How does a single GPU run train？

[18022443868]

D:\Anaconda3\envs\refinenet\python.exe "C:\Program Files\JetBrains\PyCharm 2020.1.3\plugins\python\helpers\pydev\pydevd.py" --multiproc --qt-support=auto --client 127.0.0.1 --port 54082 --file E:/vSLAM/light-weight-refinenet-master/src/train.py
pydev debugger: process 16648 is connecting
Connected to pydev debugger (build 201.8538.36)
INFO:main: Loaded Segmenter 152, ImageNet-Pre-Trained=True, #PARAMS=62.04M
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2020.1.3\plugins\python\helpers\pydev\pydevd.py", line 1438, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.1.3\plugins\python\helpers\pydev_pydev_imps_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "E:/vSLAM/light-weight-refinenet-master/src/train.py", line 621, in
main()
File "E:/vSLAM/light-weight-refinenet-master/src/train.py", line 531, in main
best_val, epoch_start = load_ckpt(args.ckpt_path, {"segmenter": segmenter})
File "E:/vSLAM/light-weight-refinenet-master/src/train.py", line 390, in load_ckpt
v.load_state_dict(ckpt[k])
File "D:\Anaconda3\envs\refinenet\lib\site-packages\torch\nn\modules\module.py", line 845, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for DataParallel:
Missing key(s) in state_dict: "module.layer2.4.conv1.weight", "module.layer2.4.bn1.weight", "module.layer2.4.bn1.bias", "module.layer2.4.bn1.running_mean", "module.layer2.4.bn1.running_var", "module.layer2.4.conv2.weight", "module.layer2.4.bn2.weight", "module.layer2.4.bn2.bias", "module.layer2.4.bn2.running_mean", "module.layer2.4.bn2.running_var", "module.layer2.4.conv3.weight", "module.layer2.4.bn3.weight", "module.layer2.4.bn3.bias", "module.layer2.4.bn3.running_mean", "module.layer2.4.bn3.running_var", "module.layer2.5.conv1.weight", "module.layer2.5.bn1.weight", "module.layer2.5.bn1.bias", "module.layer2.5.bn1.running_mean", "module.layer2.5.bn1.running_var", "module.layer2.5.conv2.weight", "module.layer2.5.bn2.weight", "module.layer2.5.bn2.bias", "module.layer2.5.bn2.running_mean", "module.layer2.5.bn2.running_var", "module.layer2.5.conv3.weight", "module.layer2.5.bn3.weight", "module.layer2.5.bn3.bias", "module.layer2.5.bn3.running_mean", "module.layer2.5.bn3.running_var", "module.layer2.6.conv1.weight", "module.layer2.6.bn1.weight", "module.layer2.6.bn1.bias", "module.layer2.6.bn1.running_mean", "module.layer2.6.bn1.running_var", "module.layer2.6.conv2.weight", "module.layer2.6.bn2.weight", "module.layer2.6.bn2.bias", "module.layer2.6.bn2.running_mean", "module.layer2.6.bn2.running_var", "module.layer2.6.conv3.weight", "module.layer2.6.bn3.weight", "module.layer2.6.bn3.bias", "module.layer2.6.bn3.running_mean", "module.layer2.6.bn3.running_var", "module.layer2.7.conv1.weight", "module.layer2.7.bn1.weight", "module.layer2.7.bn1.bias", "module.layer2.7.bn1.running_mean", "module.layer2.7.bn1.running_var", "module.layer2.7.conv2.weight", "module.layer2.7.bn2.weight", "module.layer2.7.bn2.bias", "module.layer2.7.bn2.running_mean", "module.layer2.7.bn2.running_var", "module.layer2.7.conv3.weight", "module.layer2.7.bn3.weight", "module.layer2.7.bn3.bias", "module.layer2.7.bn3.running_mean", "module.layer2.7.bn3.running_var", "module.layer3.6.conv1.weight", "module.layer3.6.bn1.weight", "module.layer3.6.bn1.bias", "module.layer3.6.bn1.running_mean", "module.layer3.6.bn1.running_var", "module.layer3.6.conv2.weight", "module.layer3.6.bn2.weight", "module.layer3.6.bn2.bias", "module.layer3.6.bn2.running_mean", "module.layer3.6.bn2.running_var", "module.layer3.6.conv3.weight", "module.layer3.6.bn3.weight", "module.layer3.6.bn3.bias", "module.layer3.6.bn3.running_mean", "module.layer3.6.bn3.running_var", "module.layer3.7.conv1.weight", "module.layer3.7.bn1.weight", "module.layer3.7.bn1.bias", "module.layer3.7.bn1.running_mean", "module.layer3.7.bn1.running_var", "module.layer3.7.conv2.weight", "module.layer3.7.bn2.weight", "module.layer3.7.bn2.bias", "module.layer3.7.bn2.running_mean", "module.layer3.7.bn2.running_var", "module.layer3.7.conv3.weight", "module.layer3.7.bn3.weight", "module.layer3.7.bn3.bias", "module.layer3.7.bn3.running_mean", "module.layer3.7.bn3.running_var", "module.layer3.8.conv1.weight", "module.layer3.8.bn1.weight", "module.layer3.8.bn1.bias", "module.layer3.8.bn1.running_mean", "module.layer3.8.bn1.running_var", "module.layer3.8.conv2.weight", "module.layer3.8.bn2.weight", "module.layer3.8.bn2.bias", "module.layer3.8.bn2.running_mean", "module.layer3.8.bn2.running_var", "module.layer3.8.conv3.weight", "module.layer3.8.bn3.weight", "module.layer3.8.bn3.bias", "module.layer3.8.bn3.running_mean", "module.layer3.8.bn3.running_var", "module.layer3.9.conv1.weight", "module.layer3.9.bn1.weight", "module.layer3.9.bn1.bias", "module.layer3.9.bn1.running_mean", "module.layer3.9.bn1.running_var", "module.layer3.9.conv2.weight", "module.layer3.9.bn2.weight", "module.layer3.9.bn2.bias", "module.layer3.9.bn2.running_mean", "module.layer3.9.bn2.running_var", "module.layer3.9.conv3.weight", "module.layer3.9.bn3.weight", "module.layer3.9.bn3.bias", "module.layer3.9.bn3.running_mean", "module.layer3.9.bn3.running_var", "module.layer3.10.conv1.weight", "module.layer3.10.bn1.weight", "module.layer3.10.bn1.bias", "module.layer3.10.bn1.running_mean", "module.layer3.10.bn1.running_var", "module.layer3.10.conv2.weight", "module.layer3.10.bn2.weight", "module.layer3.10.bn2.bias", "module.layer3.10.bn2.running_mean", "module.layer3.10.bn2.running_var", "module.layer3.10.conv3.weight", "module.layer3.10.bn3.weight", "module.layer3.10.bn3.bias", "module.layer3.10.bn3.running_mean", "module.layer3.10.bn3.running_var", "module.layer3.11.conv1.weight", "module.layer3.11.bn1.weight", "module.layer3.11.bn1.bias", "module.layer3.11.bn1.running_mean", "module.layer3.11.bn1.running_var", "module.layer3.11.conv2.weight", "module.layer3.11.bn2.weight", "module.layer3.11.bn2.bias", "module.layer3.11.bn2.running_mean", "module.layer3.11.bn2.running_var", "module.layer3.11.conv3.weight", "module.layer3.11.bn3.weight", "module.layer3.11.bn3.bias", "module.layer3.11.bn3.running_mean", "module.layer3.11.bn3.running_var", "module.layer3.12.conv1.weight", "module.layer3.12.bn1.weight", "module.layer3.12.bn1.bias", 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