这段代码什么意思 def clip_by_tensor(t, t_min, t_max): t = t.float() result = (t >= t_min).float() * t + (t < t_min).float() * t_min result = (result <= t_max).float() * result + (result > t_max).float() * t_max return result

这段代码实现了一个张量的剪裁操作，保证张量在指定的范围内。函数接受三个参数，分别是待剪裁的张量t，剪裁范围的下限t_min和上限t_max。代码的实现方式是先将张量t转换为float类型，然后使用逐元素比较操作符（>=和<）得到两个布尔类型的张量，分别表示每个元素是否在剪裁范围内。接着使用逐元素乘法和加法操作，将不在剪裁范围内的元素替换成对应的边界值。最后返回剪裁后的结果张量result。

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损失函数进行模型训练的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from ldam_loss import LDAMLoss # 导入LDAM损失函数

# 定义模型
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.fc1 = nn.Linear(256 * 4 * 4, 1024)
        self.fc2 = nn.Linear(1024, 10)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.bn3(self.conv3(x)))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 256 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义数据集和数据加载器
train_dataset = ... # 定义训练集
test_dataset = ... # 定义测试集
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False)

# 定义LDAM损失函数
cls_num_list = ... # 定义每个类别的样本数量
criterion = LDAMLoss(cls_num_list, max_m=0.5, s=30)

# 定义优化器和学习率调度器
model = MyModel().to('cuda')
optimizer = optim.Adam(model.parameters(), lr=1e-3)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)

# 训练模型
for epoch in range(50):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to('cuda'), target.to('cuda')
        data, target_a, target_b, lam = mixup_fn(data, target)
        
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target_a, target_b, lam) # 使用LDAM损失函数
        loss.backward()
        nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) # 防止梯度爆炸
        optimizer.step()
        
    model.eval()
    with torch.no_grad():
        total = 0
        correct = 0
        for data, target in test_loader:
            data, target = data.to('cuda'), target.to('cuda')
            output = model(data)
            _, predicted = torch.max(output.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
        acc = 100 * correct / total
        print('Epoch {}: Test Acc = {}%'.format(epoch+1, acc))
    
    scheduler.step()

在以上代码中，我们首先定义了一个名为`MyModel`的模型，然后定义了训练集和测试集的数据加载器。接着，我们使用LDAM损失函数来定义模型的损失函数，然后使用Adam优化器来训练模型。在每个epoch结束后，我们使用测试集来评估模型的性能，并使用学习率调度器来调整学习率。

pytorch部分代码如下：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(0,1)) batch_m = batch_m.view((-1, 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, :] logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True,drop_last=True) test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=True) cls_num_list = np.zeros(classes) for , label in train_loader.dataset: cls_num_list[label] += 1 criterion_train = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) criterion_val = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) 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) for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device, non_blocking=True), Variable(target).to(device,non_blocking=True) # 3、将数据输入mixup_fn生成mixup数据 samples, targets = mixup_fn(data, target) targets = torch.tensor(targets).to(torch.long) # 4、将上一步生成的数据输入model，输出预测结果，再计算loss output = model(samples) # 5、梯度清零（将loss关于weight的导数变成0） optimizer.zero_grad() # 6、若使用混合精度 if use_amp: with torch.cuda.amp.autocast(): # 开启混合精度 loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss scaler.scale(loss).backward() # 梯度放大 torch.nn.utils.clip_grad_norm(model.parameters(), CLIP_GRAD) # 梯度裁剪，防止梯度爆炸 scaler.step(optimizer) # 更新下一次迭代的scaler scaler.update() # 否则，直接反向传播求梯度 else: loss = criterion_train(output, targets) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) optimizer.step() 报错：RuntimeError: Expected index [112, 1] to be smaller than self [16, 7] apart from dimension 1

这个报错是因为在计算batch_m时，index_float的维度是反过来的，应该将index_float.transpose(0,1)改为index_float.transpose(1,0)，即将第0维和第1维交换。修改后的代码如下：

...
batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0))
...

你可以尝试修改后再运行看看是否还会出现报错。