np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:])是什么意思
时间: 2024-05-19 18:16:15 浏览: 11
这行代码是将数组labels4[:, 1:]中的元素限制在0和2*s之间,将小于0的元素变为0,将大于2*s的元素变为2*s。其中,labels4[:, 1:]表示选取labels4数组中所有行的第2列及以后的元素构成的新数组。np.clip是numpy库中的函数,用于将一个数组中的元素限制在一个范围内。out=labels4[:, 1:]表示将结果保存在labels4[:, 1:]中,即原地修改labels4数组。
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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))
```
the element in labels should strictly be 1 or 0, and it must be 1 in sum of a row, Continue to refine the last code and don't add any other packages but numpy.
Here's a refined version of the code that ensures that the elements in the labels array are either 0 or 1 and that the sum of each row is 1:
```
import numpy as np
def label_propagation(xs: np.ndarray, ys: np.ndarray, num_iter: int = 50,
k_type: str = 'rbf', bandwidth: float = 0.1) -> np.ndarray:
n = xs.shape[0]
# Create the affinity matrix
if k_type == 'rbf':
affinity_matrix = np.exp(-np.square(np.linalg.norm(xs[:, None] - xs[None, :], axis=2)) / (2 * bandwidth ** 2))
elif k_type == 'gate':
affinity_matrix = np.where(np.linalg.norm(xs[:, None] - xs[None, :], axis=2) <= bandwidth, 1, 0)
elif k_type == 'triangle':
affinity_matrix = np.maximum(0, 1 - np.linalg.norm(xs[:, None] - xs[None, :], axis=2) / bandwidth)
elif k_type == 'linear':
affinity_matrix = xs @ xs.T
else:
raise ValueError('Invalid kernel type')
# Normalize the affinity matrix
degree_matrix = np.diag(np.sum(affinity_matrix, axis=1))
degree_matrix_inv_sqrt = np.sqrt(np.linalg.inv(degree_matrix))
normalized_affinity_matrix = degree_matrix_inv_sqrt @ affinity_matrix @ degree_matrix_inv_sqrt
# Initialize the labels
labels = ys.copy()
labeled_indices = np.where(ys != 0)[0]
num_labeled = len(labeled_indices)
# Perform label propagation
for i in range(num_iter):
labels = normalized_affinity_matrix @ labels
# Fix the labeled samples
labels[labeled_indices] = ys[labeled_indices]
# Ensure the labels are between 0 and 1
labels = np.clip(labels, 0, 1)
# Ensure the sum of each row is 1
row_sums = np.sum(labels, axis=1)
row_sums[row_sums == 0] = 1
labels = labels / row_sums[:, None]
# Ensure the labeled samples remain fixed
labels[labeled_indices] = ys[labeled_indices]
# Ensure the labels are either 0 or 1
labels = np.where(labels >= 0.5, 1, 0)
return labels
```
This implementation performs the same steps as before, but it additionally ensures that the sum of each row in the labels array is 1 and that the elements in the array are either 0 or 1 using the np.clip and np.where functions. The labeled samples are also fixed throughout the iterations to ensure that they do not change.