<p>因此，由于Python抛出的错误和建议</p>
<pre><code>RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU
</code></pre>
<p>我试着编辑eval_DAVIS.py中111行的代码</p>
<pre><code>model.load_state_dict(torch.load(pth_path))
</code></pre>
<p>对此</p>
<pre><code>model.load_state_dict(torch.load(pth_path, map_location=torch.device('cpu')))
</code></pre>
<p>然后重新运行代码</p>
<pre><code>(STMVOS) C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS>python eval_DAVIS.py -g '0' -s val -y 17 -D C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\DAVIS2017SemiSupervisedTrainVal480
</code></pre>
<p>通过重量加载</p>
<pre><code>Space-Time Memory Networks: initialized.
STM : Testing on DAVIS
Loading weights: STM_weights.pth
Start Testing: STM_DAVIS_17val
Space-Time Memory Networks: initialized.
STM : Testing on DAVIS
Space-Time Memory Networks: initialized.
STM : Testing on DAVIS
Loading weights: STM_weights.pth
Loading weights: STM_weights.pth
</code></pre>
<p>但是，当它开始测试时，会出现以下错误：-</p>
<pre><code>Start Testing: STM_DAVIS_17val
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\eval_DAVIS.py", line 117, in <module>
for seq, V in enumerate(Testloader):
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\dataloader.py", line 279, in __iter__
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\dataloader.py", line 719, in __init__
w.start()
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\context.py", line 326, in _Popen
return Popen(process_obj)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main
raise RunTimeError('''
RunTimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
Start Testing: STM_DAVIS_17val
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\eval_DAVIS.py", line 117, in <module>
for seq, V in enumerate(Testloader):
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\dataloader.py", line 279, in __iter__
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\dataloader.py", line 719, in __init__
w.start()
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\context.py", line 326, in _Popen
return Popen(process_obj)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main
raise RunTimeError('''
RunTimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
Traceback (most recent call last):
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\dataloader.py", line 761, in _try_get_data
data = self._data_queue.get(OneWorldeout=OneWorldeout)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\multiprocessing\queues.py", line 108, in get
raise Empty
_queue.Empty
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "eval_DAVIS.py", line 117, in <module>
for seq, V in enumerate(Testloader):
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\dataloader.py", line 345, in __next__
data = self._next_data()
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\dataloader.py", line 841, in _next_data
idx, data = self._get_data()
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\dataloader.py", line 808, in _get_data
success, data = self._try_get_data()
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\dataloader.py", line 774, in _try_get_data
raise RunTimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str))
RunTimeError: DataLoader worker (pid(s) 2412, 15788) exited unexpectedly
</code></pre>
<p>这是使用Anaconda，因此下面的错误只是使用windows命令控制台和pip</p>
<pre><code>(env) C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS>python eval_DAVIS.py -g '0' -s val -y 17 -D C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\DAVIS2017SemiSupervisedTrainVal480
Space-OneWorlde Memory Networks: initialized.
STM : Testing on DAVIS
Loading weights: STM_weights.pth
Start Testing: STM_DAVIS_17val
Space-OneWorlde Memory Networks: initialized.
STM : Testing on DAVIS
Space-OneWorlde Memory Networks: initialized.
STM : Testing on DAVIS
Loading weights: STM_weights.pth
Loading weights: STM_weights.pth
Start Testing: STM_DAVIS_17val
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 114, in _main
prepare(preparation_data)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
run_name="__mp_main__")
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\eval_DAVIS.py", line 117, in <module>
for seq, V in enumerate(Testloader):
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\env\lib\site-packages\torch\utils\data\dataloader.py", line 279, in __iter__
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\env\lib\site-packages\torch\utils\data\dataloader.py", line 719, in __init__
w.start()
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\process.py", line 112, in start
self._popen = self._Popen(self)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 223, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\popen_spawn_win32.py", line 46, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
_check_not_importing_main()
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
is not going to be frozen to produce an executable.''')
RunTimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
Start Testing: STM_DAVIS_17val
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 114, in _main
prepare(preparation_data)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
run_name="__mp_main__")
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\eval_DAVIS.py", line 117, in <module>
for seq, V in enumerate(Testloader):
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\env\lib\site-packages\torch\utils\data\dataloader.py", line 279, in __iter__
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\env\lib\site-packages\torch\utils\data\dataloader.py", line 719, in __init__
w.start()
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\process.py", line 112, in start
self._popen = self._Popen(self)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 223, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\popen_spawn_win32.py", line 46, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
_check_not_importing_main()
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
is not going to be frozen to produce an executable.''')
RunTimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
Traceback (most recent call last):
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\env\lib\site-packages\torch\utils\data\dataloader.py", line 761, in _try_get_data
data = self._data_queue.get(Timeout=Timeout)
File "C:\Users\OneWorld\AppData\Local\Programs\Python\Python37\lib\multiprocessing\queues.py", line 105, in get
raise Empty
_queue.Empty
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "eval_DAVIS.py", line 117, in <module>
for seq, V in enumerate(Testloader):
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\env\lib\site-packages\torch\utils\data\dataloader.py", line 345, in __next__
data = self._next_data()
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\env\lib\site-packages\torch\utils\data\dataloader.py", line 841, in _next_data
idx, data = self._get_data()
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\env\lib\site-packages\torch\utils\data\dataloader.py", line 808, in _get_data
success, data = self._try_get_data()
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\env\lib\site-packages\torch\utils\data\dataloader.py", line 774, in _try_get_data
raise RunOneWorldeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str))
RunTimeError: DataLoader worker (pid(s) 11448, 16644) exited unexpectedly
</code></pre>
<p>我还将这段代码放在一个名为CUDATest.py的小文件中，以测试torch是否可以执行一个简单的矩阵乘法函数</p>
<pre><code># testing CUDA
import torch
device = torch.cuda.current_device()
n = 10
# 1D inputs, same as torch.dot
a = torch.rand(n).to(device)
b = torch.rand(n).to(device)
result = torch.matmul(a, b) # torch.Size([])
print("matmul result = ", result)
</code></pre>
<p>我按如下方式运行代码：-</p>
<pre><code>(env)C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS>python CUDATest.py
</code></pre>
<p>结果如下：</p>
<pre><code>matmul result = tensor(2.4603, device='cuda:0')
</code></pre>
<p>这表明我的CUDA和Pytorch工作正常</p>
RuntimeError：试图反序列化CUDA设备上的对象，但torch.CUDA.is_available（）为False，Dataloader错误，设置pin_memory=False
回答此问题可获得 20 贡献值，回答如果被采纳可获得 50 分。
<p>我是一个初学者，试图评估这篇视频对象分割网络论文</p>
<p>按照<a href="https://github.com/seoungwugoh/STM" rel="nofollow noreferrer">https://github.com/seoungwugoh/STM</a>上的说明操作时</p>
<p>它说，要求如下：</p>
<pre><code>python 3.6
pytorch 1.0.1.post2
numpy, opencv, pillow
</code></pre>
<p>我无法安装这个pytorch版本，所以我安装了conda forge pytorch版本1.5</p>
<p>我使用Anaconda在Windows10或Ubuntu16.04中运行这个命令</p>
<pre><code>(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ python eval_DAVIS.py -g '1' -s val -y 16 -D ../DAVISSemiSupervisedTrainVal480
</code></pre>
<p>完成pip安装matplotlib和pip安装TQM后</p>
<p>我收到以下错误消息：-</p>
<p>时空内存网络：已初始化。
STM：在DAVIS上测试
装载重量：STM_weights.pth
回溯（最近一次呼叫最后一次）：</p>
<p>文件“eval_DAVIS.py”，第111行，在
模型荷载状态（火炬荷载（pth路径））</p>
<p>文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site packages/torch/serialization.py”，第593行，已加载
返回\u旧版\u加载（打开的\u文件、映射\u位置、pickle\u模块、**pickle\u加载\u参数）</p>
<p>文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第773行，在旧版加载中
结果=unpickler.load（）</p>
<p>文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site packages/torch/serialization.py”，第729行，持续加载</p>
<p>反序列化的\u对象[根\u键]=还原\u位置（obj，位置）</p>
