(2)ConcatDataset()函数 ConcatDataset(datasets):合并dataset 参数:datasets:List of dataset 也可直接用+合并两个数据集 实例: # 方式一dc1 = d1 + d2 + d3# 方式二:与方式一等价dc2 = tud.ConcatDataset([d1, d2, d3])print(dc1.__len__(), dc1.
datasets.CIFAR10("./dataset", download=True, train=False, transform=torchvision.transforms.ToTensor()) # 加载测试集,batch_size=64 意味着每次从test_data中取64个数据进行打包 test_loader = DataLoader(dataset=test_data, batch_size=64, shuffle=True, num_workers=0, drop_last=False) # 实例化...
# 对数据（图像）执行的转换target_transform=None)# 对标签执行的转换（如果需要的话）test_data=data...
import torchfrom torchvision import datasetstrain_data = datasets.ImageFolder(train_path, transform=data_transform)val_data = datasets.ImageFolder(val_path, transform=data_transform) 这里使用了PyTorch自带的ImageFolder类的用于读取按一定结构存储的图片数据(path对应图片存放的目录,目录下包含若干子目录,每个子目...
# 1. Initialize file path or list of file names. pass def __getitem__(self, index): # TODO # 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open). # 2. Preprocess the data (e.g. torchvision.Transform). ...
class ConcatDataset(Dataset): """ Dataset to concatenate multiple datasets. Purpose: useful to assemble different existing datasets, possibly large-scale datasets as the concatenation operation is done in an on-the-fly manner. Arguments: datasets (sequence): List of datasets to be concatenated """...
parser.add_argument("--dataset-path", default='./datasets', type=str, help="Path of the trainset.") 1#创建数据集2train_dataset = MedicalDataset(args.dataset_path,'train')3test_dataset = MedicalDataset(args.dataset_path,'test')4print(len(train_dataset), len(test_dataset))#训练集400,...
self.imgs:保存(img-path, class) tuple的 list def verity_datasets(): root = './datasets/train' # 根路径 data = datasets.ImageFolder(root) # 可以理解载入dataset print('data.classes:',data.classes) # 类别信息 print('data.class_to_idx:',data.class_to_idx) # 类别与索引 ...
)vgg_layers_list.append(nn.ReLU())vgg_layers_list.append(nn.Dropout(0.5,inplace=False))vgg_layers_list.append(nn.Linear(4096,2))model = nn.Sequential(*vgg_layers_list)model=model.to(device)#Num of epochs to trainnum_epochs=10#Lossloss_func = nn.CrossEntropyLoss()# Optimizer # ...
collate_fn (callable, optional) –merges a list of samples to form a mini-batch of Tensor(s). Used when using batched loading from a map-style dataset. pin_memory (bool, optional) –If True, the data loader will copy Tensors into CUDA pinned memory before returning them. If your data...