(星期三) 中午1:32
收件人<EMAIL_ADDRESS>抄送<EMAIL_ADDRESS><EMAIL_ADDRESS>主题: Re: [Media-Smart/vedadet] The model and loaded state dict do not match exactly (#21)
I have encountered the identical issue when trying to train tinaface, but at the end it seems to be working:
2021-01-20 13:22:11,689 - vedadet - WARNING - EvalHook is not in modes ['train']
2021-01-20 13:22:11,689 - vedadet - INFO - Loading weights from torchvision://resnet50
2021-01-20 13:22:11,811 - vedadet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.weight, backbone.bn1.bias, backbone.fc.weight, backbone.fc.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn3.running_mean, backbone.layer2.0.bn3.running_var, backbone.layer2.0.bn3.weight, backbone.layer2.0.bn3.bias, backbone.layer2.0.downsample.1.running_mean, backbone.layer2.0.downsample.1.running_var, backbone.layer2.1.bn1.running_mean, backbone.layer2.1.bn1.running_var, backbone.layer2.1.bn1.weight, backbone.layer2.1.bn1.bias, backbone.layer2.1.bn2.running_mean, backbone.layer2.1.bn2.running_var, backbone.layer2.1.bn2.weight, backbone.layer2.1.bn2.bias, backbone.layer2.1.bn3.running_mean, backbone.layer2.1.bn3.running_var, backbone.layer2.1.bn3.weight, backbone.layer2.1.bn3.bias, backbone.layer2.2.bn1.running_mean, backbone.layer2.2.bn1.running_var, backbone.layer2.2.bn1.weight, backbone.layer2.2.bn1.bias, backbone.layer2.2.bn2.running_mean, backbone.layer2.2.bn2.running_var, backbone.layer2.2.bn2.weight, backbone.layer2.2.bn2.bias, backbone.layer2.2.bn3.running_mean, backbone.layer2.2.bn3.running_var, backbone.layer2.2.bn3.weight, backbone.layer2.2.bn3.bias, backbone.layer2.3.bn1.running_mean, backbone.layer2.3.bn1.running_var, backbone.layer2.3.bn1.weight, backbone.layer2.3.bn1.bias, backbone.layer2.3.bn2.running_mean, backbone.layer2.3.bn2.running_var, backbone.layer2.3.bn2.weight, backbone.layer2.3.bn2.bias, backbone.layer2.3.bn3.running_mean, backbone.layer2.3.bn3.running_var, backbone.layer2.3.bn3.weight, backbone.layer2.3.bn3.bias, backbone.layer3.0.bn1.running_mean, backbone.layer3.0.bn1.running_var, backbone.layer3.0.bn1.weight, backbone.layer3.0.bn1.bias, backbone.layer3.0.bn2.running_mean, backbone.layer3.0.bn2.running_var, backbone.layer3.0.bn2.weight, backbone.layer3.0.bn2.bias, backbone.layer3.0.bn3.running_mean, backbone.layer3.0.bn3.running_var, backbone.layer3.0.bn3.weight, backbone.layer3.0.bn3.bias, backbone.layer3.0.downsample.1.running_mean, backbone.layer3.0.downsample.1.running_var, backbone.layer3.1.bn1.running_mean, backbone.layer3.1.bn1.running_var, backbone.layer3.1.bn1.weight, backbone.layer3.1.bn1.bias, backbone.layer3.1.bn2.running_mean, backbone.layer3.1.bn2.running_var, backbone.layer3.1.bn2.weight, backbone.layer3.1.bn2.bias, backbone.layer3.1.bn3.running_mean, backbone.layer3.1.bn3.running_var, backbone.layer3.1.bn3.weight, backbone.layer3.1.bn3.bias, backbone.layer3.2.bn1.running_mean, backbone.layer3.2.bn1.running_var, backbone.layer3.2.bn1.weight, backbone.layer3.2.bn1.bias, backbone.layer3.2.bn2.running_mean, backbone.layer3.2.bn2.running_var, backbone.layer3.2.bn2.weight, backbone.layer3.2.bn2.bias, backbone.layer3.2.bn3.running_mean, backbone.layer3.2.bn3.running_var, backbone.layer3.2.bn3.weight, backbone.layer3.2.bn3.bias, backbone.layer3.3.bn1.running_mean, backbone.layer3.3.bn1.running_var, backbone.layer3.3.bn1.weight, backbone.layer3.3.bn1.bias, backbone.layer3.3.bn2.running_mean, backbone.layer3.3.bn2.running_var, backbone.layer3.3.bn2.weight, backbone.layer3.3.bn2.bias, backbone.layer3.3.bn3.running_mean, backbone.layer3.3.bn3.running_var, backbone.layer3.3.bn3.weight, backbone.layer3.3.bn3.bias, backbone.layer3.4.bn1.running_mean, backbone.layer3.4.bn1.running_var, backbone.layer3.4.bn1.weight, backbone.layer3.4.bn1.bias, backbone.layer3.4.bn2.running_mean, backbone.layer3.4.bn2.running_var, backbone.layer3.4.bn2.weight, backbone.layer3.4.bn2.bias, backbone.layer3.4.bn3.running_mean, backbone.layer3.4.bn3.running_var, backbone.layer3.4.bn3.weight, backbone.layer3.4.bn3.bias, backbone.layer3.5.bn1.running_mean, backbone.layer3.5.bn1.running_var, backbone.layer3.5.bn1.weight, backbone.layer3.5.bn1.bias, backbone.layer3.5.bn2.running_mean, backbone.layer3.5.bn2.running_var, backbone.layer3.5.bn2.weight, backbone.layer3.5.bn2.bias, backbone.layer3.5.bn3.running_mean, backbone.layer3.5.bn3.running_var, backbone.layer3.5.bn3.weight, backbone.layer3.5.bn3.bias, backbone.layer4.0.bn1.running_mean, backbone.layer4.0.bn1.running_var, backbone.layer4.0.bn1.weight, backbone.layer4.0.bn1.bias, backbone.layer4.0.bn2.running_mean, backbone.layer4.0.bn2.running_var, backbone.layer4.0.bn2.weight, backbone.layer4.0.bn2.bias, backbone.layer4.0.bn3.running_mean, backbone.layer4.0.bn3.running_var, backbone.layer4.0.bn3.weight, backbone.layer4.0.bn3.bias, backbone.layer4.0.downsample.1.running_mean, backbone.layer4.0.downsample.1.running_var, backbone.layer4.1.bn1.running_mean, backbone.layer4.1.bn1.running_var, backbone.layer4.1.bn1.weight, backbone.layer4.1.bn1.bias, backbone.layer4.1.bn2.running_mean, backbone.layer4.1.bn2.running_var, backbone.layer4.1.bn2.weight, backbone.layer4.1.bn2.bias, backbone.layer4.1.bn3.running_mean, backbone.layer4.1.bn3.running_var, backbone.layer4.1.bn3.weight, backbone.layer4.1.bn3.bias, backbone.layer4.2.bn1.running_mean, backbone.layer4.2.bn1.running_var, backbone.layer4.2.bn1.weight, backbone.layer4.2.bn1.bias, backbone.layer4.2.bn2.running_mean, backbone.layer4.2.bn2.running_var, backbone.layer4.2.bn2.weight, backbone.layer4.2.bn2.bias, backbone.layer4.2.bn3.running_mean, backbone.layer4.2.bn3.running_var, backbone.layer4.2.bn3.weight, backbone.layer4.2.bn3.bias
missing keys in source state_dict: backbone.gn1.weight, backbone.gn1.bias, backbone.layer1.0.gn1.weight, backbone.layer1.0.gn1.bias, backbone.layer1.0.gn2.weight, backbone.layer1.0.gn2.bias, backbone.layer1.0.gn3.weight, backbone.layer1.0.gn3.bias, backbone.layer1.1.gn1.weight, backbone.layer1.1.gn1.bias, backbone.layer1.1.gn2.weight, backbone.layer1.1.gn2.bias, backbone.layer1.1.gn3.weight, backbone.layer1.1.gn3.bias, backbone.layer1.2.gn1.weight, backbone.layer1.2.gn1.bias, backbone.layer1.2.gn2.weight, backbone.layer1.2.gn2.bias, backbone.layer1.2.gn3.weight, backbone.layer1.2.gn3.bias, backbone.layer2.0.gn1.weight, backbone.layer2.0.gn1.bias, backbone.layer2.0.gn2.weight, backbone.layer2.0.gn2.bias, backbone.layer2.0.gn3.weight, backbone.layer2.0.gn3.bias, backbone.layer2.1.gn1.weight, backbone.layer2.1.gn1.bias, backbone.layer2.1.gn2.weight, backbone.layer2.1.gn2.bias, backbone.layer2.1.gn3.weight, backbone.layer2.1.gn3.bias, backbone.layer2.2.gn1.weight, backbone.layer2.2.gn1.bias, backbone.layer2.2.gn2.weight, backbone.layer2.2.gn2.bias, backbone.layer2.2.gn3.weight, backbone.layer2.2.gn3.bias, backbone.layer2.3.gn1.weight, backbone.layer2.3.gn1.bias, backbone.layer2.3.gn2.weight, backbone.layer2.3.gn2.bias, backbone.layer2.3.gn3.weight, backbone.layer2.3.gn3.bias, backbone.layer3.0.gn1.weight, backbone.layer3.0.gn1.bias, backbone.layer3.0.conv2.conv_offset.weight, backbone.layer3.0.conv2.conv_offset.bias, backbone.layer3.0.gn2.weight, backbone.layer3.0.gn2.bias, backbone.layer3.0.gn3.weight, backbone.layer3.0.gn3.bias, backbone.layer3.1.gn1.weight, backbone.layer3.1.gn1.bias, backbone.layer3.1.conv2.conv_offset.weight, backbone.layer3.1.conv2.conv_offset.bias, backbone.layer3.1.gn2.weight, backbone.layer3.1.gn2.bias, backbone.layer3.1.gn3.weight, backbone.layer3.1.gn3.bias, backbone.layer3.2.gn1.weight, backbone.layer3.2.gn1.bias, backbone.layer3.2.conv2.conv_offset.weight, backbone.layer3.2.conv2.conv_offset.bias, 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/home/zhuang/miniconda3/envs/vedadet/lib/python3.6/site-packages/torch/nn/functional.py:3121: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
/home/zhuang/scripts/code for facetracking/vedadet/vedadet/criteria/iou_bbox_anchor_criterion.py:329: UserWarning: This overload of nonzero is deprecated:
nonzero()
Consider using one of the following signatures instead:
nonzero(*, bool as_tuple) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.)
iou_weights[(bbox_weights.sum(axis=1) > 0).nonzero()] = 1.
2021-01-20 13:22:50,511 - vedadet - INFO - Saved weights, optimizer, meta at epoch 1, iter 70 as ./workdir/tinaface/epoch_1
2021-01-20 13:23:29,618 - vedadet - INFO - Saved weights, optimizer, meta at epoch 2, iter 140 as ./workdir/tinaface/epoch_2
2021-01-20 13:24:08,643 - vedadet - INFO - Saved weights, optimizer, meta at epoch 3, iter 210 as ./workdir/tinaface/epoch_3
2021-01-20 13:24:48,695 - vedadet - INFO - Saved weights, optimizer, meta at epoch 4, iter 280 as ./workdir/tinaface/epoch_4
2021-01-20 13:25:29,062 - vedadet - INFO - Saved weights, optimizer, meta at epoch 5, iter 350 as ./workdir/tinaface/epoch_5
2021-01-20 13:26:09,527 - vedadet - INFO - Saved weights, optimizer, meta at epoch 6, iter 420 as ./workdir/tinaface/epoch_6
2021-01-20 13:26:50,262 - vedadet - INFO - Saved weights, optimizer, meta at epoch 7, iter 490 as ./workdir/tinaface/epoch_7
However, is this normal? it seems that the programe is saving weights at each epoch, but it should not be this quick..
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change tinaface.py of dict(typename='LoggerHook', interval=100) to dict(typename='LoggerHook', interval=1), and it should show the training infomation.

change tinaface.py of dict(typename='LoggerHook', interval=100) to dict(typename='LoggerHook', interval=1), and it should show the training infomation.

change tinaface.py of dict(typename='LoggerHook', interval=100) to dict(typename='LoggerHook', interval=1), and it should show the training infomation.

这很是让人头大，这个方法在我这里却不起作用

change tinaface.py of dict(typename='LoggerHook', interval=100) to dict(typename='LoggerHook', interval=1), and it should show the training infomation.

这很是让人头大，这个方法在我这里却不起作用

change tinaface.py of dict(typename='LoggerHook', interval=100) to dict(typename='LoggerHook', interval=1), and it should show the training infomation.

您好，请问您是否愿意我们添加为好友，我的QQ：2107536844

change tinaface.py of dict(typename='LoggerHook', interval=100) to dict(typename='LoggerHook', interval=1), and it should show the training infomation.

您好，请问您是否愿意我们添加为好友，我的QQ：2107536844