RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False, Dataloader Error, and setting pin_memory=False
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False, Dataloader Error, and setting pin_memory=False
我是初学者，尝试评价这篇视频对象分割网络论文。
按照 https://github.com/seoungwugoh/STM
上的说明进行操作
说要求如下：-
python 3.6
pytorch 1.0.1.post2
numpy, opencv, pillow
我无法安装这个pytorch版本，所以我安装了conda-forge pytorch 1.5版本。
并且我 运行 使用 Anaconda
在 Windows 10 或 Ubuntu 16.04 中执行此命令
(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ python eval_DAVIS.py -g '1' -s val -y 16 -D ../DAVISSemiSupervisedTrainVal480
在执行 pip install matplotlib 和 pip install tqdm 之后...
我收到以下错误消息：-
Space-时间记忆网络：已初始化。
STM：在 DAVIS 上测试
加载权重：STM_weights.pth
回溯（最近调用最后）：
文件 "eval_DAVIS.py"，第 111 行，在
model.load_state_dict(torch.load(pth_path))
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第 593 行，加载中
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第 773 行，在 _legacy_load
结果 = unpickler.load()
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第 729 行，在 persistent_load
中
deserialized_objects[root_key] = restore_location（对象，位置）
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第178行，默认_restore_location
结果 = fn（存储，位置）
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第 154 行，在 _cuda_deserialize
设备 = validate_cuda_device（位置）
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第 138 行，在 validate_cuda_device raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError：试图在 CUDA 设备上反序列化对象，但 torch.cuda.is_available() 为 False。如果您 运行 宁在 CPU-only 机器上，请使用 torch.load 和 map_location=torch.device('cpu') 来映射您的存储到 CPU
我的显卡驱动、系统和安装包如下：-
(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ nvidia-smi
+-----------------------------------------------------------------------------+
| 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 |
+-----------------------------------------------------------------------------+
我也试过这个
(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ python -c 'import torch; print(torch.rand(2,3).cuda())'
张量([[0.9178, 0.8239, 0.4761],
[0.9429, 0.8877, 0.0097]], 设备='cuda:0')
这说明cuda在这里工作
(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
(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ conda list
/home/oneworld/anaconda3/envs/STMVOS 环境中的软件包：
名称版本构建频道
_libgcc_mutex 0.1 主要<br>
blas 1.0 mkl<br>
bzip2 1.0.8 h516909a_2 conda-forge
ca 证书 2020.4.5.1 hecc5488_0 conda-forge
开罗 1.16.0 hcf35c78_1003 conda-forge
证书 2020.4.5.1 py38_0<br>
cudatoolkit 10.2.89 hfd86e86_1<br>
循环仪 0.10.0 pypi_0 pypi
dbus 1.13.6 he372182_0 conda-forge
外籍人士 2.2.9 he1b5a44_2 conda-forge
ffmpeg 4.2.3 h167e202_0 conda-forge
fontconfig 2.13.1 h86ecdb6_1001 conda-forge
自由打字 2.9.1 h8a8886c_1<br>
gettext 0.19.8.1 hc5be6a0_1002康达锻造
giflib 5.2.1 h516909a_2 conda-forge
油嘴滑舌 2.64.3 h6f030ca_0 conda-forge
gmp 6.2.0 he1b5a44_2 康达锻造
gnutls 3.6.5 hd3a4fd2_1002 康达锻造
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 康达锻造
icu 64.2 he1b5a44_1 康达锻造
英特尔-openmp 2020.1 217<br>
碧玉 1.900.1 h07fcdf6_1006 conda-forge
jpeg 9c h14c3975_1001 康达锻造
kiwisolver 1.2.0 pypi_0 pypi
跛脚 3.100 h14c3975_1001 conda-forge
ld_impl_linux-642.33.1h53a641e_7<br>
libblas 3.8.0 15_mkl conda-forge
libcblas 3.8.0 15_mkl 康达锻造
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-服务 2.3.0 py38he904b0f_0<br>
mkl_fft 1.0.15 py38ha843d7b_0<br>
mkl_random1.1.1py38h0573a6f_0<br>
ncurses 6.2 he6710b0_1<br>
荨麻 3.4.1 h1bed415_1002 conda-forge
