device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') torch.manual_seed(2020)
时间: 2023-12-16 20:25:12 浏览: 39
这段代码的作用是设置PyTorch的运行设备为GPU(如果可用)或CPU,并设置随机数种子为2020。其中,引用和引用都是设置设备的代码,而引用则是检查可用GPU数量和是否可用GPU的代码。设置设备为GPU可以加速模型的训练和推理,但需要注意的是,如果GPU显存不足或者模型过于复杂,可能会导致程序崩溃。设置随机数种子可以保证每次运行程序时生成的随机数相同,有利于结果的可重复性和调试。
相关问题
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。
try: import thop except ImportError: thop = None logger = logging.getLogger(__name__) @contextmanager def torch_distributed_zero_first(local_rank: int): if local_rank not in [-1, 0]: torch.distributed.barrier() yield if local_rank == 0: torch.distributed.barrier() def init_torch_seeds(seed=0): torch.manual_seed(seed) if seed == 0: cudnn.benchmark, cudnn.deterministic = False, True else: cudnn.benchmark, cudnn.deterministic = True, False def select_device(device='', batch_size=None): s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' cpu = device.lower() == 'cpu' if cpu: os.environ['CUDA_VISIBLE_DEVICES'] = '-1' elif device: # non-cpu device requested os.environ['CUDA_VISIBLE_DEVICES'] = device assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' cuda = not cpu and torch.cuda.is_available() if cuda: n = torch.cuda.device_count() if n > 1 and batch_size: # check that batch_size is compatible with device_count assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' space = ' ' * len(s) for i, d in enumerate(device.split(',') if device else range(n)): p = torch.cuda.get_device_properties(i) s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n" s += 'CPU\n' logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe return torch.device('cuda:0' if cuda else 'cpu') def time_synchronized(): if torch.cuda.is_available(): torch.cuda.synchronize() return time.time()
这段代码是一个Python脚本,包含了一些用于PyTorch训练的实用函数。具体来说,它包含了以下函数:
- import thop:导入PyTorch的模块thop,用于计算模型的FLOPs和参数数量。
- torch_distributed_zero_first:用于在分布式训练中同步所有进程的执行顺序。
- init_torch_seeds:用于初始化PyTorch的随机数种子。
- select_device:用于选择PyTorch的设备,可以选择CPU或GPU。
- time_synchronized:用于在CPU和GPU之间同步时间。
这些函数都是用于方便PyTorch训练的实用函数,可以大大简化代码编写。
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