train_dataset = datasets.ImageFolder(
    traindir,
    transforms.Compose([
        transforms.Resize((224)) ###

train_dataset = datasets.ImageFolder(
    traindir,
    transforms.Compose([
        transforms.Resize((224,224)),

dataset = MyDataset()
dataloader = DataLoader(dataset)
num_epoches = 100
for epoch in range(num_epoches):
for img, label in dataloader:
....

worker subprocess with the worker id (an int in [0, num_workers - 1]) as
input, after seeding and before data loading. (default: None) 

class DataLoader(object):
    r"""
    Data loader. Combines a dataset and a sampler, and provides
    single- or multi-process iterators over the dataset.
    Arguments:
        dataset (Dataset): dataset from which to load the data.
        batch_size (int, optional): how many samples per batch to load
            (default: 1).
        shuffle (bool, optional): set to ``True`` to have the data reshuffled
            at every epoch (default: False).
        sampler (Sampler, optional): defines the strategy to draw samples from
            the dataset. If specified, ``shuffle`` must be False.
        batch_sampler (Sampler, optional): like sampler, but returns a batch of
            indices at a time. Mutually exclusive with batch_size, shuffle,
            sampler, and drop_last.
        num_workers (int, optional): how many subprocesses to use for data
            loading. 0 means that the data will be loaded in the main process.
            (default: 0)
        collate_fn (callable, optional): merges a list of samples to form a mini-batch.
        pin_memory (bool, optional): If ``True``, the data loader will copy tensors
            into CUDA pinned memory before returning them.
        drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
            if the dataset size is not divisible by the batch size. If ``False`` and
            the size of dataset is not divisible by the batch size, then the last batch
            will be smaller. (default: False)
        timeout (numeric, optional): if positive, the timeout value for collecting a batch
            from workers. Should always be non-negative. (default: 0)
        worker_init_fn (callable, optional): 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)
    .. note:: By default, each worker will have its PyTorch seed set to
              ``base_seed + worker_id``, where ``base_seed`` is a long generated
              by main process using its RNG. However, seeds for other libraies
              may be duplicated upon initializing workers (w.g., NumPy), causing
              each worker to return identical random numbers. (See
              :ref:`dataloader-workers-random-seed` section in FAQ.) You may
              use ``torch.initial_seed()`` to access the PyTorch seed for each
              worker in :attr:`worker_init_fn`, and use it to set other seeds
              before data loading.
    .. warning:: If ``spawn`` start method is used, :attr:`worker_init_fn` cannot be an
                 unpicklable object, e.g., a lambda function.
    """
    __initialized = False
    def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,
                 num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False,
                 timeout=0, worker_init_fn=None):
        self.dataset = dataset
        self.batch_size = batch_size
        self.num_workers = num_workers
        self.collate_fn = collate_fn
        self.pin_memory = pin_memory
        self.drop_last = drop_last
        self.timeout = timeout
        self.worker_init_fn = worker_init_fn
        if timeout < 0:
            raise ValueError('timeout option should be non-negative')
        if batch_sampler is not None:
            if batch_size > 1 or shuffle or sampler is not None or drop_last:
                raise ValueError('batch_sampler option is mutually exclusive '
                                 'with batch_size, shuffle, sampler, and '
                                 'drop_last')
            self.batch_size = None
            self.drop_last = None
        if sampler is not None and shuffle:
            raise ValueError('sampler option is mutually exclusive with '
                             'shuffle')
        if self.num_workers < 0:
            raise ValueError('num_workers option cannot be negative; '
                             'use num_workers=0 to disable multiprocessing.')
        if batch_sampler is None:
            if sampler is None:
                if shuffle:
                    sampler = RandomSampler(dataset)  //将list打乱
                else:
                    sampler = SequentialSampler(dataset)
            batch_sampler = BatchSampler(sampler, batch_size, drop_last)
        self.sampler = sampler
        self.batch_sampler = batch_sampler
        self.__initialized = True
    def __setattr__(self, attr, val):
        if self.__initialized and attr in ('batch_size', 'sampler', 'drop_last'):
            raise ValueError('{} attribute should not be set after {} is '
                             'initialized'.format(attr, self.__class__.__name__))
        super(DataLoader, self).__setattr__(attr, val)
    def __iter__(self):
        return _DataLoaderIter(self)
    def __len__(self):
        return len(self.batch_sampler)

class RandomSampler(Sampler):
    r"""Samples elements randomly, without replacement.
    Arguments:
        data_source (Dataset): dataset to sample from
    """
    def __init__(self, data_source):
        self.data_source = data_source
    def __iter__(self):
        return iter(torch.randperm(len(self.data_source)).tolist())
    def __len__(self):
        return len(self.data_source)

class BatchSampler(Sampler):
    r"""Wraps another sampler to yield a mini-batch of indices.
    Args:
        sampler (Sampler): Base sampler.
        batch_size (int): Size of mini-batch.
        drop_last (bool): If ``True``, the sampler will drop the last batch if
            its size would be less than ``batch_size``
    Example:
        >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=False))
        [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
        >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True))
        [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
    """
    def __init__(self, sampler, batch_size, drop_last):
        if not isinstance(sampler, Sampler):
            raise ValueError("sampler should be an instance of "
                             "torch.utils.data.Sampler, but got sampler={}"
                             .format(sampler))
        if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
                batch_size <= 0:
            raise ValueError("batch_size should be a positive integeral value, "
                             "but got batch_size={}".format(batch_size))
        if not isinstance(drop_last, bool):
            raise ValueError("drop_last should be a boolean value, but got "
                             "drop_last={}".format(drop_last))
        self.sampler = sampler
        self.batch_size = batch_size
        self.drop_last = drop_last
    def __iter__(self):
        batch = []
        for idx in self.sampler:
            batch.append(idx)
            if len(batch) == self.batch_size:
                yield batch
                batch = []
        if len(batch) > 0 and not self.drop_last:
            yield batch
    def __len__(self):
        if self.drop_last:
            return len(self.sampler) // self.batch_size
        else:
            return (len(self.sampler) + self.batch_size - 1) // self.batch_size