忍者 1.9.0 py38hfd86e86_0<br>
nspr 4.25 he1b5a44_0 康达锻造
nss 3.47 he751ad9_0 康达锻造
numpy 1.18.1 py38h4f9e942_0<br>
numpy-base 1.18.1 py38hde5b4d6_1<br>
文件 0.46 py_0<br>
opencv 4.2.0 py38_6 康达锻造
openh264 2.1.1 h8b12597_0 康达锻造
openssl 1.1.1g h516909a_0 conda-forge
pcre 8.44 he1b5a44_0 conda-forge
枕头 7.1.2 py38hb39fc2d_0<br>
点 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
python3.8.1h0371630_1<br>
python-dateutil 2.8.1 pypi_0 pypi
python_abi 3.8 1_cp38 康达锻造
火炬 1.5.0 py3.8_cuda10.2.89_cudnn7.6.5_0
qt 5.12.5 hd8c4c69_1 conda-forge
阅读线 7.0 h7b6447c_5<br>
安装工具 46.4.0 py38_0<br>
六个 1.14.0 py38_0<br>
sqlite 3.31.1 h62c20be_1<br>
tk 8.6.8 hbc83047_0<br>
火炬视觉 0.6.0 py38_cu102 火炬
tqdm 4.46.0 pypi_0 皮皮
车轮 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
卡在eval_DAVIS.py的代码如下：-
print('Loading weights:', pth_path)
model.load_state_dict(torch.load(pth_path))
我正在使用 Ubuntu 16.04，但是我在 windows 10 中尝试了类似的设置并收到了相同的错误消息。
非常感谢任何帮助。
亲切的问候
同一个世界
我刚刚为这个项目成功创建了 README.md 文件 运行，它在这里：Install PyTorch via pip to run STM Paper。我已经在 Windows 10 中使用 Cuda 版本 10.1 进行了测试。只需按照此 README.md 一步一步操作，您就可以开始了。
根据您的系统配置，您的 PyTorch 安装命令可能会有所不同，如下图所示获取安装命令：
您的 requirements.txt 文件应如下所示：
注意：我没有用 [path/to/DAVIS] 或其他东西做任何事情。您也许可以 运行 脚本 eval_DAVIS.py 而不会出现安装错误，这一切都是我测试过的。你也应该是 Ubuntu 中的 运行，只需使用 README.md 中的适当命令即可。
祝您编程愉快！
因此，由于错误和 Python 提出的建议。
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
我尝试从这个
编辑eval_DAVIS.py中第111行的代码
model.load_state_dict(torch.load(pth_path))
至此
model.load_state_dict(torch.load(pth_path, map_location=torch.device('cpu')))
然后重新运行代码。
(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
通过权重加载。
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
但是，当它开始测试时，出现如下错误：-
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
这是使用 Anaconda，所以下面的错误只是使用 windows 命令控制台和 pip
(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
我还将这段代码放在一个名为 CUDATest.py 的小文件中，以测试 torch 是否可以执行简单的矩阵乘法函数。
# 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)
我运行代码如下：-
(env)C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS>python CUDATest.py
结果如下：-
matmul result = tensor(2.4603, device='cuda:0')
这表明我的 CUDA 和 Pytorch 工作正常。
我从 3.8 更改了 python 版本。到 3.6，使用 conda-forge 进行安装，并重新安装 matplotlib。
我运行代码eval_DAVIS.py在MSVSCode的调试模式下，而不是从命令行注释掉args，如下：-
# def get_arguments():
# parser = argparse.ArgumentParser(description="SST")
# parser.add_argument("-g", type=str, help="0; 0,1; 0,3; etc", required=True)
# parser.add_argument("-s", type=str, help="set", required=True)
# parser.add_argument("-y", type=int, help="year", required=True)
# parser.add_argument("-viz", help="Save visualization", action="store_true")
# parser.add_argument("-D", type=str, help="path to data",default='/local/DATA')
# return parser.parse_args()
# args = get_arguments()
# GPU = args.g
# YEAR = args.y
# SET = args.s
# VIZ = args.viz
# DATA_ROOT = args.D
GPU = '0'
YEAR = '17'
SET = 'val'
VIZ = 'store_true'
DATA_ROOT = '..\DAVIS2017SemiSupervisedTrainVal480'
线以上
for seq, V in enumerate(Testloader):
我写这个是为了测试是否有cuda is available的问题
if torch.cuda.is_available() == False:
print("********** CUDA is NOT available just before line of error **********")
else:
print("********** CUDA is available, and working fine just before line of error ***********")
这会产生以下终端日志
Space-time Memory Networks: initialized.
STM : Testing on DAVIS
using Cuda devices, num: 1
--- Produce mask overaid video outputs. Evaluation will run slow.
