classDataset(object):"""An abstract class representing a :class:`Dataset`. All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite :meth:`__getitem__`, su
worker_init_fn (callable, optional): 每个worker初始化函数 If not None, this will be called on each worker subprocess with the worker id (an int in [0, num_workers - 1]) as input, after seeding and before data loading. (default: None) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11...
size()) if i == 3: break # 下面三个类都需要实现 __call__ 函数 class Rescale(object): """Rescale the image in a sample to a given size. Args: output_size (tuple or int): Desired output size. If tuple, output is matched to output_size. If int, smaller of image edges is ma...
self.drop_last=NoneifsamplerisnotNoneandshuffle:raiseValueError('sampler option is mutually exclusive with''shuffle')ifself.num_workers <0:raiseValueError('num_workers option cannot be negative;''use num_workers=0 to disable multiprocessing.')ifbatch_samplerisNone:ifsamplerisNone:ifshuffle: sampler...
self._data_queue = self._worker_result_queue# 如果不需要pin,则直接使用_worker_result_queue# .pid can be None only before process is spawned (not the case, so ignore)_utils.signal_handling._set_worker_pids(id(self),tuple(w.pidforwinself._workers))# type: ignore[misc]_utils.signal_han...
index) tuples """ def __init__( self, root: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, loader: Callable[[str], Any] = default_loader, is_valid_file: Optional[Callable[[str], bool]] = None, ): super(ImageF...
先介绍一下DataLoader(object)的参数: dataset(Dataset): 传入的数据集 batch_size(int, optional): 每个batch有多少个样本 shuffle(bool, optional): 在每个epoch开始的时候,对数据进行重新排序 sampler(Sampler, optional): 自定义从数据集中取样本的策略,如果指定这个参数,那么shuffle必须为False ...
加载数据后，数据和标签是tuple元组的形式，我们可以使用enumerate访问可遍历的数组对象即可:# 使用enumerate...
When a subclass is used with :class:`~torch.utils.data.DataLoader`, each item in the dataset will be yielded from the :class:`~torch.utils.data.DataLoader` iterator. When :attr:`num_workers > 0`, each worker process will have a different copy of the dataset object, so it is often ...
然后通过self.collate_fn函数将batch size个tuple(每个tuple长度为2,其中第一个值是数据,Tensor类型,第二个值是标签,int类型)封装成一个list,这个list长度为2,两个值都是Tensor,一个是batch size个数据组成的FloatTensor,另一个是batch size个标签组成的LongTensor。所以简单讲self.collate_fn函数就是将batch size...