if padding: with torch.no_grad():什么意思

这段代码是在使用 PyTorch 框架进行深度学习模型训练时，对输入数据进行填充操作。如果 padding 参数为 True，则使用 torch.no_grad() 上下文管理器来禁用梯度计算，以提高训练效率。

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。

import torch import torch.nn as nn import torch.optim as optim import torchvision.datasets as datasets import torchvision.transforms as transforms # 定义超参数 batch_size = 64 learning_rate = 0.001 num_epochs = 10 # 定义数据预处理 transform = transforms.Compose([ transforms.ToTensor(), # 转换为Tensor类型 transforms.Normalize((0.1307,), (0.3081,)) # 标准化，使得均值为0，标准差为1 ]) # 加载MNIST数据集 train_dataset = datasets.MNIST(root='C:/MNIST', train=True, transform=transform, download=True) test_dataset = datasets.MNIST(root='C:/MNIST', train=False, transform=transform, download=True) train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False) # 定义CNN模型 class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1) self.bn1 = nn.BatchNorm2d(32) self.relu1 = nn.ReLU() self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) self.bn2 = nn.BatchNorm2d(64) self.relu2 = nn.ReLU() self.pool = nn.MaxPool2d(kernel_size=2, stride=2) self.fc1 = nn.Linear(64 * 7 * 7, 128) self.relu3 = nn.ReLU() self.fc2 = nn.Linear(128, 10) def forward(self, x): out = self.conv1(x) out = self.bn1(out) out = self.relu1(out) out = self.conv2(out) out = self.bn2(out) out = self.relu2(out) out = self.pool(out) out = out.view(-1, 64 * 7 * 7) out = self.fc1(out) out = self.relu3(out) out = self.fc2(out) return out # 实例化模型并定义损失函数和优化器 model = CNN() criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) # 训练模型 total_step = len(train_loader) for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): # 前向传播 outputs = model(images) loss = criterion(outputs, labels) # 反向传播和优化 optimizer.zero_grad() loss.backward() optimizer.step() # 每100个batch打印一次训练信息 if (i+1) % 100 == 0: print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' .format(epoch+1, num_epochs, i+1, total_step, loss.item())) # 测试模型 model.eval() # 进入测试模式，关闭Dropout和BatchNormalization层 with torch.no_grad(): correct = 0 total = 0 for images, labels in test_loader: outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))运行一下此代码

好的，运行这段代码需要先安装PyTorch库。如果已经安装了PyTorch库，可以直接在命令行或者Python IDE中运行这段代码，或者将这段代码保存为一个.py文件然后运行。在运行之前，需要将代码中的数据集路径修改为本地路径，可以在代码中找到“root='C:/MNIST'”这一行将其修改为你的本地路径。运行后，模型会进行训练和测试，并输出测试准确率。