--- Require FFMPEG for encoding, Check folder ./viz
Loading weights: STM_weights.pth
Start Testing: STM_DAVIS_17val
********** CUDA is available, and working fine just before line of error ***********
Space-time Memory Networks: initialized.
STM : Testing on DAVIS
using Cuda devices, num: 1
--- Produce mask overaid video outputs. Evaluation will run slow.
--- Require FFMPEG for encoding, Check folder ./viz
Space-time Memory Networks: initialized.
STM : Testing on DAVIS
using Cuda devices, num: 1
--- Produce mask overaid video outputs. Evaluation will run slow.
--- Require FFMPEG for encoding, Check folder ./viz
Loading weights: STM_weights.pth
Loading weights: STM_weights.pth
Start Testing: STM_DAVIS_17val
********** CUDA is available, and working fine just before line of error ***********
Start Testing: STM_DAVIS_17val
********** CUDA is available, and working fine just before line of error ***********
到了这行代码
for seq, V in enumerate(Testloader):
并提供以下错误消息：-
Exception has occurred: 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.
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\eval_DAVIS.py", line 127, in <module>
for seq, V in enumerate(Testloader):
File "<string>", line 1, in <module>
因此，这消除了 CUDA 错误，无需切换代码以使用 CPU。
但是，这仍然会产生 freeze_support() 错误...
并且日志识别出数据加载器错误：-
Traceback (most recent call last):
File "eval_DAVIS.py", line 127, 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 798, 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) 15916, 1232) exited unexpectedly
因为 Python 错误建议
if __name__ == '__main__':
freeze_support()
我添加了这一行
if __name__ == '__main__':
线以上
for seq, V in enumerate(Testloader):
并缩进了该行和下面的所有内容。
然后一直工作到[自行车包装]结束
但是在 [黑天鹅]
之前要求 scipy 安装
所以我安装了 conda scipy
然后重新运行，它开始遍历其余的 [bmx-trees]、[breakdance] 等
生成的 eval_DAVIS.py 文件如下所示...
from __future__ import division
import torch
from torch.autograd import Variable
from torch.utils import data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.utils.model_zoo as model_zoo
from torchvision import models
# general libs
import cv2
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import math
import time
import tqdm
import os
import argparse
import copy
### My libs
from dataset import DAVIS_MO_Test
from model import STM
torch.set_grad_enabled(False) # Volatile
# def get_arguments():
# parser = argparse.ArgumentParser(description="SST")
# parser.add_argument("-g", type=str, help="0; 0,1; 0,3; etc", required=True)
# parser.add_argument("-s", type=str, help="set", required=True)
# parser.add_argument("-y", type=int, help="year", required=True)
# parser.add_argument("-viz", help="Save visualization", action="store_true")
# parser.add_argument("-D", type=str, help="path to data",default='/local/DATA')
# return parser.parse_args()
# args = get_arguments()
# GPU = args.g
# YEAR = args.y
# SET = args.s
# VIZ = args.viz
# DATA_ROOT = args.D
GPU = '0'
YEAR = '17'
SET = 'val'
VIZ = 'store_true'
DATA_ROOT = '..\DAVIS2017SemiSupervisedTrainVal480'
# Model and version
MODEL = 'STM'
print(MODEL, ': Testing on DAVIS')
os.environ['CUDA_VISIBLE_DEVICES'] = GPU
if torch.cuda.is_available():
print('using Cuda devices, num:', torch.cuda.device_count())
if VIZ:
print('--- Produce mask overaid video outputs. Evaluation will run slow.')
