class Trainer(object): def __init__(self, net, per_num=20, start_num=0, end_num=10, save_path="./model/Lwf", epoch=50, lr=0.0005, batch_size=128): self.lr = lr self.epoch = epoch self.batch_size = batch_size self.strat_num = start_num self.end_num = end_num self.class_num = end_num - start_num self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.save_path = save_path self.main_net_path = save_path + "/LwF_" + str(start_num) + ".pth" transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.RandomRotation(10), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) trainset = Cifar100Split(start_num=start_num, end_num=end_num, train=True, transform=transform_train) testset = Cifar100Split(start_num=start_num, end_num=end_num, train=False, transform=transform_test) test_all = Cifar100Split(start_num=0, end_num=end_num, train=False, transform=transform_test) self.train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=0) self.test_loader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=0) self.test_loader_all = DataLoader(test_all, batch_size=batch_size, shuffle=False, num_workers=0)
时间: 2024-04-25 14:27:46 浏览: 12
这段代码是一个PyTorch中的类Trainer的初始化函数。在初始化时,它接受一些参数,包括网络模型net、每个类别的训练样本数per_num、起始类别编号start_num、结束类别编号end_num、保存路径save_path、训练轮数epoch、学习率lr、批量大小batch_size等。此外,该类还定义了一些图像预处理的操作,包括随机裁剪、随机翻转、随机旋转等,并对训练集和测试集进行了划分和加载。该类的作用是训练深度神经网络模型以实现对图像数据的分类任务。
相关问题
class Trainer(object): def __init__(self, model, optim, device, train_dataloader, val_dataloader, test_dataloader, opt_da=None, discriminator=None, experiment=None, alpha=1, **config):是什么意思
这段代码定义了一个名为Trainer的类,它具有以下属性:
- model:表示使用的模型。
- optim:表示优化器。
- device:表示计算设备。
- train_dataloader:表示训练数据加载器。
- val_dataloader:表示验证数据加载器。
- test_dataloader:表示测试数据加载器。
- opt_da:表示强化学习中的数据增强器。
- discriminator:表示使用的判别器。
- experiment:表示实验名称。
- alpha:表示损失函数中的超参数。
该类的初始化方法__init__用于初始化这些属性。
class Trainer_est(object):
As an AI language model, I am not capable of providing personal opinion or preference in terms of a trainer but here is the example of Trainer_est class:
```
class Trainer_est:
def __init__(self, name, experience, specialty):
self.name = name
self.experience = experience
self.specialty = specialty
def train(self, client):
print("{} is training {} in {}.".format(self.name, client.name, self.specialty))
client.train()
self.experience += 1
print("{}'s experience increased to {}.".format(self.name, self.experience))
```
This is a simple class for a trainer, with attributes of name, experience, and specialty, as well as a method called "train" that takes a "client" object as an argument and prints a message indicating that the trainer is training the client in their specialty. It then calls the "train" method of the client object, which presumably performs some kind of training activity. Finally, the method increases the trainer's experience by 1 and prints a message indicating the new experience level.