交叉熵 IndexError 维度超出范围
Cross entropy IndexError Dimension out of range
我正在尝试在一些图像中训练 GAN，我按照 pytorch 页面上的教程进行操作并获得了以下代码，但是在训练过程中应用交叉熵函数时，它 return 出现了错误代码下方：
import random
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as torch_dataset
import torchvision.transforms as transforms
import torchvision.utils as vutils
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML
seed = 1
print("Random Seed: ", seed)
random.seed(seed)
torch.manual_seed(seed)
images_folder_path = "./images/"
batch_size = 128
image_size = 256
n_channels = 1
z_vector = 100
n_features_generator = 64
n_features_discriminator = 64
num_epochs = 5
lr = 0.0002
beta1 = 0.5
dataset = torch_dataset.ImageFolder(
root=images_folder_path, transform=transforms.Compose(
[
transforms.Grayscale(num_output_channels=1),
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(0.5, 0.5)
]
)
)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0)
device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose2d(z_vector, n_features_generator * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(n_features_generator * 8),
nn.ReLU(True),
nn.ConvTranspose2d(n_features_generator * 8, n_features_generator * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_generator * 4),
nn.ReLU(True),
nn.ConvTranspose2d(n_features_generator * 4, n_features_generator * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_generator * 2),
nn.ReLU(True),
nn.ConvTranspose2d(n_features_generator * 2, n_features_generator, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_generator),
nn.ReLU(True),
nn.ConvTranspose2d(n_features_generator, n_channels, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, inputs):
return self.main(inputs)
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(n_channels, n_features_discriminator, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(n_features_discriminator, n_features_discriminator * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_discriminator * 2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(n_features_discriminator * 2, n_features_discriminator * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_discriminator * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(n_features_discriminator * 4, n_features_discriminator * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_discriminator * 8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(n_features_discriminator * 8, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, inputs):
return self.main(inputs)
netG = Generator().to(device)
if device.type == 'cuda':
netG = nn.DataParallel(netG)
netG.apply(weights_init)
print(netG)
netD = Discriminator().to(device)
if device.type == 'cuda':
netD = nn.DataParallel(netD)
netD.apply(weights_init)
print(netD)
criterion = nn.CrossEntropyLoss()
fixed_noise = torch.randn(64, z_vector, 1, 1, device=device)
real_label = 1.
fake_label = 0.
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
img_list = []
G_losses = []
D_losses = []
iters = 0
print("Starting Training Loop...")
for epoch in range(num_epochs):
for i, data in enumerate(dataloader, 0):
netD.zero_grad()
real_cpu = data[0].to(device)
b_size = real_cpu.size(0)
label = torch.full((b_size,), real_label, dtype=torch.float, device=device)
output = netD(real_cpu).view(-1)
# ----------------------------------------------------------------------------------
errD_real = criterion(output, label) # ERROR HAPPENS HERE
# ----------------------------------------------------------------------------------
errD_real.backward()
D_x = output.mean().item()
noise = torch.randn(b_size, z_vector, 1, 1, device=device)
fake = netG(noise)
label.fill_(fake_label)
output = netD(fake.detach()).view(-1)
errD_fake = criterion(output, label)
errD_fake.backward()
D_G_z1 = output.mean().item()
errD = errD_real + errD_fake
optimizerD.step()
netG.zero_grad()
label.fill_(real_label)
output = netD(fake).view(-1)
errG = criterion(output, label)
errG.backward()
D_G_z2 = output.mean().item()
optimizerG.step()
if i % 50 == 0:
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
% (epoch, num_epochs, i, len(dataloader),
errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))
G_losses.append(errG.item())
D_losses.append(errD.item())
if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
with torch.no_grad():
fake = netG(fixed_noise).detach().cpu()
img_list.append(vutils.make_grid(fake, padding=2, normalize=True))
iters += 1
错误：
追溯（最近调用最后）：
文件“G:/Pastas Estruturadas/Conhecimento/CEFET/IA/SpectroGAN/dcgan.py”，第 137 行，在
errD_real = 标准（输出，标签）
文件“C:\Users\Ramon\anaconda3\envs\vision\lib\site-packages\torch\nn\modules\module.py”，第 722 行，在 _call_impl
中
结果 = self.forward(*输入, **kwargs)
文件“C:\Users\Ramon\anaconda3\envs\vision\lib\site-packages\torch\nn\modules\loss.py”，第 948 行，向前
ignore_index=self.ignore_index,减少=self.reduction)
文件“C:\Users\Ramon\anaconda3\envs\vision\lib\site-packages\torch\nn\functional.py”，第 2422 行，在 cross_entropy
中
return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, 减少)
文件“C:\Users\Ramon\anaconda3\envs\vision\lib\site-packages\torch\nn\functional.py”，第 1591 行，在 log_softmax
中
ret = input.log_softmax（昏暗）
IndexError: 维度超出范围（预期在 [-1, 0] 范围内，但得到 1）
进程已完成，退出代码为 1
您的模型输出与您的标准不一致。
如果要保留模型并更改标准：
使用BCELoss instead of CrossEntropyLoss. Note: You will need to cast your labels to float before passing them in. Also consider removing the Sigmoid() from the model and using BCEWithLogitsLoss.
如果要保留标准并更改模型：
CrossEntropyLoss 期望形状 (..., num_classes)。因此，对于您的 2 class 案例（真假），您必须为批处理中的每个图像预测 2 个值，这意味着您将需要更改模型中最后一层的输出通道。它还需要原始 logits，因此您应该删除 Sigmoid().