print('--- Require FFMPEG for encoding, Check folder ./viz')
palette = Image.open(DATA_ROOT + '/Annotations/480p/blackswan/00000.png').getpalette()
def Run_video(Fs, Ms, num_frames, num_objects, Mem_every=None, Mem_number=None):
# initialize storage tensors
if Mem_every:
to_memorize = [int(i) for i in np.arange(0, num_frames, step=Mem_every)]
elif Mem_number:
to_memorize = [int(round(i)) for i in np.linspace(0, num_frames, num=Mem_number+2)[:-1]]
else:
raise NotImplementedError
Es = torch.zeros_like(Ms)
Es[:,:,0] = Ms[:,:,0]
for t in tqdm.tqdm(range(1, num_frames)):
# memorize
with torch.no_grad():
prev_key, prev_value = model(Fs[:,:,t-1], Es[:,:,t-1], torch.tensor([num_objects]))
if t-1 == 0: #
this_keys, this_values = prev_key, prev_value # only prev memory
else:
this_keys = torch.cat([keys, prev_key], dim=3)
this_values = torch.cat([values, prev_value], dim=3)
# segment
with torch.no_grad():
logit = model(Fs[:,:,t], this_keys, this_values, torch.tensor([num_objects]))
Es[:,:,t] = F.softmax(logit, dim=1)
# update
if t-1 in to_memorize:
keys, values = this_keys, this_values
pred = np.argmax(Es[0].cpu().numpy(), axis=0).astype(np.uint8)
return pred, Es
Testset = DAVIS_MO_Test(DATA_ROOT, resolution='480p', imset='20{}/{}.txt'.format(YEAR,SET), single_object=(YEAR==16))
Testloader = data.DataLoader(Testset, batch_size=1, shuffle=False, num_workers=2, pin_memory=True)
model = nn.DataParallel(STM())
if torch.cuda.is_available():
model.cuda()
model.eval() # turn-off BN
pth_path = 'STM_weights.pth'
print('Loading weights:', pth_path)
model.load_state_dict(torch.load(pth_path)) # , map_location=torch.device('cpu')
code_name = '{}_DAVIS_{}{}'.format(MODEL,YEAR,SET)
print('Start Testing:', code_name)
if torch.cuda.is_available() == False:
print("********** CUDA is NOT available just before line of error **********")
else:
print("********** CUDA is available, and working fine just before line of error ***********")
if __name__ == '__main__':
for seq, V in enumerate(Testloader):
Fs, Ms, num_objects, info = V
seq_name = info['name'][0]
num_frames = info['num_frames'][0].item()
print('[{}]: num_frames: {}, num_objects: {}'.format(seq_name, num_frames, num_objects[0][0]))
pred, Es = Run_video(Fs, Ms, num_frames, num_objects, Mem_every=5, Mem_number=None)
# Save results for quantitative eval ######################
test_path = os.path.join('./test', code_name, seq_name)
if not os.path.exists(test_path):
os.makedirs(test_path)
for f in range(num_frames):
img_E = Image.fromarray(pred[f])
img_E.putpalette(palette)
img_E.save(os.path.join(test_path, '{:05d}.png'.format(f)))
if VIZ:
from helpers import overlay_davis
# visualize results #######################
viz_path = os.path.join('./viz/', code_name, seq_name)
if not os.path.exists(viz_path):
os.makedirs(viz_path)
for f in range(num_frames):
pF = (Fs[0,:,f].permute(1,2,0).numpy() * 255.).astype(np.uint8)
pE = pred[f]
canvas = overlay_davis(pF, pE, palette)
canvas = Image.fromarray(canvas)
canvas.save(os.path.join(viz_path, 'f{}.jpg'.format(f)))
vid_path = os.path.join('./viz/', code_name, '{}.mp4'.format(seq_name))
frame_path = os.path.join('./viz/', code_name, seq_name, 'f%d.jpg')
os.system('ffmpeg -framerate 10 -i {} {} -vcodec libx264 -crf 10 -pix_fmt yuv420p -nostats -loglevel 0 -y'.format(frame_path, vid_path))
然而...
最后我遇到了内存不足的错误
[car-shadow]: num_frames: 40, num_objects: 1
100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:09<00:00, 3.98it/s]
Traceback (most recent call last):
File "eval_DAVIS.py", line 129, 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 856, in _next_data
return self._process_data(data)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\dataloader.py", line 881, in _process_data
data.reraise()
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\_utils.py", line 395, in reraise
raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in pin memory thread for device 0.
Original Traceback (most recent call last):
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\_utils\pin_memory.py", line 31, in _pin_memory_loop
data = pin_memory(data)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\_utils\pin_memory.py", line 55, in pin_memory
return [pin_memory(sample) for sample in data]
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\_utils\pin_memory.py", line 55, in <listcomp>
return [pin_memory(sample) for sample in data]
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\_utils\pin_memory.py", line 47, in pin_memory
return data.pin_memory()
RuntimeError: cuda runtime error (2) : out of memory at ..\aten\src\THC\THCCachingHostAllocator.cpp:278
所以我在 eval_DAVIS.py
的第 108 行附近将测试加载器从 pin_memory=True 设置为 false
Testloader = data.DataLoader(Testset, batch_size=1, shuffle=False, num_workers=2, pin_memory=False)
然后重新运行。
似乎工作正常。
我是初学者，尝试评价这篇视频对象分割网络论文。
按照 https://github.com/seoungwugoh/STM
上的说明进行操作说要求如下：-
python 3.6
pytorch 1.0.1.post2
numpy, opencv, pillow
我无法安装这个pytorch版本，所以我安装了conda-forge pytorch 1.5版本。
并且我 运行 使用 Anaconda
在 Windows 10 或 Ubuntu 16.04 中执行此命令(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ python eval_DAVIS.py -g '1' -s val -y 16 -D ../DAVISSemiSupervisedTrainVal480
在执行 pip install matplotlib 和 pip install tqdm 之后...