class _DataLoaderIter(object):
    r"""Iterates once over the DataLoader's dataset, as specified by the sampler"""
    def __init__(self, loader):
        self.dataset = loader.dataset
        self.collate_fn = loader.collate_fn
        self.batch_sampler = loader.batch_sampler
        self.num_workers = loader.num_workers
        self.pin_memory = loader.pin_memory and torch.cuda.is_available()
        self.timeout = loader.timeout
        self.done_event = threading.Event()
        self.sample_iter = iter(self.batch_sampler)
        base_seed = torch.LongTensor(1).random_().item()
        if self.num_workers > 0:
            self.worker_init_fn = loader.worker_init_fn
            self.index_queues = [multiprocessing.Queue() for _ in range(self.num_workers)]
            self.worker_queue_idx = 0
            self.worker_result_queue = multiprocessing.SimpleQueue()
            self.batches_outstanding = 0
            self.worker_pids_set = False
            self.shutdown = False
            self.send_idx = 0
            self.rcvd_idx = 0
            self.reorder_dict = {}
            self.workers = [
                multiprocessing.Process(
                    target=_worker_loop,
                    args=(self.dataset, self.index_queues[i],
                          self.worker_result_queue, self.collate_fn, base_seed + i,
                          self.worker_init_fn, i))
                for i in range(self.num_workers)]
            if self.pin_memory or self.timeout > 0:
                self.data_queue = queue.Queue()
                if self.pin_memory:
                    maybe_device_id = torch.cuda.current_device()
                else:
                    # do not initialize cuda context if not necessary
                    maybe_device_id = None
                self.worker_manager_thread = threading.Thread(
                    target=_worker_manager_loop,
                    args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,
                          maybe_device_id))
                self.worker_manager_thread.daemon = True
                self.worker_manager_thread.start()
            else:
                self.data_queue = self.worker_result_queue
            for w in self.workers:
                w.daemon = True  # ensure that the worker exits on process exit
                w.start()
            _update_worker_pids(id(self), tuple(w.pid for w in self.workers))
            _set_SIGCHLD_handler()
            self.worker_pids_set = True
            # prime the prefetch loop
            for _ in range(2 * self.num_workers):
                self._put_indices()
    def __len__(self):
        return len(self.batch_sampler)
    def _get_batch(self):
        if self.timeout > 0:
            try:
                return self.data_queue.get(timeout=self.timeout)
            except queue.Empty:
                raise RuntimeError('DataLoader timed out after {} seconds'.format(self.timeout))
        else:
            return self.data_queue.get()
    def __next__(self):
        if self.num_workers == 0:  # same-process loading
            indices = next(self.sample_iter)  # may raise StopIteration
            batch = self.collate_fn([self.dataset[i] for i in indices])
            if self.pin_memory:
                batch = pin_memory_batch(batch)
            return batch
        # check if the next sample has already been generated
        if self.rcvd_idx in self.reorder_dict:
            batch = self.reorder_dict.pop(self.rcvd_idx)
            return self._process_next_batch(batch)
        if self.batches_outstanding == 0:
            self._shutdown_workers()
            raise StopIteration
        while True:
            assert (not self.shutdown and self.batches_outstanding > 0)
            idx, batch = self._get_batch()
            self.batches_outstanding -= 1
            if idx != self.rcvd_idx:
                # store out-of-order samples
                self.reorder_dict[idx] = batch
                continue
            return self._process_next_batch(batch)
    next = __next__  # Python 2 compatibility
    def __iter__(self):
        return self
    def _put_indices(self):
        assert self.batches_outstanding < 2 * self.num_workers
        indices = next(self.sample_iter, None)
        if indices is None:
            return
        self.index_queues[self.worker_queue_idx].put((self.send_idx, indices))
        self.worker_queue_idx = (self.worker_queue_idx + 1) % self.num_workers
        self.batches_outstanding += 1
        self.send_idx += 1
    def _process_next_batch(self, batch):
        self.rcvd_idx += 1
        self._put_indices()
        if isinstance(batch, ExceptionWrapper):
            raise batch.exc_type(batch.exc_msg)
        return batch

def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn, worker_id):
    global _use_shared_memory
    _use_shared_memory = True
    # Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
    # module's handlers are executed after Python returns from C low-level
    # handlers, likely when the same fatal signal happened again already.
    # https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
    _set_worker_signal_handlers()
    torch.set_num_threads(1)
    random.seed(seed)
    torch.manual_seed(seed)
    if init_fn is not None:
        init_fn(worker_id)
    watchdog = ManagerWatchdog()
    while True:
        try:
            r = index_queue.get(timeout=MANAGER_STATUS_CHECK_INTERVAL)
        except queue.Empty:
            if watchdog.is_alive():
                continue
            else:
                break
        if r is None:
            break
        idx, batch_indices = r
        try:
            samples = collate_fn([dataset[i] for i in batch_indices])
        except Exception:
            data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
        else:
            data_queue.put((idx, samples))
            del samples