torch.flatten的参数设置

torch.flatten函数的参数设置如下：

torch.flatten(input, start_dim=0, end_dim=-1)

其中，input是要被压平的张量；

start_dim是指定从哪一维开始压平，默认值是0，即从第一个维度开始；

end_dim是指定压平的终止维度，默认值是-1，即压平到最后一维。

相关问题

def find_preferences_2d(self, *losses): assert len(losses) >= 2 grads = [] for loss in losses: self.optimizer_actor.zero_grad() self.optimizer_critic.zero_grad() grad = torch.autograd.grad(loss, self.model.actor.parameters(), retain_graph=True, create_graph=self.adaptive)[0] torch.nn.utils.clip_grad_norm_(self.model.actor.parameters(), self.max_grad_norm) grad = torch.flatten(grad) grad = torch.squeeze(grad) grads.append(grad) total_grad = grads[1] - grads[0] print("total_grad:",total_grad) nom = torch.dot(total_grad, grads[0]) #相同类型矩阵做点积 den = torch.norm(total_grad) ** 2 eps = nom/(den + self.adam_eps) eps = torch.clamp(eps, 0, 1) pareto_loss = eps*grads[0] + (1-eps)*grads[1] pareto_loss = torch.norm(pareto_loss) ** 2 return [1-eps, eps], pareto_loss

这段代码实现了一个二维偏好查找函数（_preferences_2d）。它接收任意数量损失函数（losses）作为参数，但至少需要两个损失函数首先，代码创建一个空列表grad，用于存储损失函数对模型参数的梯度。

然后，使用一个循环遍历每个损失函数。在每个迭代中，代码先将模型的actor和critic优化器的梯度清零，然后使用torch.autograd.grad函数计算损失函数对actor参数的梯度。其中，retain_graph参数设置为True表示保留计算图以供后续使用，create_graph参数根据self.adaptive的值决定是否创建计算图。接着，代码使用torch.nn.utils.clip_grad_norm_函数对actor参数的梯度进行裁剪，以防止梯度爆炸问题。最后，代码将梯度展平成一维张量，并将其添加到grads列表中。

接下来，代码计算总梯度（total_grad），即第二个损失函数的梯度减去第一个损失函数的梯度。然后，代码使用torch.dot函数计算total_grad和grads[0]之间的点积（内积）。接着，代码计算total_grad的范数的平方，并将其保存在den变量中。

接下来，代码计算一个比例系数eps，用于加权求和grads[0]和grads[1]以得到pareto_loss。eps的计算公式为eps = nom / (den + self.adam_eps)，其中nom是total_grad和grads[0]的点积，self.adam_eps是一个小的常数，用于避免除零错误。然后，代码使用torch.clamp函数将eps限制在0和1之间。

最后，代码计算pareto_loss，即eps乘以grads[0]加上(1-eps)乘以grads[1]的范数的平方。

函数返回一个长度为2的列表，其中第一个元素是[1-eps, eps]，第二个元素是pareto_loss。

总体而言，这段代码是用于在二维空间中查找偏好的函数。它通过计算损失函数的梯度差异和权重系数来确定最佳的权衡解。

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))