我收到以下错误消息：-
Space-时间记忆网络：已初始化。 STM：在 DAVIS 上测试 加载权重：STM_weights.pth 回溯（最近调用最后）：
文件 "eval_DAVIS.py"，第 111 行，在 model.load_state_dict(torch.load(pth_path))
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第 593 行，加载中 return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第 773 行，在 _legacy_load 结果 = unpickler.load()
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第 729 行，在 persistent_load
中deserialized_objects[root_key] = restore_location（对象，位置）
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第178行，默认_restore_location 结果 = fn（存储，位置）
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第 154 行，在 _cuda_deserialize 设备 = validate_cuda_device（位置）
文件“/home/oneworld/anaconda3/envs/STMVOS/lib/python3.8/site-packages/torch/serialization.py”，第 138 行，在 validate_cuda_device raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError：试图在 CUDA 设备上反序列化对象，但 torch.cuda.is_available() 为 False。如果您 运行 宁在 CPU-only 机器上，请使用 torch.load 和 map_location=torch.device('cpu') 来映射您的存储到 CPU
我的显卡驱动、系统和安装包如下：-
(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ nvidia-smi
+-----------------------------------------------------------------------------+
| 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 |
+-----------------------------------------------------------------------------+
我也试过这个
(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ python -c 'import torch; print(torch.rand(2,3).cuda())'
张量([[0.9178, 0.8239, 0.4761], [0.9429, 0.8877, 0.0097]], 设备='cuda:0')
这说明cuda在这里工作
(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
(STMVOS) oneworld@oneworld:~/Documents/VideoObjectSegmentation/STMVOS$ conda list
/home/oneworld/anaconda3/envs/STMVOS 环境中的软件包：
名称版本构建频道
_libgcc_mutex 0.1 主要<br>
blas 1.0 mkl<br>
bzip2 1.0.8 h516909a_2 conda-forge
ca 证书 2020.4.5.1 hecc5488_0 conda-forge
开罗 1.16.0 hcf35c78_1003 conda-forge
证书 2020.4.5.1 py38_0<br>
cudatoolkit 10.2.89 hfd86e86_1<br>
循环仪 0.10.0 pypi_0 pypi
dbus 1.13.6 he372182_0 conda-forge
外籍人士 2.2.9 he1b5a44_2 conda-forge
ffmpeg 4.2.3 h167e202_0 conda-forge
fontconfig 2.13.1 h86ecdb6_1001 conda-forge
自由打字 2.9.1 h8a8886c_1<br>
gettext 0.19.8.1 hc5be6a0_1002康达锻造
giflib 5.2.1 h516909a_2 conda-forge
油嘴滑舌 2.64.3 h6f030ca_0 conda-forge
gmp 6.2.0 he1b5a44_2 康达锻造
gnutls 3.6.5 hd3a4fd2_1002 康达锻造
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 康达锻造
icu 64.2 he1b5a44_1 康达锻造
英特尔-openmp 2020.1 217<br>
碧玉 1.900.1 h07fcdf6_1006 conda-forge
jpeg 9c h14c3975_1001 康达锻造
kiwisolver 1.2.0 pypi_0 pypi
跛脚 3.100 h14c3975_1001 conda-forge
ld_impl_linux-642.33.1h53a641e_7<br>
libblas 3.8.0 15_mkl conda-forge
libcblas 3.8.0 15_mkl 康达锻造
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-服务 2.3.0 py38he904b0f_0<br>
mkl_fft 1.0.15 py38ha843d7b_0<br>
mkl_random1.1.1py38h0573a6f_0<br>
ncurses 6.2 he6710b0_1<br>
荨麻 3.4.1 h1bed415_1002 conda-forge
忍者 1.9.0 py38hfd86e86_0<br>
nspr 4.25 he1b5a44_0 康达锻造
nss 3.47 he751ad9_0 康达锻造
numpy 1.18.1 py38h4f9e942_0<br>
numpy-base 1.18.1 py38hde5b4d6_1<br>
文件 0.46 py_0<br>
opencv 4.2.0 py38_6 康达锻造
openh264 2.1.1 h8b12597_0 康达锻造
openssl 1.1.1g h516909a_0 conda-forge
pcre 8.44 he1b5a44_0 conda-forge
