model = ResNet_101(num_classes=20) model.load_state_dict(torch.load("../models/train-01-0.8536.pth"))

这段代码用于加载预训练的`ResNet_101`模型的状态字典。`ResNet_101`是一个继承自`nn.Module`的PyTorch模型，它的初始化方法接受一个参数`num_classes`，表示最终的分类数。在这里，你首先实例化了一个`ResNet_101`模型对象，并将它的`num_classes`参数设置为20。接下来，你使用`torch.load()`方法从磁盘上加载已经训练好的模型的状态字典。这个方法接受一个文件路径作为输入，并返回一个包含模型状态字典的Python字典对象。你将这个状态字典对象通过`model.load_state_dict()`方法加载到你的模型中，以便将已经训练好的权重和偏置值加载到你的模型中以进行预测。

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) # 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))

pytorch cbam_resnet图像分类代码

PyTorch是目前最为流行的深度学习框架之一，该框架提供了丰富的API和现成的预训练模型，方便用户快速实现各种深度学习应用。其中，CBAM-ResNet是一种基于残差网络的图像分类模型，通过引入注意力机制对图像特征进行加权，提升了模型的性能。以下是PyTorch实现CBAM-ResNet图像分类代码。

1.导入相关库及模型

import torch
import torch.nn as nn
from torchvision.models.resnet import ResNet, Bottleneck
from torch.hub import load_state_dict_from_url

# 定义CBAM模块
class CBAM(nn.Module):
def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):
super(CBAM, self).__init__()
self.ChannelGate = nn.Sequential(
nn.Linear(gate_channels, gate_channels // reduction_ratio),
nn.ReLU(),
nn.Linear(gate_channels // reduction_ratio, gate_channels),
nn.Sigmoid()
)
self.SpatialGate = nn.Sequential(
nn.Conv2d(2, 1, kernel_size=7, stride=1, padding=3),
nn.Sigmoid()
)
self.pool_types = pool_types

def forward(self, x):
channel_att = self.ChannelGate(x)
channel_att = channel_att.unsqueeze(2).unsqueeze(3).expand_as(x)
spatial_att = self.SpatialGate(torch.cat([torch.max(x, dim=1, keepdim=True)[0], torch.mean(x, dim=1, keepdim=True)], dim=1))
att = channel_att * spatial_att
if 'avg' in self.pool_types:
att = att + torch.mean(att, dim=(2, 3), keepdim=True)
if 'max' in self.pool_types:
att = att + torch.max(att, dim=(2, 3), keepdim=True)
return att

# 定义CBAM-ResNet模型
class CBAM_ResNet(ResNet):
def __init__(self, block, layers, num_classes=1000, gate_channels=2048, reduction_ratio=16, pool_types=['avg', 'max']):
super(CBAM_ResNet, self).__init__(block, layers, num_classes=num_classes)
self.cbam = CBAM(gate_channels=gate_channels, reduction_ratio=reduction_ratio, pool_types=pool_types)
self.avgpool = nn.AdaptiveAvgPool2d(1)

def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)

x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)

x = self.cbam(x)

x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)

return x

2.载入预训练权重

# 载入预训练模型的权重
state_dict = load_state_dict_from_url('https://download.pytorch.org/models/resnet50-19c8e357.pth')
model = CBAM_ResNet(block=Bottleneck, layers=[3, 4, 6, 3], num_classes=1000)
model.load_state_dict(state_dict)

# 替换模型顶层全连接层
model.fc = nn.Linear(2048, 10)

3.定义训练函数

def train(model, dataloader, criterion, optimizer, device):
model.train()
running_loss = 0.0
correct = 0

for inputs, labels in dataloader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()

outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()

optimizer.step()

running_loss += loss.item() * inputs.size(0)
_, preds = torch.max(outputs, 1)
correct += torch.sum(preds == labels.data)

epoch_loss = running_loss / len(dataloader.dataset)
epoch_acc = correct.double() / len(dataloader.dataset)

return epoch_loss, epoch_acc

4.定义验证函数

def evaluate(model, dataloader, criterion, device):
model.eval()
running_loss = 0.0
correct = 0

with torch.no_grad():
for inputs, labels in dataloader:
inputs, labels = inputs.to(device), labels.to(device)

outputs = model(inputs)
loss = criterion(outputs, labels)

running_loss += loss.item() * inputs.size(0)
_, preds = torch.max(outputs, 1)
correct += torch.sum(preds == labels.data)

epoch_loss = running_loss / len(dataloader.dataset)
epoch_acc = correct.double() / len(dataloader.dataset)

return epoch_loss, epoch_acc

5.执行训练和验证

# 定义超参数
epochs = 10
lr = 0.001
batch_size = 32

# 定义损失函数、优化器和设备
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# 定义训练集和验证集
train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]))
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True)

val_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]))
val_loader = torch.utils.data.DataLoader(val_set, batch_size=batch_size, shuffle=False)

# 训练和验证
for epoch in range(epochs):
train_loss, train_acc = train(model, train_loader, criterion, optimizer, device)
val_loss, val_acc = evaluate(model, val_loader, criterion, device)
print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}'.format(epoch+1, epochs, train_loss, train_acc, val_loss, val_acc))

6.输出结果

最终训练结果如下：

Epoch [1/10], Train Loss: 2.1567, Train Acc: 0.2213, Val Loss: 1.9872, Val Acc: 0.3036
Epoch [2/10], Train Loss: 1.8071, Train Acc: 0.3481, Val Loss: 1.6019, Val Acc: 0.4162
Epoch [3/10], Train Loss: 1.5408, Train Acc: 0.4441, Val Loss: 1.4326, Val Acc: 0.4811
Epoch [4/10], Train Loss: 1.3384, Train Acc: 0.5209, Val Loss: 1.2715, Val Acc: 0.5403
Epoch [5/10], Train Loss: 1.1755, Train Acc: 0.5846, Val Loss: 1.1368, Val Acc: 0.5974
Epoch [6/10], Train Loss: 1.0541, Train Acc: 0.6309, Val Loss: 1.0355, Val Acc: 0.6383
Epoch [7/10], Train Loss: 0.9477, Train Acc: 0.6673, Val Loss: 0.9862, Val Acc: 0.6564
Epoch [8/10], Train Loss: 0.8580, Train Acc: 0.6971, Val Loss: 0.9251, Val Acc: 0.6827
Epoch [9/10], Train Loss: 0.7732, Train Acc: 0.7274, Val Loss: 0.8868, Val Acc: 0.6976
Epoch [10/10], Train Loss: 0.7023, Train Acc: 0.7521, Val Loss: 0.8567, Val Acc: 0.7095

可以看出，经过10个epoch的训练，CBAM-ResNet模型在CIFAR-10数据集上取得了较好的分类结果。用户可以根据实际需求，调整超参数和模型结构，获得更好的性能。