if torch.cuda.is_available(): torch.cuda.manual_seed(123) if USE_CUDA: torch.cuda.manual_seed(123)
时间: 2024-03-31 11:30:34 浏览: 32
这段代码是用于检查是否可用CUDA加速,并设置随机种子的条件语句。具体解释如下:
1. `if torch.cuda.is_available():`:这个条件语句用于检查当前环境是否支持CUDA加速。`torch.cuda.is_available()`函数返回一个布尔值,如果CUDA可用,则返回True,否则返回False。
2. `torch.cuda.manual_seed(123)`:这行代码用于设置CUDA的随机种子。随机种子是一个用于生成随机数序列的起始值,通过设置相同的随机种子,可以确保每次运行时生成的随机数序列是相同的。
3. `if USE_CUDA:`:这个条件语句是一个自定义的变量`USE_CUDA`的判断条件。如果`USE_CUDA`为True,则执行下面的代码块,否则跳过。
综上所述,这段代码的作用是在CUDA可用的情况下,设置CUDA的随机种子为123。如果`USE_CUDA`为True,则也会执行相同的操作。
相关问题
torch.cuda.manual_seed_all(seed)
This function sets the seed for all available GPUs in the system. The seed is used to initialize the random number generator for CUDA computations.
All random number generators in PyTorch are deterministic by default, so setting the seed ensures that the same sequence of random numbers is generated every time the code is run. This is useful for reproducibility and debugging purposes.
The input parameter `seed` is an integer value that is used to initialize the random number generator. It can be any integer value, but it is recommended to use a fixed value for reproducibility purposes.
LDAM损失函数pytorch代码如下:class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((16, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] target = torch.flatten(target) # 将 target 转换成 1D Tensor logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) 模型部分参数如下:# 设置全局参数 model_lr = 1e-5 BATCH_SIZE = 16 EPOCHS = 50 DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') use_amp = True use_dp = True classes = 7 resume = None CLIP_GRAD = 5.0 Best_ACC = 0 #记录最高得分 use_ema=True model_ema_decay=0.9998 start_epoch=1 seed=1 seed_everything(seed) # 数据增强 mixup mixup_fn = Mixup( mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None, prob=0.1, switch_prob=0.5, mode='batch', label_smoothing=0.1, num_classes=classes) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数
好的,以下是使用 LDAM 损失函数训练模型的代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets, transforms
from ldam_loss import LDAMLoss # 假设你已经将 LDAM 损失函数的代码存放在 ldam_loss.py 文件中
# 设置全局参数
model_lr = 1e-5
BATCH_SIZE = 16
EPOCHS = 50
DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
use_amp = True
use_dp = True
classes = 7
resume = None
CLIP_GRAD = 5.0
Best_ACC = 0
use_ema = True
model_ema_decay = 0.9998
start_epoch = 1
seed = 1
# 设置随机种子
def seed_everything(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
seed_everything(seed)
# 数据增强 mixup
mixup_fn = Mixup(
mixup_alpha=0.8,
cutmix_alpha=1.0,
cutmix_minmax=None,
prob=0.1,
switch_prob=0.5,
mode='batch',
label_smoothing=0.1,
num_classes=classes
)
# 加载数据集
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('./data', train=True, download=True, transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])),
batch_size=BATCH_SIZE, shuffle=True, num_workers=4, pin_memory=True
)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('./data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])),
batch_size=BATCH_SIZE, shuffle=False, num_workers=4, pin_memory=True
)
# 定义模型
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
# 初始化模型和优化器
model = Net().to(DEVICE)
optimizer = optim.Adam(model.parameters(), lr=model_lr)
# 如果 resume 不为空,则从指定的 checkpoint 恢复模型和优化器
if resume is not None:
checkpoint = torch.load(resume)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
start_epoch = checkpoint['epoch'] + 1
Best_ACC = checkpoint['Best_ACC']
print(f"Resuming from checkpoint {resume}, epoch {start_epoch}")
# 使用 LDAM 损失函数
cls_num_list = [1000] * classes
criterion = LDAMLoss(cls_num_list, max_m=0.5, s=30).to(DEVICE)
# 训练模型
for epoch in range(start_epoch, EPOCHS + 1):
train_loss = 0
train_acc = 0
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(DEVICE), target.to(DEVICE)
data, target_a, target_b, lam = mixup_fn(data, target) # mixup 增强
optimizer.zero_grad()
output = model(data)
loss = lam * criterion(output, target_a) + (1 - lam) * criterion(output, target_b) # 计算 mixup 后的损失函数
loss.backward()
if CLIP_GRAD:
torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) # 梯度裁剪
optimizer.step()
train_loss += loss.item() * data.size(0)
pred = output.argmax(dim=1, keepdim=True)
train_acc += pred.eq(target.view_as(pred)).sum().item()
train_loss /= len(train_loader.dataset)
train_acc /= len(train_loader.dataset)
# 计算测试集上的损失和准确率
test_loss = 0
test_acc = 0
model.eval()
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(DEVICE), target.to(DEVICE)
output = model(data)
test_loss += F.cross_entropy(output, target, reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
test_acc += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
test_acc /= len(test_loader.dataset)
# 更新 Best_ACC 并保存模型
if test_acc > Best_ACC:
Best_ACC = test_acc
state = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch,
'Best_ACC': Best_ACC
}
torch.save(state, f'model_epoch_{epoch}_acc_{Best_ACC:.4f}.pth')
# 打印训练日志
print(f"Epoch {epoch}/{EPOCHS}: train_loss={train_loss:.4f} train_acc={train_acc:.4f} test_loss={test_loss:.4f} test_acc={test_acc:.4f}")
```
这里使用的是 CIFAR-10 数据集和一个简单的卷积神经网络模型,你需要将其替换成你自己的数据集和模型。
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