super(torch.utils.data.Dataset, self).__init__() self.root = root # 数据集的根路径 self.is_train = is_train # 数据集是否用于训练? self.images, self.labels = self._prepare_data() # 将提前准备的数据集的数据存入images和labels
时间: 2023-06-01 08:06:31 浏览: 57
这是Python中的一行代码,用于初始化一个类。它继承了torch.utils.data.Dataset类,并调用了其父类的__init__()方法。self.root是一个类的属性,表示数据集的根目录。
相关问题
class DistributedSampler(_DistributedSampler): def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True): super().__init__(dataset, num_replicas=num_replicas, rank=rank) self.shuffle = shuffle def __iter__(self): if self.shuffle: g = torch.Generator() g.manual_seed(self.epoch) indices = torch.randperm(len(self.dataset), generator=g).tolist() else: indices = torch.arange(len(self.dataset)).tolist() indices += indices[:(self.total_size - len(indices))] assert len(indices) == self.total_size indices = indices[self.rank:self.total_size:self.num_replicas] assert len(indices) == self.num_samples return iter(indices) def build_dataloader(dataset_cfg, class_names, batch_size, dist, root_path=None, workers=4, seed=None, logger=None, training=True, merge_all_iters_to_one_epoch=False, total_epochs=0): dataset = __all__[dataset_cfg.DATASET]( dataset_cfg=dataset_cfg, class_names=class_names, root_path=root_path, training=training, logger=logger, ) if merge_all_iters_to_one_epoch: assert hasattr(dataset, 'merge_all_iters_to_one_epoch') dataset.merge_all_iters_to_one_epoch(merge=True, epochs=total_epochs) if dist: if training: sampler = torch.utils.data.distributed.DistributedSampler(dataset) else: rank, world_size = common_utils.get_dist_info() sampler = DistributedSampler(dataset, world_size, rank, shuffle=False) else: sampler = None dataloader = DataLoader( dataset, batch_size=batch_size, pin_memory=True, num_workers=workers, shuffle=(sampler is None) and training, collate_fn=dataset.collate_batch, drop_last=False, sampler=sampler, timeout=0, worker_init_fn=partial(common_utils.worker_init_fn, seed=seed) ) return dataset, dataloader, sampler
这段代码是一个用于构建数据加载器的函数。它接受一些参数,包括数据集的配置、类别名称、批次大小、分布式训练标志、数据集的根路径等。
首先,根据数据集的配置和其他参数,创建一个数据集对象。
如果设置了 `merge_all_iters_to_one_epoch` 标志为 `True`,则调用数据集对象的 `merge_all_iters_to_one_epoch` 方法,将所有迭代器合并到一个周期中。
接下来,如果分布式训练标志为 `True`,则根据训练模式创建相应的采样器。对于训练模式,使用 `torch.utils.data.distributed.DistributedSampler` 创建采样器;对于非训练模式,根据当前进程的排名和世界大小创建 `DistributedSampler` 采样器,并设置 `shuffle` 参数为 `False`。
如果不是分布式训练,则采样器为 `None`。
最后,使用 `torch.utils.data.DataLoader` 创建数据加载器,传入数据集对象、批次大小、是否在训练模式下洗牌、数据集对象的 `collate_batch` 方法用于批量整理数据、是否丢弃最后一个批次、采样器以及其他参数。
函数返回数据集对象、数据加载器和采样器。
import torch import torch.nn as nn import torch.optim as optim import torchvision # datasets and pretrained neural nets import torch.utils.data import torch.nn.functional as F import torchvision.transforms as transforms transform = transforms.Compose([transforms.ToTensor(), ]) trainset = torchvision.datasets.MNIST(root='.\mydata', train=True, download=True, transform=transform) #文件夹地址可自定义 testset = torchvision.datasets.MNIST(root='.\mydata', train=False, download=True, transform=transform) #文件夹地址可自定义 trainloader = torch.utils.data.DataLoader(trainset, batch_size =32, shuffle=True) testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)写一个构建网络定义损失函数,优化器训练 迭代次数为10次 模型评测尝试将数据集更改为FashionMNIST或者CIFIR10再训练模型,或对比卷积神经网络和全连接神经网络模型的预测准确度,或尝试数据增强方法的代码
好的,这里是一个简单的代码示例,包括构建网络、定义损失函数、优化器、训练和评估模型的步骤。同时,也包括了针对FashionMNIST数据集和CIFAR10数据集的代码,以及卷积神经网络和全连接神经网络模型的比较,以及数据增强方法的代码。
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torch.utils.data
import torch.nn.functional as F
import torchvision.transforms as transforms
# 数据集选项
dataset = 'MNIST' # 可以选择'MNIST', 'FashionMNIST'或者'CIFAR10'
data_root = './data' # 数据集存储的文件夹地址
# 构建网络
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(28 * 28, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = x.view(-1, 28 * 28)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# 定义损失函数和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 加载数据集
if dataset == 'MNIST':
trainset = torchvision.datasets.MNIST(root=data_root, train=True, download=True, transform=transforms.ToTensor())
testset = torchvision.datasets.MNIST(root=data_root, train=False, download=True, transform=transforms.ToTensor())
elif dataset == 'FashionMNIST':
trainset = torchvision.datasets.FashionMNIST(root=data_root, train=True, download=True, transform=transforms.ToTensor())
testset = torchvision.datasets.FashionMNIST(root=data_root, train=False, download=True, transform=transforms.ToTensor())
elif dataset == 'CIFAR10':
trainset = torchvision.datasets.CIFAR10(root=data_root, train=True, download=True, transform=transforms.ToTensor())
testset = torchvision.datasets.CIFAR10(root=data_root, train=False, download=True, transform=transforms.ToTensor())
else:
raise ValueError('Invalid dataset name')
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
# 评估模型
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))
```
针对FashionMNIST数据集和CIFAR10数据集的代码仅需要改变数据集的名称和地址即可。卷积神经网络和全连接神经网络模型的比较可以使用以下代码:
```python
# 定义卷积神经网络
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=5, padding=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5, padding=2)
self.fc1 = nn.Linear(32 * 7 * 7, 256)
self.fc2 = nn.Linear(256, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = x.view(-1, 32 * 7 * 7)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# 定义全连接神经网络
class FCNet(nn.Module):
def __init__(self):
super(FCNet, self).__init__()
self.fc1 = nn.Linear(28 * 28, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = x.view(-1, 28 * 28)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
if dataset == 'MNIST' or dataset == 'FashionMNIST':
net = FCNet()
elif dataset == 'CIFAR10':
net = ConvNet()
else:
raise ValueError('Invalid dataset name')
# 训练和评估模型的代码和之前相同
```
最后,以下是数据增强的代码示例,可以在训练数据集上应用随机的图像变换,从而增加数据集的多样性和泛化性能。
```python
# 数据增强
train_transform = transforms.Compose([
transforms.RandomCrop(28, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
trainset = torchvision.datasets.MNIST(root=data_root, train=True, download=True, transform=train_transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)
# 训练模型的代码和之前相同
```
需要注意的是,在评估模型时,不应该使用数据增强,因为这会导致测试结果不准确。