loss = nn.CrossEntropyLoss() optimizer = t.optim.Adam(resnet.parameters())

这是一个 PyTorch 中进行深度学习模型训练时常用的代码片段。

`nn.CrossEntropyLoss()` 是一个损失函数，用于计算分类问题中预测结果与真实标签之间的差异，并输出一个标量值作为损失值。

`optim.Adam()` 是一个优化器，用于更新神经网络模型中的参数，以最小化损失函数。Adam 是一种常用的自适应学习率优化算法。

`resnet.parameters()` 是指定需要更新的参数，这里是 ResNet 网络的所有参数。

这段代码的作用是定义了一个使用 CrossEntropyLoss 损失函数和 Adam 优化器对 ResNet 网络进行训练的过程。

相关问题

loss = smp.utils.losses.DiceLoss()，metrics = [ smp.utils.metrics.IoU(threshold=0.5), ] optimizer = torch.optim.Adam([ dict(params=model1.parameters(), lr=0.0001), ])请输出unet分割训练及验证代码

以下是unet分割训练及验证代码：

# 导入必要的库
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import segmentation_models_pytorch as smp

# 定义模型
model = smp.Unet(
encoder_name="resnet18", # 使用resnet18作为编码器
encoder_weights="imagenet", # 使用预训练的imagenet权重
in_channels=3, # 输入通道数为3
classes=1 # 输出通道数为1
)

# 定义损失函数、优化器和评价指标
loss = smp.utils.losses.DiceLoss()
metrics = [
smp.utils.metrics.IoU(threshold=0.5),
]
optimizer = torch.optim.Adam([
dict(params=model.parameters(), lr=0.0001),
])

# 定义训练和验证函数
def train(model, dataloader, loss_fn, optimizer):
model.train()
train_loss = 0.0
train_iou = 0.0
for inputs, targets in dataloader:
inputs = inputs.cuda()
targets = targets.cuda()
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_fn(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
train_iou += metrics[0](outputs, targets).item()
train_loss /= len(dataloader)
train_iou /= len(dataloader)
return train_loss, train_iou

def validate(model, dataloader, loss_fn, metrics):
model.eval()
val_loss = 0.0
val_iou = 0.0
with torch.no_grad():
for inputs, targets in dataloader:
inputs = inputs.cuda()
targets = targets.cuda()
outputs = model(inputs)
loss = loss_fn(outputs, targets)
val_loss += loss.item()
val_iou += metrics[0](outputs, targets).item()
val_loss /= len(dataloader)
val_iou /= len(dataloader)
return val_loss, val_iou

# 加载数据集
train_dataset = MyDataset(train_data)
val_dataset = MyDataset(val_data)
train_dataloader = DataLoader(train_dataset, batch_size=16, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=16, shuffle=False)

# 开始训练
for epoch in range(10):
train_loss, train_iou = train(model, train_dataloader, loss, optimizer)
val_loss, val_iou = validate(model, val_dataloader, loss, metrics)
print("Epoch: {}, Train Loss: {:.4f}, Train IoU: {:.4f}, Val Loss: {:.4f}, Val IoU: {:.4f}".format(epoch+1, train_loss, train_iou, val_loss, val_iou))

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) # 读取数据集 dataset_train = datasets.ImageFolder('/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/train', transform=transform) dataset_test = datasets.ImageFolder("/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/valid", transform=transform_test) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数

好的，下面是使用LDAM损失函数训练模型的代码：

import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.utils.data import DataLoader

# 定义LDAM损失函数
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((x.size(0), 1)) # size=(batch_size, 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)

# 定义模型
model = models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, classes)
model.to(DEVICE)

# 定义优化器和学习率调整器
optimizer = optim.Adam(model.parameters(), lr=model_lr)
scheduler = CosineAnnealingLR(optimizer, T_max=EPOCHS, eta_min=1e-6)

# 定义LDAM损失函数
cls_num_list = [len(dataset_train[dataset_train.targets == t]) for t in range(classes)]
criterion = LDAMLoss(cls_num_list)

# 定义数据加载器
train_loader = DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True, num_workers=4, pin_memory=True)
test_loader = DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False, num_workers=4, pin_memory=True)

# 训练模型
best_acc = 0.0
for epoch in range(start_epoch, EPOCHS + 1):
    model.train()
    train_loss = 0.0
    train_corrects = 0
    for inputs, labels in train_loader:
        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
        if use_dp:
            inputs, labels = dp(inputs, labels)
        if use_amp:
            with amp.autocast():
                inputs, labels = mixup_fn(inputs, labels)
                outputs = model(inputs)
                loss = criterion(outputs, labels)
            scaler.scale(loss).backward()
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD)
            scaler.step(optimizer)
            scaler.update()
        else:
            inputs, labels_a, labels_b, lam = mixup_fn(inputs, labels)
            outputs = model(inputs)
            loss = mixup_criterion(criterion, outputs, labels_a, labels_b, lam)
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD)
            optimizer.step()
            optimizer.zero_grad()
        train_loss += loss.item() * inputs.size(0)
        _, preds = torch.max(outputs, 1)
        train_corrects += torch.sum(preds == labels.data)
    train_loss /= len(dataset_train)
    train_acc = train_corrects.double() / len(dataset_train)

    model.eval()
    test_loss = 0.0
    test_corrects = 0
    with torch.no_grad():
        for inputs, labels in test_loader:
            inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            test_loss += loss.item() * inputs.size(0)
            _, preds = torch.max(outputs, 1)
            test_corrects += torch.sum(preds == labels.data)
    test_loss /= len(dataset_test)
    test_acc = test_corrects.double() / len(dataset_test)

    # 更新最佳模型
    if test_acc > best_acc:
        if use_ema:
            ema_model.load_state_dict(model.state_dict())
        best_acc = test_acc

    # 更新学习率
    scheduler.step()

    # 打印训练结果
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'.format(
        epoch, EPOCHS, train_loss, train_acc, test_loss, test_acc))