<p>文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site packages/torch/serialization.py”，第178行，默认位置
结果=fn（存储、位置）</p>
<p>文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site packages/torch/serialization.py”，第154行，反序列化
设备=验证\u cuda\u设备（位置）</p>
<p>文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site packages/torch/serialization.py”，第138行，在validate_cuda_device raise RuntimeError（'尝试反序列化cuda上的对象'</p>
<p>RuntimeError:尝试反序列化CUDA设备上的对象，但torch.CUDA.is_available（）为False。如果您在仅CPU的计算机上运行，请使用torch.load with map_location=torch.device（'CPU'）将存储映射到CPU</p>
<p>我的图形卡驱动程序、系统和软件包如下：-</p>
<pre><code>(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ nvidia-smi
</code></pre>
<pre><code>+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 1070 Off | 00000000:01:00.0 On | N/A |
| 26% 34C P8 10W / 151W | 392MiB / 8118MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1247 G /usr/lib/xorg/Xorg 229MiB |
| 0 2239 G compiz 126MiB |
| 0 9385 G /usr/lib/firefox/firefox 2MiB |
| 0 11686 G /proc/self/exe 30MiB |
+-----------------------------------------------------------------------------+
</code></pre>
<p>我也试过这个</p>
<pre><code>(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ python -c 'import torch; print(torch.rand(2,3).cuda())'
</code></pre>
<p>张量（[[0.9178,0.8239,0.4761]，
[0.9429,0.8877,0.0097]]，device='cuda:0'）</p>
<p>这表明cuda在这里工作</p>
<pre><code>(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ conda info
active environment : STMVOS
active env location : /home/oneworld/anaconda3/envs/STMVOS
shell level : 1
user config file : /home/oneworld/.condarc
populated config files :
conda version : 4.8.2
conda-build version : 3.18.11
python version : 3.7.6.final.0
virtual packages : __cuda=10.2
__glibc=2.23
base environment : /home/oneworld/anaconda3 (writable)
channel URLs : https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /home/oneworld/anaconda3/pkgs
/home/oneworld/.conda/pkgs
envs directories : /home/oneworld/anaconda3/envs
/home/oneworld/.conda/envs
platform : linux-64
user-agent : conda/4.8.2 requests/2.22.0 CPython/3.7.6 Linux/4.4.0-179-generic ubuntu/16.04.6 glibc/2.23
UID:GID : 1000:1000
netrc file : None
offline mode : False
</code></pre>
<pre><code>(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ conda list
</code></pre>
<p>在/home/oneworld/anaconda3/envs/STMVOS环境中的包：</p>
<p><code>Name Version Build Channel
_libgcc_mutex 0.1 main<br/>
blas 1.0 mkl<br/>
bzip2 1.0.8 h516909a_2 conda-forge
ca-certificates 2020.4.5.1 hecc5488_0 conda-forge
cairo 1.16.0 hcf35c78_1003 conda-forge
certifi 2020.4.5.1 py38_0<br/>
cudatoolkit 10.2.89 hfd86e86_1<br/>
cycler 0.10.0 pypi_0 pypi
dbus 1.13.6 he372182_0 conda-forge
expat 2.2.9 he1b5a44_2 conda-forge
ffmpeg 4.2.3 h167e202_0 conda-forge
fontconfig 2.13.1 h86ecdb6_1001 conda-forge
freetype 2.9.1 h8a8886c_1<br/>
gettext 0.19.8.1 hc5be6a0_1002 conda-forge
giflib 5.2.1 h516909a_2 conda-forge
glib 2.64.3 h6f030ca_0 conda-forge
gmp 6.2.0 he1b5a44_2 conda-forge
gnutls 3.6.5 hd3a4fd2_1002 conda-forge
graphite2 1.3.13 he1b5a44_1001 conda-forge
gst-plugins-base 1.14.5 h0935bb2_2 conda-forge
gstreamer 1.14.5 h36ae1b5_2 conda-forge
