我怎么能让这代码在GPU上运行，该怎么改呢？我试了好多方法了。
#主程序文件，用于训练模型
import argparse
import os
import numpy as np
import math
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
# import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets, models, transforms
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from tools.my_dataset import myDataset
import torch
os.makedirs("train_images", exist_ok=True)
os.makedirs("save_Model", exist_ok=True)
PATH='E:\GAN'
parser = argparse.ArgumentParser()
parser.add_argument("--n_epochs", type=int, default=600, help="number of epochs of training")
parser.add_argument("--batch_size", type=int, default=4, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--n_cpu", type=int, default=4, help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
parser.add_argument("--img_size", type=int, default=500, help="size of each train_images dimension")
parser.add_argument("--channels", type=int, default=3, help="number of train_images channels")
parser.add_argument("--sample_interval", type=int, default=400, help="interval betwen train_images samples")
opt = parser.parse_args()
print(opt)
img_shape = (opt.channels, opt.img_size, opt.img_size)
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
def block(in_feat, out_feat, normalize=True):
layers = [nn.Linear(in_feat, out_feat)]
if normalize:
layers.append(nn.BatchNorm1d(out_feat, 0.8))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
self.model = nn.Sequential(
*block(opt.latent_dim, 128, normalize=False),
*block(128, 256),
*block(256, 512),
*block(512, 1024),
nn.Linear(1024, int(np.prod(img_shape))),
nn.Tanh()
)
def forward(self, z):
img = self.model(z)
img = img.view(img.size(0), *img_shape)
return img
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.model = nn.Sequential(
nn.Linear(int(np.prod(img_shape)), 512),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(512, 256),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(256, 1),
nn.Sigmoid(),
)
def forward(self, img):
img_flat = img.view(img.size(0), -1)
validity = self.model(img_flat)
return validity
# 损失函数
adversarial_loss = torch.nn.BCELoss()
#定义生成器和判别器
generator = Generator()
discriminator = Discriminator()
dataset = r'E:\GAN'
train_data_directory = os.path.join(dataset, 'train_data')
image_transforms = {
'train_data': transforms.Compose([
transforms.Resize([opt.img_size,opt.img_size]),
transforms.ToTensor(),
])
}
data = {
'train_data': myDataset(data_dir=train_data_directory,
transform=image_transforms['train_data'])
}
dataloader = DataLoader(data['train_data'], batch_size=opt.batch_size, shuffle=True)
train_data_data_size = len(data['train_data'])
print('train_size: {:4d} '.format(train_data_data_size))
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
Tensor = torch.FloatTensor
# ----------
# 开始训练
# ----------
ans=0
for epoch in range(opt.n_epochs):
# for i, (imgs, _) in enumerate(dataloader):
for i, imgs in enumerate(dataloader):
#真实图像来自于train_data
# Adversarial ground truths
valid = Variable(Tensor(imgs.size(0), 1).fill_(1.0), requires_grad=False)
fake = Variable(Tensor(imgs.size(0), 1).fill_(0.0), requires_grad=False)
# 真实图像
real_imgs = Variable(imgs.type(Tensor))
# -----------------
# 训练生成器
# -----------------
optimizer_G.zero_grad()
# 噪声输入
z = Variable(Tensor(np.random.normal(0, 3, (imgs.shape[0], opt.latent_dim))))
# 生成图像
gen_imgs = generator(z)
# 计算G_损失
aa = discriminator(gen_imgs)
g_loss = adversarial_loss(aa, valid)
g_loss.backward()
optimizer_G.step()
# ---------------------
# 训练判别器
# ---------------------
optimizer_D.zero_grad()
# 测量判别器检测假图像的能力
bb = discriminator(real_imgs)
real_loss = adversarial_loss(bb, valid)
# 此处需要注意，detach()是为了截断梯度流，不计算生成网络的损失，
# 因为d_loss包含了fake_loss，回传的时候如果不做处理，默认会计算generator的梯度，
# 而这里只需要计算判别网络的梯度，更新其权重值，生成网络保持不变即可。
fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake)
d_loss = (real_loss + fake_loss) / 2
d_loss.backward()
optimizer_D.step()
print(
"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"
% (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item())
)
batches_done = epoch * len(dataloader) + i
if batches_done % 4000 == 0:
save_image(gen_imgs.data[:25], "E:\GAN\\train_images\%d.png" % batches_done, nrow=5, normalize=True)
#保存模型
ans+=1
torch.save(generator, './save_Model/generator%d.pth' %ans)
要将代码在GPU上运行，你需要进行以下更改：
在代码的开始部分，添加以下代码将torch转换为GPU操作：
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
generator.to(device)
discriminator.to(device)
adversarial_loss.to(device)
Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
更改数据加载器的参数pin_memory=True，以启用数据在GPU上的预加载：
dataloader = DataLoader(data['train_data'], batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu, pin_memory=True)
在循环的开始部分，将输入和目标张量移动到GPU上：
real_imgs = real_imgs.to(device)
imgs = imgs.to(device)
valid = valid.to(device)
fake = fake.to(device)
在生成器和判别器的优化器的定义中，添加参数device，以启用模型参数在GPU上的更新：
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_G.to(device)
optimizer_D.to(device)
在训练循环的最后，将生成的图像张量移动到CPU上，并使用save_image()保存图像：
save_image(gen_imgs.data[:25].cpu(), "E:\GAN\\train_images\%d.png" % batches_done, nrow=5, normalize=True)
请注意，为了在GPU上运行代码，你的系统必须具有支持CUDA的GPU，并且已正确安装CUDA驱动程序和PyTorch的CUDA版本。如若有用，还望博友采纳！