state = {'models': best_model.state_dict()}中的state_dict()是什么意思
时间: 2023-10-30 15:51:55 浏览: 27
在 PyTorch 中,`state_dict()` 是一个函数,用于返回包含模型所有参数(weights和biases等)及其对应值的字典。这个函数返回的字典可以被保存到文件中,或者用于模型恢复。
在 `state = {'models': best_model.state_dict()}` 中,我们将模型的 `state_dict()` 保存在一个字典中,以便后面可以使用 `torch.load()` 函数加载模型。这是一种保存和恢复 PyTorch 模型的常用方式。
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))
```
根据以下代码,利用shap库写出绘制bar plot图的代码“def five_fold_train(x: pd.DataFrame, y: pd.DataFrame, model_class: type, super_parameters: dict = None, return_model=False): """ 5折交叉验证训练器 :param x: :param y: :param model_class: 学习方法类别,传入一个类型 :param super_parameters: 超参数 :param return_model: 是否返回每个模型 :return: list of [pred_y,val_y,auc,precision,recall] """ res = [] models = [] k_fold = KFold(5, random_state=456, shuffle=True) for train_index, val_index in k_fold.split(x, y): #即对数据进行位置索引,从而在数据表中提取出相应的数据 train_x, train_y, val_x, val_y = x.iloc[train_index], y.iloc[train_index], x.iloc[val_index], y.iloc[val_index] if super_parameters is None: super_parameters = {} model = model_class(**super_parameters).fit(train_x, train_y) pred_y = model.predict(val_x) auc = metrics.roc_auc_score(val_y, pred_y) precision = metrics.precision_score(val_y, (pred_y > 0.5) * 1) recall = metrics.recall_score(val_y, (pred_y > 0.5) * 1) res.append([pred_y, val_y, auc, precision, recall]) models.append(model) # print(f"fold: auc{auc} precision{precision} recall{recall}") if return_model: return res, models else: return res best_params = { "n_estimators": 500, "learning_rate": 0.05, "max_depth": 6, "colsample_bytree": 0.6, "min_child_weight": 1, "gamma": 0.7, "subsample": 0.6, "random_state": 456 } res, models = five_fold_train(x, y, XGBRegressor, super_parameters=best_params, return_model=True)”
import shap
import matplotlib.pyplot as plt
# 选择需要绘制的模型
model_index = 0
# 获取特征重要性信息
explainer = shap.TreeExplainer(models[model_index])
shap_values = explainer.shap_values(x)
# 绘制bar plot
shap.summary_plot(shap_values, x, plot_type="bar")
# 显示图像
plt.show()