training_data = datasets.FashionMNIST( root="data", train=True, download=True, transform=ToTensor() ) root 参数,即数据集所在的目录。 在download 为 True 时,最自动下载数据集文件 train 代表,这是训练数据 是从AWS S3 上下载的压缩包文件。 >python main.py Downloading http://fashion-mnist.s3-web...
pytorch dataset 会在做前向推理的时候，提前把下一次数据拉到内存当中。
dataset_test = datasets.CIFAR10(root=root_dir, train=False, transform=transforms.ToTensor(), download=True) dataloader_train = DataLoader(dataset=dataset_train, batch_size=64, shuffle=True, drop_last=True) dataloader_test = DataLoader(dataset=dataset_test, batch_size=64, shuffle=True, drop_last...
device_ids=[args.local_rank], broadcast_buffers=False) else: if args.local_rank == 0: _logger.info("Using native Torch DistributedDataParallel.") model = NativeDDP(model,
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root: str, image_folder: str, mask_folder: str, transforms: Optional[Callable] = None, seed: int = None, fraction: float = None, subset: str = None, image_color_mode: str = "rgb", mask_color_mode: str = "grayscale") -> None: ...
def __init__(self, root_dir, fnames, transforms=None): # store transforms func self.transforms = transforms # initialize storage arrays self.wave_loc = [] self.labels = [] # for each hdf5 file... for fname in fnames: # open the file ...
import sys import os.path if __name__ == "__main__": f = open('dataset.txt', ...
transform=None)# 对标签执行的转换（如果需要的话）test_data=datasets.ImageFolder(root=test_dir,...