Я следовал руководству по pytorch, обучая классификатор с использованием набора данных CIFAR10. Все, что я сделал, это скопировал и вставил коды на странице учебника в проект Pycharm, но я столкнулся с неизвестными ошибками. Я также использовал блокнот Jupyter для запуска того же кода и снова встречал ошибки. Ниже приведен код и ошибки. Что мне делать?
import torch
import torchvision
import matplotlib.pyplot as plt
import numpy as np
import torchvision.transforms as transforms
import torchvision.models as models
import torch.autograd as Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
Ошибка в Pycharm:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\spawn.py", line 114, in _main
prepare(preparation_data)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
run_name="__mp_main__")
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "D:\Users\VML1\PycharmProjects\untitled1\HY1.py", line 57, in <module>
for i, data in enumerate(trainloader, 0):
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 417, in __iter__
return DataLoaderIter(self)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 234, in __init__
w.start()
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\process.py", line 105, in start
self._popen = self._Popen(self)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\context.py", line 223, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\popen_spawn_win32.py", line 33, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
_check_not_importing_main()
File "D:\ProgramData\Anaconda3\envs\pytorch\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 "D:/Users/VML1/PycharmProjects/untitled1/HY1.py", line 57, in <module>
for i, data in enumerate(trainloader, 0):
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 417, in __iter__
return DataLoaderIter(self)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 234, in __init__
w.start()
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\process.py", line 105, in start
self._popen = self._Popen(self)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\context.py", line 223, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
reduction.dump(process_obj, to_child)
File "D:\ProgramData\Anaconda3\envs\pytorch\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
BrokenPipeError: [Errno 32] Broken pipe
Ошибка в Jupyter:
TypeError
Traceback (most recent call last)
<ipython-input-2-1dd1ecde814e> in <module>
---> 45 outputs = net(inputs)
46 loss = criterion(outputs, labels)
47 loss.backward()
c:\users\vml1\anaconda3\envs\hypytorch\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
355 result = self._slow_forward(*input, **kwargs)
356 else:
--> 357 result = self.forward(*input, **kwargs)
358 for hook in self._forward_hooks.values():
359 hook_result = hook(self, input, result)
c:\users\vml1\anaconda3\envs\hypytorch\lib\site-packages\torchvision\models\resnet.py in forward(self, x)
137
138 def forward(self, x):
--> 139 x = self.conv1(x)
140 x = self.bn1(x)
141 x = self.relu(x)
c:\users\vml1\anaconda3\envs\hypytorch\lib\site-
packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
355 result = self._slow_forward(*input, **kwargs)
356 else:
--> 357 result = self.forward(*input, **kwargs)
358 for hook in self._forward_hooks.values():
359 hook_result = hook(self, input, result)
c:\users\vml1\anaconda3\envs\hypytorch\lib\site-packages\torch\nn\modules\conv.py in forward(self, input)
280 def forward(self, input):
281 return F.conv2d(input, self.weight, self.bias, self.stride,
--> 282 self.padding, self.dilation, self.groups)
283
284
c:\users\vml1\anaconda3\envs\hypytorch\lib\site-
packages\torch\nn\functional.py in conv2d(input, weight, bias, stride,
padding, dilation, groups)
88 _pair(0), groups, torch.backends.cudnn.benchmark,
89 torch.backends.cudnn.deterministic,
torch.backends.cudnn.enabled)
---> 90 return f(input, weight, bias)
91
92
TypeError: argument 0 is not a Variable