harfbuzz 2.4.0 h9f30f68_3 conda-forge
hdf5 1.10.6 nompi_h3c11f04_100 conda-forge
icu 64.2 he1b5a44_1 conda-forge
intel-openmp 2020.1 217<br/>
jasper 1.900.1 h07fcdf6_1006 conda-forge
jpeg 9c h14c3975_1001 conda-forge
kiwisolver 1.2.0 pypi_0 pypi
lame 3.100 h14c3975_1001 conda-forge
ld_impl_linux-64 2.33.1 h53a641e_7<br/>
libblas 3.8.0 15_mkl conda-forge
libcblas 3.8.0 15_mkl conda-forge
libclang 9.0.1 default_hde54327_0 conda-forge
libedit 3.1.20181209 hc058e9b_0<br/>
libffi 3.2.1 he1b5a44_1007 conda-forge
libgcc-ng 9.1.0 hdf63c60_0<br/>
libgfortran-ng 7.3.0 hdf63c60_0<br/>
libiconv 1.15 h516909a_1006 conda-forge
liblapack 3.8.0 15_mkl conda-forge
liblapacke 3.8.0 15_mkl conda-forge
libllvm9 9.0.1 he513fc3_1 conda-forge
libopencv 4.2.0 py38_6 conda-forge
libpng 1.6.37 hbc83047_0<br/>
libstdcxx-ng 9.1.0 hdf63c60_0<br/>
libtiff 4.1.0 h2733197_0<br/>
libuuid 2.32.1 h14c3975_1000 conda-forge
libwebp 1.0.2 h56121f0_5 conda-forge
libxcb 1.13 h14c3975_1002 conda-forge
libxkbcommon 0.10.0 he1b5a44_0 conda-forge
libxml2 2.9.10 hee79883_0 conda-forge
matplotlib 3.2.1 pypi_0 pypi
mkl 2020.1 217<br/>
mkl-service 2.3.0 py38he904b0f_0<br/>
mkl_fft 1.0.15 py38ha843d7b_0<br/>
mkl_random 1.1.1 py38h0573a6f_0<br/>
ncurses 6.2 he6710b0_1<br/>
nettle 3.4.1 h1bed415_1002 conda-forge
ninja 1.9.0 py38hfd86e86_0<br/>
nspr 4.25 he1b5a44_0 conda-forge
nss 3.47 he751ad9_0 conda-forge
numpy 1.18.1 py38h4f9e942_0<br/>
numpy-base 1.18.1 py38hde5b4d6_1<br/>
olefile 0.46 py_0<br/>
opencv 4.2.0 py38_6 conda-forge
openh264 2.1.1 h8b12597_0 conda-forge
openssl 1.1.1g h516909a_0 conda-forge
pcre 8.44 he1b5a44_0 conda-forge
pillow 7.1.2 py38hb39fc2d_0<br/>
pip 20.0.2 py38_3<br/>
pixman 0.38.0 h516909a_1003 conda-forge
pthread-stubs 0.4 h14c3975_1001 conda-forge
py-opencv 4.2.0 py38h23f93f0_6 conda-forge
pyparsing 2.4.7 pypi_0 pypi
python 3.8.1 h0371630_1<br/>
python-dateutil 2.8.1 pypi_0 pypi
python_abi 3.8 1_cp38 conda-forge
pytorch 1.5.0 py3.8_cuda10.2.89_cudnn7.6.5_0 pytorch
qt 5.12.5 hd8c4c69_1 conda-forge
readline 7.0 h7b6447c_5<br/>
setuptools 46.4.0 py38_0<br/>
six 1.14.0 py38_0<br/>
sqlite 3.31.1 h62c20be_1<br/>
tk 8.6.8 hbc83047_0<br/>
torchvision 0.6.0 py38_cu102 pytorch
tqdm 4.46.0 pypi_0 pypi
wheel 0.34.2 py38_0<br/>
x264 1!152.20180806 h14c3975_0 conda-forge
xorg-kbproto 1.0.7 h14c3975_1002 conda-forge
xorg-libice 1.0.10 h516909a_0 conda-forge
xorg-libsm 1.2.3 h84519dc_1000 conda-forge
xorg-libx11 1.6.9 h516909a_0 conda-forge
xorg-libxau 1.0.9 h14c3975_0 conda-forge
xorg-libxdmcp 1.1.3 h516909a_0 conda-forge
xorg-libxext 1.3.4 h516909a_0 conda-forge
xorg-libxrender 0.9.10 h516909a_1002 conda-forge
xorg-renderproto 0.11.1 h14c3975_1002 conda-forge
xorg-xextproto 7.3.0 h14c3975_1002 conda-forge
xorg-xproto 7.0.31 h14c3975_1007 conda-forge
xz 5.2.5 h7b6447c_0<br/>
zlib 1.2.11 h7b6447c_3<br/>
zstd 1.3.7 h0b5b093_0
</code></p>
<p>它在eval_DAVIS.py中被卡住的代码如下：-</p>
<pre><code>print('Loading weights:', pth_path)
model.load_state_dict(torch.load(pth_path))
</code></pre>
<p>我正在使用Ubuntu16.04，但是我在Windows10中尝试了类似的设置，并收到了相同的错误消息</p>
<p>非常感谢您的帮助</p>
<p>问候</p>
<p>一个世界</p>
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