枕头 7.1.2 py38hb39fc2d_0<br>
点 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
python3.8.1h0371630_1<br>
python-dateutil 2.8.1 pypi_0 pypi
python_abi 3.8 1_cp38 康达锻造
火炬 1.5.0 py3.8_cuda10.2.89_cudnn7.6.5_0
qt 5.12.5 hd8c4c69_1 conda-forge
阅读线 7.0 h7b6447c_5<br>
安装工具 46.4.0 py38_0<br>
六个 1.14.0 py38_0<br>
sqlite 3.31.1 h62c20be_1<br>
tk 8.6.8 hbc83047_0<br>
火炬视觉 0.6.0 py38_cu102 火炬
tqdm 4.46.0 pypi_0 皮皮
车轮 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
卡在eval_DAVIS.py的代码如下：-
print('Loading weights:', pth_path)
model.load_state_dict(torch.load(pth_path))
我正在使用 Ubuntu 16.04，但是我在 windows 10 中尝试了类似的设置并收到了相同的错误消息。
非常感谢任何帮助。
亲切的问候
同一个世界
我刚刚为这个项目成功创建了 README.md 文件 运行，它在这里：Install PyTorch via pip to run STM Paper。我已经在 Windows 10 中使用 Cuda 版本 10.1 进行了测试。只需按照此 README.md 一步一步操作，您就可以开始了。
根据您的系统配置，您的 PyTorch 安装命令可能会有所不同，如下图所示获取安装命令：
您的 requirements.txt 文件应如下所示：
注意：我没有用 [path/to/DAVIS] 或其他东西做任何事情。您也许可以 运行 脚本 eval_DAVIS.py 而不会出现安装错误，这一切都是我测试过的。你也应该是 Ubuntu 中的 运行，只需使用 README.md 中的适当命令即可。
祝您编程愉快！
因此，由于错误和 Python 提出的建议。
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
我尝试从这个
编辑eval_DAVIS.py中第111行的代码model.load_state_dict(torch.load(pth_path))
至此
model.load_state_dict(torch.load(pth_path, map_location=torch.device('cpu')))
然后重新运行代码。
(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
通过权重加载。
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
但是，当它开始测试时，出现如下错误：-
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
这是使用 Anaconda，所以下面的错误只是使用 windows 命令控制台和 pip
(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
我还将这段代码放在一个名为 CUDATest.py 的小文件中，以测试 torch 是否可以执行简单的矩阵乘法函数。
# 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)
我运行代码如下：-
(env)C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS>python CUDATest.py
结果如下：-
matmul result = tensor(2.4603, device='cuda:0')
这表明我的 CUDA 和 Pytorch 工作正常。
我从 3.8 更改了 python 版本。到 3.6，使用 conda-forge 进行安装，并重新安装 matplotlib。
我运行代码eval_DAVIS.py在MSVSCode的调试模式下，而不是从命令行注释掉args，如下：-
# def get_arguments():
# parser = argparse.ArgumentParser(description="SST")
# parser.add_argument("-g", type=str, help="0; 0,1; 0,3; etc", required=True)
# parser.add_argument("-s", type=str, help="set", required=True)
# parser.add_argument("-y", type=int, help="year", required=True)
# parser.add_argument("-viz", help="Save visualization", action="store_true")
# parser.add_argument("-D", type=str, help="path to data",default='/local/DATA')
# return parser.parse_args()
# args = get_arguments()
# GPU = args.g
# YEAR = args.y
# SET = args.s
# VIZ = args.viz
# DATA_ROOT = args.D
GPU = '0'
YEAR = '17'
SET = 'val'
VIZ = 'store_true'
DATA_ROOT = '..\DAVIS2017SemiSupervisedTrainVal480'
线以上
for seq, V in enumerate(Testloader):
我写这个是为了测试是否有cuda is available的问题
if torch.cuda.is_available() == False:
print("********** CUDA is NOT available just before line of error **********")
else:
print("********** CUDA is available, and working fine just before line of error ***********")
这会产生以下终端日志
Space-time Memory Networks: initialized.
STM : Testing on DAVIS
using Cuda devices, num: 1
--- Produce mask overaid video outputs. Evaluation will run slow.
