self.mixup_scale = (0.5, 1.5)
时间: 2024-06-02 15:12:18 浏览: 2
这行代码可能是在进行数据增强时使用的。mixup是一种数据增强技术,其基本思想是将两张图片进行线性组合,产生一个新的样本。这个新的样本的标签也是两张原始图片标签的线性组合。mixup_scale指定了线性组合时第一张图片的权重范围,这里的(0.5, 1.5)表示第一张图片的权重在0.5到1.5之间取值。这个范围的取值可以根据实验结果来调整,以获得更好的分类效果。
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:
```python
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((-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) classes=7, 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) 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() 报错:File "/home/adminis/hpy/ConvNextV2_Demo/models/losses.py", line 53, in forward return F.cross_entropy(logit, target, weight=self.weight) File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/functional.py", line 2824, in cross_entropy return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) RuntimeError: multi-target not supported at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15
这个报错是因为 `target` 中包含了多个标签,而 `F.cross_entropy` 函数只支持单标签的情况。你可以将 `target` 转换成一个 1D 的 Tensor,其中每个元素表示一个样本的类别标签。可以使用 `torch.flatten()` 或者 `torch.view()` 来实现这个转换。修改后的代码如下:
```python
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((-1, 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)
```
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