我正在尝试在一些图像中训练 GAN，我按照 pytorch 页面上的教程进行操作并获得了以下代码，但是在训练过程中应用交叉熵函数时，它 return 出现了错误代码下方：
import random
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as torch_dataset
import torchvision.transforms as transforms
import torchvision.utils as vutils
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML
seed = 1
print("Random Seed: ", seed)
random.seed(seed)
torch.manual_seed(seed)
images_folder_path = "./images/"
batch_size = 128
image_size = 256
n_channels = 1
z_vector = 100
n_features_generator = 64
n_features_discriminator = 64
num_epochs = 5
lr = 0.0002
beta1 = 0.5
dataset = torch_dataset.ImageFolder(
root=images_folder_path, transform=transforms.Compose(
[
transforms.Grayscale(num_output_channels=1),
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(0.5, 0.5)
]
)
)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0)
device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose2d(z_vector, n_features_generator * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(n_features_generator * 8),
nn.ReLU(True),
nn.ConvTranspose2d(n_features_generator * 8, n_features_generator * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_generator * 4),
nn.ReLU(True),
nn.ConvTranspose2d(n_features_generator * 4, n_features_generator * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_generator * 2),
nn.ReLU(True),
nn.ConvTranspose2d(n_features_generator * 2, n_features_generator, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_generator),
nn.ReLU(True),
nn.ConvTranspose2d(n_features_generator, n_channels, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, inputs):
return self.main(inputs)
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(n_channels, n_features_discriminator, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(n_features_discriminator, n_features_discriminator * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_discriminator * 2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(n_features_discriminator * 2, n_features_discriminator * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_discriminator * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(n_features_discriminator * 4, n_features_discriminator * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(n_features_discriminator * 8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(n_features_discriminator * 8, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, inputs):
return self.main(inputs)
netG = Generator().to(device)
if device.type == 'cuda':
netG = nn.DataParallel(netG)
netG.apply(weights_init)
print(netG)
netD = Discriminator().to(device)
if device.type == 'cuda':
netD = nn.DataParallel(netD)
netD.apply(weights_init)
print(netD)
criterion = nn.CrossEntropyLoss()
fixed_noise = torch.randn(64, z_vector, 1, 1, device=device)
real_label = 1.
fake_label = 0.
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
img_list = []
G_losses = []
D_losses = []
iters = 0
print("Starting Training Loop...")
for epoch in range(num_epochs):
for i, data in enumerate(dataloader, 0):
netD.zero_grad()
real_cpu = data[0].to(device)
b_size = real_cpu.size(0)
label = torch.full((b_size,), real_label, dtype=torch.float, device=device)
output = netD(real_cpu).view(-1)
# ----------------------------------------------------------------------------------
errD_real = criterion(output, label) # ERROR HAPPENS HERE
# ----------------------------------------------------------------------------------
errD_real.backward()
D_x = output.mean().item()
noise = torch.randn(b_size, z_vector, 1, 1, device=device)
fake = netG(noise)
label.fill_(fake_label)
output = netD(fake.detach()).view(-1)
errD_fake = criterion(output, label)
errD_fake.backward()
D_G_z1 = output.mean().item()
errD = errD_real + errD_fake
optimizerD.step()
netG.zero_grad()
label.fill_(real_label)
output = netD(fake).view(-1)
errG = criterion(output, label)
errG.backward()
D_G_z2 = output.mean().item()
optimizerG.step()
if i % 50 == 0:
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
% (epoch, num_epochs, i, len(dataloader),
errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))
G_losses.append(errG.item())
D_losses.append(errD.item())
if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
with torch.no_grad():
fake = netG(fixed_noise).detach().cpu()
img_list.append(vutils.make_grid(fake, padding=2, normalize=True))
iters += 1
错误：
追溯（最近调用最后）：
文件“G:/Pastas Estruturadas/Conhecimento/CEFET/IA/SpectroGAN/dcgan.py”，第 137 行，在 errD_real = 标准（输出，标签）
文件“C:\Users\Ramon\anaconda3\envs\vision\lib\site-packages\torch\nn\modules\module.py”，第 722 行，在 _call_impl
中结果 = self.forward(*输入, **kwargs)
文件“C:\Users\Ramon\anaconda3\envs\vision\lib\site-packages\torch\nn\modules\loss.py”，第 948 行，向前
ignore_index=self.ignore_index,减少=self.reduction)
文件“C:\Users\Ramon\anaconda3\envs\vision\lib\site-packages\torch\nn\functional.py”，第 2422 行，在 cross_entropy
中return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, 减少)
文件“C:\Users\Ramon\anaconda3\envs\vision\lib\site-packages\torch\nn\functional.py”，第 1591 行，在 log_softmax
中ret = input.log_softmax（昏暗）
IndexError: 维度超出范围（预期在 [-1, 0] 范围内，但得到 1）
进程已完成，退出代码为 1
您的模型输出与您的标准不一致。
如果要保留模型并更改标准：
使用BCELoss instead of CrossEntropyLoss. Note: You will need to cast your labels to float before passing them in. Also consider removing the Sigmoid() from the model and using BCEWithLogitsLoss.
如果要保留标准并更改模型：
CrossEntropyLoss 期望形状 (..., num_classes)。因此，对于您的 2 class 案例（真假），您必须为批处理中的每个图像预测 2 个值，这意味着您将需要更改模型中最后一层的输出通道。它还需要原始 logits，因此您应该删除 Sigmoid().