--- Require FFMPEG for encoding, Check folder ./viz
Loading weights: STM_weights.pth
Start Testing: STM_DAVIS_17val
********** CUDA is available, and working fine just before line of error ***********
Space-time Memory Networks: initialized.
STM : Testing on DAVIS
using Cuda devices, num: 1
--- Produce mask overaid video outputs. Evaluation will run slow.
--- Require FFMPEG for encoding, Check folder ./viz
Space-time Memory Networks: initialized.
STM : Testing on DAVIS
using Cuda devices, num: 1
--- Produce mask overaid video outputs. Evaluation will run slow.
--- Require FFMPEG for encoding, Check folder ./viz
Loading weights: STM_weights.pth
Loading weights: STM_weights.pth
Start Testing: STM_DAVIS_17val
********** CUDA is available, and working fine just before line of error ***********
Start Testing: STM_DAVIS_17val
********** CUDA is available, and working fine just before line of error ***********
到了这行代码
for seq, V in enumerate(Testloader):
并提供以下错误消息：-
Exception has occurred: 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.
File "C:\Users\OneWorld\Documents\DeepLearning\VideoObjectSegmentation\STMVOS\eval_DAVIS.py", line 127, in <module>
for seq, V in enumerate(Testloader):
File "<string>", line 1, in <module>
因此，这消除了 CUDA 错误，无需切换代码以使用 CPU。
但是，这仍然会产生 freeze_support() 错误...
并且日志识别出数据加载器错误：-
Traceback (most recent call last):
File "eval_DAVIS.py", line 127, 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 798, 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) 15916, 1232) exited unexpectedly
因为 Python 错误建议
if __name__ == '__main__':
freeze_support()
我添加了这一行
if __name__ == '__main__':
线以上
for seq, V in enumerate(Testloader):
并缩进了该行和下面的所有内容。
然后一直工作到[自行车包装]结束
但是在 [黑天鹅]
之前要求 scipy 安装所以我安装了 conda scipy
然后重新运行，它开始遍历其余的 [bmx-trees]、[breakdance] 等
生成的 eval_DAVIS.py 文件如下所示...
from __future__ import division
import torch
from torch.autograd import Variable
from torch.utils import data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.utils.model_zoo as model_zoo
from torchvision import models
# general libs
import cv2
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import math
import time
import tqdm
import os
import argparse
import copy
### My libs
from dataset import DAVIS_MO_Test
from model import STM
torch.set_grad_enabled(False) # Volatile
# def get_arguments():
# parser = argparse.ArgumentParser(description="SST")
# parser.add_argument("-g", type=str, help="0; 0,1; 0,3; etc", required=True)
# parser.add_argument("-s", type=str, help="set", required=True)
# parser.add_argument("-y", type=int, help="year", required=True)
# parser.add_argument("-viz", help="Save visualization", action="store_true")
# parser.add_argument("-D", type=str, help="path to data",default='/local/DATA')
# return parser.parse_args()
# args = get_arguments()
# GPU = args.g
# YEAR = args.y
# SET = args.s
# VIZ = args.viz
# DATA_ROOT = args.D
GPU = '0'
YEAR = '17'
SET = 'val'
VIZ = 'store_true'
DATA_ROOT = '..\DAVIS2017SemiSupervisedTrainVal480'
# Model and version
MODEL = 'STM'
print(MODEL, ': Testing on DAVIS')
os.environ['CUDA_VISIBLE_DEVICES'] = GPU
if torch.cuda.is_available():
print('using Cuda devices, num:', torch.cuda.device_count())
if VIZ:
print('--- Produce mask overaid video outputs. Evaluation will run slow.')
