解释一下random_seed = 2019 torch.random.manual_seed(random_seed) np.random.seed(random_seed) epochs = 2 lr = 1e-3 resume_epoch = 0
时间: 2024-06-06 17:08:43 浏览: 23
这段代码是用于设置随机种子、训练轮数、学习率和恢复训练的起始轮数。
- `random_seed = 2019`:将随机种子设置为2019,这样每次运行程序时随机数生成器都会按照相同的方式生成随机数,确保实验的可重复性。
- `torch.random.manual_seed(random_seed)`:将PyTorch中的随机种子设置为2019,确保PyTorch中的随机数生成器也按照相同的方式生成随机数。
- `np.random.seed(random_seed)`:将NumPy中的随机种子设置为2019,确保NumPy中的随机数生成器也按照相同的方式生成随机数。
- `epochs = 2`:将训练轮数设置为2,意味着模型将会被训练2轮。
- `lr = 1e-3`:将学习率设置为0.001,这是一个超参数,用于控制模型在每次更新权重时的步长大小。
- `resume_epoch = 0`:将恢复训练的起始轮数设置为0,意味着从头开始训练模型而不是从之前的轮数开始。
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)# 导入数据 train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True,drop_last=True) test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False) 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损失函数训练模型的Pytorch代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torchvision import datasets, transforms
from ldam_loss import LDAMLoss
# 定义模型
class MyModel(nn.Module):
def __init__(self, num_classes):
super(MyModel, self).__init__()
self.num_classes = num_classes
self.features = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(128 * 4 * 4, 256),
nn.ReLU(inplace=True),
nn.Linear(256, num_classes),
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
# 设置超参数
model_lr = 1e-4
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
use_ema = True
model_ema_decay = 0.9998
start_epoch = 1
seed = 1
# 设置随机种子
def seed_everything(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
seed_everything(seed)
# 定义数据增强
transform = transforms.Compose([
transforms.Resize(224),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
transform_test = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
# 定义数据集
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)
# 定义数据加载器
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False)
# 定义模型及优化器
model = MyModel(num_classes=classes).to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=model_lr)
# 使用LDAM损失函数
cls_num_list = [dataset_train.targets.count(i) for i in range(classes)]
criterion = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, weight=None, s=30)
# 训练模型
for epoch in range(start_epoch, EPOCHS+1):
model.train()
for i, (data, target) in enumerate(train_loader):
data, target = data.to(DEVICE), target.to(DEVICE)
mixup_data, mixup_target = mixup_fn(data, target) # 数据增强
optimizer.zero_grad()
output = model(mixup_data)
loss = criterion(output, mixup_target)
if use_dp:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD)
else:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), CLIP_GRAD)
optimizer.step()
if use_ema:
ema_model = ModelEMA(model, decay=model_ema_decay)
ema_model.update(model)
else:
ema_model = None
test_acc = test(model, test_loader, DEVICE)
if test_acc > Best_ACC:
Best_ACC = test_acc
save_checkpoint({
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'Best_ACC': Best_ACC,
}, is_best=True)
```
if __name__ == "__main__": args = parse_args() print("A list all args: \n======================") pprint(vars(args)) print() #设置 CPU 生成随机数的种子 ,方便下次复现实验结果。 torch.manual_seed(args.seed) np.random.seed(args.seed) #路径拼接文件路径,可以传入多个路径 PATH = os.path.join("resources", args.data) EMBEDDING_PATH = "resources/" static_feat = ["sex", "age", "pur_power"] dynamic_feat = ["category", "shop", "brand"] device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_epochs = args.n_epochs batch_size = args.batch_size lr = args.lr item_embed_size = args.embed_size feat_embed_size = args.embed_size hidden_size = (256, 128) #CosineEmbeddingLoss余弦相似度损失函数,用于判断输入的两个向量是否相似 #BCEWithLogitsLoss就是把Sigmoid-BCELoss合成一步,计算交叉损失熵 criterion = ( nn.CosineEmbeddingLoss() if args.loss == "cosine" else nn.BCEWithLogitsLoss() ) #lower将字符串中的所有大写字母转换为小写字母 criterion_type = ( "cosine" if "cosine" in criterion.__class__.__name__.lower() else "bce" ) neg_label = -1. if criterion_type == "cosine" else 0. neg_item = args.neg_item columns = ["user", "item", "label", "time", "sex", "age", "pur_power", "category", "shop", "brand"] ( n_users, n_items, train_user_consumed, eval_user_consumed, train_data, eval_data, user_map, item_map, feat_map#feature是特征比如数据集里 age, brand 之类的 ) = process_feat_data( PATH, columns, test_size=0.2, time_col="time", static_feat=static_feat, dynamic_feat=dynamic_feat ) print(f"n_users: {n_users}, n_items: {n_items}, " f"train_shape: {train_data.shape}, eval_shape: {eval_data.shape}") train_user, train_item, train_label = sample_items_random( train_data, n_items, train_user_consumed, neg_label, neg_item ) eval_user, eval_item, eval_label = sample_items_random( eval_d
这个代码段的作用是:
1. 检查当前运行的代码是否为主程序,而不是被其他程序导入后调用的子程序;
2. 如果是主程序,则调用 parse_args() 函数解析命令行参数,并将结果保存在 args 变量中;
3. 打印所有解析后的参数列表,并用 pprint() 函数以易于阅读的格式输出。