我在将模型加载到Google colab时遇到问题。这是代码：
我已附上以下代码
我曾尝试更改statedict的名称，但无济于事
基本上，我正在尝试保存模型以备后用，但是，由于我无法正确保存和加载模型，这变得非常困难。请帮我解决这个问题。在代码部分之后，您还将在下面找到我附加的错误。
这是代码
from zipfile import ZipFile
file_name = 'data.zip'
with ZipFile(file_name, 'r') as zip:
zip.extractall()
from zipfile import ZipFile
file_name = 'results.zip'
with ZipFile(file_name, 'r') as zip:
zip.extractall()
!pip install tensorflow-gpu
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
from torch.autograd import Variable
batchSize = 64
imageSize = 64
transform = transforms.Compose([transforms.Resize(imageSize), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])
dataset = dset.CIFAR10(root = './data', download = True, transform = transform)
dataloader = torch.utils.data.DataLoader(dataset, batch_size = batchSize, shuffle = True, num_workers = 2)
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
class G(nn.Module):
def __init__(self):
super(G, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose2d(100, 512, 4, 1, 0, bias = False),
nn.BatchNorm2d(512),
nn.ReLU(True),
nn.ConvTranspose2d(512, 256, 4, 2, 1, bias = False),
nn.BatchNorm2d(256),
nn.ReLU(True),
nn.ConvTranspose2d(256, 128, 4, 2, 1, bias = False),
nn.BatchNorm2d(128),
nn.ReLU(True),
nn.ConvTranspose2d(128, 64, 4, 2, 1, bias = False),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.ConvTranspose2d(64, 3, 4, 2, 1, bias = False),
nn.Tanh()
)
def forward(self, input):
output = self.main(input)
return output
netG = G()
netG.load_state_dict(torch.load('generator.pth'))
netG.eval()
#netG.apply(weights_init)
class D(nn.Module):
def __init__(self):
super(D, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(3, 64, 4, 2, 1, bias = False),
nn.LeakyReLU(0.2, inplace = True),
nn.Conv2d(64, 128, 4, 2, 1, bias = False),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, inplace = True),
nn.Conv2d(128, 256, 4, 2, 1, bias = False),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, inplace = True),
nn.Conv2d(256, 512, 4, 2, 1, bias = False),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, inplace = True),
nn.Conv2d(512, 1, 4, 1, 0, bias = False),
nn.Sigmoid()
)
def forward(self, input):
output = self.main(input)
return output.view(-1)
netD = D()
netD.load_state_dict(torch.load('discriminator.pth'))
netD.eval()
#netD.apply(weights_init)
criterion = nn.BCELoss()
checkpoint = torch.load('discriminator.pth')
optimizerD = optim.Adam(netD.parameters(), lr = 0.0002, betas = (0.5, 0.999))
optimizerD.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']
errD = checkpoint['loss']
checkpoint1 = torch.load('genrator.pth')
optimizerG = optim.Adam(netG.parameters(), lr = 0.0002, betas = (0.5, 0.999))
optimizerG.load_state_dict(checkpoint1['optimizer_state_dict'])
errG = checkpoint1['loss']
k = epoch
for j in range(k, 10):
for i, data in enumerate(dataloader, 0):
netD.zero_grad()
real, _ = data
input = Variable(real)
target = Variable(torch.ones(input.size()[0]))
output = netD(input)
errD_real = criterion(output, target)
noise = Variable(torch.randn(input.size()[0], 100, 1, 1))
fake = netG(noise)
target = Variable(torch.zeros(input.size()[0]))
output = netD(fake.detach())
errD_fake = criterion(output, target)
errD = errD_real + errD_fake
errD.backward()
optimizerD.step()
netG.zero_grad()
target = Variable(torch.ones(input.size()[0]))
output = netD(fake)
errG = criterion(output, target)
errG.backward()
optimizerG.step()
print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f' % (epoch+1, 10, i+1, len(dataloader), errD.data, errG.data))
if i % 100 == 0:
vutils.save_image(real, '%s/real_samples.png' % "./results", normalize = True)
fake = netG(noise)
vutils.save_image(fake.data, '%s/fake_samples_epoch_%03d.png' % ("./results", epoch+1), normalize = True)
torch.save({
'epoch': epoch,
'model_state_dict': netD.state_dict(),
'optimizer_state_dict': optimizerD.state_dict(),
'loss': errD
}, 'discriminator.pth')
torch.save({
'epoch': epoch,
'model_state_dict': netG.state_dict(),
'optimizer_state_dict': optimizerG.state_dict(),
'loss': errG
}, 'generator.pth')
这是错误
RuntimeError Traceback (most recent call last)
<ipython-input-23-3e55546152c7> in <module>()
26 # Creating the generator
27 netG = G()
---> 28 netG.load_state_dict(torch.load('generator.pth'))
29 netG.eval()
30 #netG.apply(weights_init)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
767 if len(error_msgs) > 0:
768 raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
--> 769 self.__class__.__name__, "\n\t".join(error_msgs)))
770
771 def _named_members(self, get_members_fn, prefix='', recurse=True):
RuntimeError: Error(s) in loading state_dict for G:
Missing key(s) in state_dict: "main.0.weight", "main.1.weight", "main.1.bias", "main.1.running_mean", "main.1.running_var", "main.3.weight", "main.4.weight", "main.4.bias", "main.4.running_mean", "main.4.running_var", "main.6.weight", "main.7.weight", "main.7.bias", "main.7.running_mean", "main.7.running_var", "main.9.weight", "main.10.weight", "main.10.bias", "main.10.running_mean", "main.10.running_var", "main.12.weight".
Unexpected key(s) in state_dict: "epoch", "model_state_dict", "optimizer_state_dict", "loss".
最佳答案
您需要访问已加载检查点内的'model_state_dict'键。
尝试：
netG.load_state_dict(torch.load('generator.pth')['model_state_dict'])
您可能还需要对鉴别器应用相同的修复程序。
关于image-processing - state_dict中缺少键，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/55744941/