print('--- Require FFMPEG for encoding, Check folder ./viz')
palette = Image.open(DATA_ROOT + '/Annotations/480p/blackswan/00000.png').getpalette()
def Run_video(Fs, Ms, num_frames, num_objects, Mem_every=None, Mem_number=None):
# initialize storage tensors
if Mem_every:
to_memorize = [int(i) for i in np.arange(0, num_frames, step=Mem_every)]
elif Mem_number:
to_memorize = [int(round(i)) for i in np.linspace(0, num_frames, num=Mem_number+2)[:-1]]
else:
raise NotImplementedError
Es = torch.zeros_like(Ms)
Es[:,:,0] = Ms[:,:,0]
for t in tqdm.tqdm(range(1, num_frames)):
# memorize
with torch.no_grad():
prev_key, prev_value = model(Fs[:,:,t-1], Es[:,:,t-1], torch.tensor([num_objects]))
if t-1 == 0: #
this_keys, this_values = prev_key, prev_value # only prev memory
else:
this_keys = torch.cat([keys, prev_key], dim=3)
this_values = torch.cat([values, prev_value], dim=3)
# segment
with torch.no_grad():
logit = model(Fs[:,:,t], this_keys, this_values, torch.tensor([num_objects]))
Es[:,:,t] = F.softmax(logit, dim=1)
# update
if t-1 in to_memorize:
keys, values = this_keys, this_values
pred = np.argmax(Es[0].cpu().numpy(), axis=0).astype(np.uint8)
return pred, Es
Testset = DAVIS_MO_Test(DATA_ROOT, resolution='480p', imset='20{}/{}.txt'.format(YEAR,SET), single_object=(YEAR==16))
Testloader = data.DataLoader(Testset, batch_size=1, shuffle=False, num_workers=2, pin_memory=True)
model = nn.DataParallel(STM())
if torch.cuda.is_available():
model.cuda()
model.eval() # turn-off BN
pth_path = 'STM_weights.pth'
print('Loading weights:', pth_path)
model.load_state_dict(torch.load(pth_path)) # , map_location=torch.device('cpu')
code_name = '{}_DAVIS_{}{}'.format(MODEL,YEAR,SET)
print('Start Testing:', code_name)
if torch.cuda.is_available() == False:
print("********** CUDA is NOT available just before line of error **********")
else:
print("********** CUDA is available, and working fine just before line of error ***********")
if __name__ == '__main__':
for seq, V in enumerate(Testloader):
Fs, Ms, num_objects, info = V
seq_name = info['name'][0]
num_frames = info['num_frames'][0].item()
print('[{}]: num_frames: {}, num_objects: {}'.format(seq_name, num_frames, num_objects[0][0]))
pred, Es = Run_video(Fs, Ms, num_frames, num_objects, Mem_every=5, Mem_number=None)
# Save results for quantitative eval ######################
test_path = os.path.join('./test', code_name, seq_name)
if not os.path.exists(test_path):
os.makedirs(test_path)
for f in range(num_frames):
img_E = Image.fromarray(pred[f])
img_E.putpalette(palette)
img_E.save(os.path.join(test_path, '{:05d}.png'.format(f)))
if VIZ:
from helpers import overlay_davis
# visualize results #######################
viz_path = os.path.join('./viz/', code_name, seq_name)
if not os.path.exists(viz_path):
os.makedirs(viz_path)
for f in range(num_frames):
pF = (Fs[0,:,f].permute(1,2,0).numpy() * 255.).astype(np.uint8)
pE = pred[f]
canvas = overlay_davis(pF, pE, palette)
canvas = Image.fromarray(canvas)
canvas.save(os.path.join(viz_path, 'f{}.jpg'.format(f)))
vid_path = os.path.join('./viz/', code_name, '{}.mp4'.format(seq_name))
frame_path = os.path.join('./viz/', code_name, seq_name, 'f%d.jpg')
os.system('ffmpeg -framerate 10 -i {} {} -vcodec libx264 -crf 10 -pix_fmt yuv420p -nostats -loglevel 0 -y'.format(frame_path, vid_path))
然而...
最后我遇到了内存不足的错误
[car-shadow]: num_frames: 40, num_objects: 1
100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 39/39 [00:09<00:00, 3.98it/s]
Traceback (most recent call last):
File "eval_DAVIS.py", line 129, 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 856, in _next_data
return self._process_data(data)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\dataloader.py", line 881, in _process_data
data.reraise()
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\_utils.py", line 395, in reraise
raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in pin memory thread for device 0.
Original Traceback (most recent call last):
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\_utils\pin_memory.py", line 31, in _pin_memory_loop
data = pin_memory(data)
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\_utils\pin_memory.py", line 55, in pin_memory
return [pin_memory(sample) for sample in data]
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\_utils\pin_memory.py", line 55, in <listcomp>
return [pin_memory(sample) for sample in data]
File "C:\Users\OneWorld\anaconda3\envs\STMVOS\lib\site-packages\torch\utils\data\_utils\pin_memory.py", line 47, in pin_memory
return data.pin_memory()
RuntimeError: cuda runtime error (2) : out of memory at ..\aten\src\THC\THCCachingHostAllocator.cpp:278
所以我在 eval_DAVIS.py
的第 108 行附近将测试加载器从 pin_memory=True 设置为 falseTestloader = data.DataLoader(Testset, batch_size=1, shuffle=False, num_workers=2, pin_memory=False)
然后重新运行。
似乎工作正常。