pytochのdataloaderは深く分析します.
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本論文の内容は以下の通りになります.https://www.cnblogs.com/ranjiewen/p/10128046.html PyTorch学習ノート(6)——Data Loaderソースコード解析 dataloaderは、本質的には反復可能なオブジェクトであり、iter()を使用してアクセスし、next()を使用してアクセスできない. iter(dataloader)を使って帰ってきたのは、次のnextを使ってアクセスできます. は、 一般的に、私たちはdatasetsオブジェクトを実現し、dataloaderに入る.その後内部でyeildを使ってbatchのデータを返します. ①Data Loaderは本質的にはiterableであり(pythonの内蔵タイプlistなどと同じ)、マルチプロセスを利用してbatch dataの処理を加速し、yieldを使用して有限メモリ②Queの特徴を使用して、キューにデータがない場合:queue.get()がブロックされ、ブロックされる場合、他のプロセス/スレッドはque.put(作業スレッド)があります.そして成功をゲットできます.データがいっぱいになった時:queue.put()③Data Loaderは効率的で、簡潔で、直感的なネットワーク入力データ構造で、使用と拡張に便利です.
データPipeLine pytouchを入力するデータをモデルにロードする操作手順はこうです.
①Datasetオブジェクトを作成する②Data Loaderオブジェクトを作成する③ループというData Loaderオブジェクトをモデルにロードして訓練する
dataset=MyDataset()dataloader=DataLoader(dataset)num_epoches=100 for epoch in range(num uepoches):for img、label in dataloader:…
したがって,直接データがモデルに入るための鍵となるステップとして,Data Loaderは非常に重要である.
まず、Data Loaderを簡単に紹介します.PyTorchでデータを読み取る重要なインターフェースです.このインターフェースはdataloader.pyで定義されています.PyTorchでモデルを訓練するなら、基本的にはこのインターフェースを使用します.後のトレーニングに使います.
データロードはデータセットとサンプラで構成され、pythonのシングル、マルチプロセスのiteratorsに基づいてデータを処理します.iter_同前next_u.方法は、iterableは_u uしかありません.iter_方法
1.Data Loader
まず、Data Loaderのパラメータを紹介します.まずdataloader初期化時にdatasetsのサンプルリスト’ を得る.そのうち_Data Loader Iterはdataloaderオブジェクトとして入力します.もしnum_workers=0よく分かります.num_ウォーカー!=0マルチスレッド機構を導入し、データローディングプロセスを加速する. マルチスレッドがない場合:batch=self.com llate_fn([self.dataset[i]for i in indices])はindexをdataデータに変換して返します.self.dataset[i]は、datasetsオブジェクトの を呼び出します.
getitem()方法マルチスレッドでは、各スレッドにインデックス・キューindex_を作成します.queues;一つのウォーカーを共有するレスリングqueueデータキュー!_にありますウォーカーカーカーloop方法でデータを読み込みます. はキュー操作が必要で、データをキャッシュして、ローディング速度を向上させます.
for inputs, labels in dataloadersを使用して反復可能なオブジェクトへのアクセスも可能である.データPipeLine pytouchを入力するデータをモデルにロードする操作手順はこうです.
①Datasetオブジェクトを作成する②Data Loaderオブジェクトを作成する③ループというData Loaderオブジェクトをモデルにロードして訓練する
dataset=MyDataset()dataloader=DataLoader(dataset)num_epoches=100 for epoch in range(num uepoches):for img、label in dataloader:…
したがって,直接データがモデルに入るための鍵となるステップとして,Data Loaderは非常に重要である.
まず、Data Loaderを簡単に紹介します.PyTorchでデータを読み取る重要なインターフェースです.このインターフェースはdataloader.pyで定義されています.PyTorchでモデルを訓練するなら、基本的にはこのインターフェースを使用します.後のトレーニングに使います.
データロードはデータセットとサンプラで構成され、pythonのシングル、マルチプロセスのiteratorsに基づいてデータを処理します.iter_同前next_u.方法は、iterableは_u uしかありません.iter_方法
1.Data Loader
まず、Data Loaderのパラメータを紹介します.
PipeLine
pytorch ：
① Dataset
② DataLoader
③ DataLoader ， img, label
dataset = MyDataset()
dataloader = DataLoader(dataset)
num_epoches = 100
for epoch in range(num_epoches):
for img, label in dataloader:
....
， ， DataLoader 。
DataLoader， PyTorch ， dataloader.py ， PyTorch （ …）， ： Dataset batch size 、 shuffle Batch Size Tensor， 。
DataLoader ：“ ， python 、 iterators 。” iterator iterable ， iterators __iter__ __next__ ， iterable __iter__ 。
1.DataLoader
DataLoader(object) ：
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) // list
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
getitem()方法
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