本文整理匯總了Python中tensorpack.dataflow.PrefetchDataZMQ方法的典型用法代碼示例。如果您正苦於以下問題：Python dataflow.PrefetchDataZMQ方法的具體用法？Python dataflow.PrefetchDataZMQ怎麽用？Python dataflow.PrefetchDataZMQ使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorpack.dataflow的用法示例。
在下文中一共展示了dataflow.PrefetchDataZMQ方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: __init__
# 需要導入模塊: from tensorpack import dataflow [as 別名]
# 或者: from tensorpack.dataflow import PrefetchDataZMQ [as 別名]
def __init__(self, mode, batch_size=256, shuffle=False, num_workers=25, cache=50000,
collate_fn=default_collate, drop_last=False, cuda=False):
# enumerate standard imagenet augmentors
imagenet_augmentors = fbresnet_augmentor(mode == 'train')
# load the lmdb if we can find it
lmdb_loc = os.path.join(os.environ['IMAGENET'],'ILSVRC-%s.lmdb'%mode)
ds = td.LMDBData(lmdb_loc, shuffle=False)
ds = td.LocallyShuffleData(ds, cache)
ds = td.PrefetchData(ds, 5000, 1)
ds = td.LMDBDataPoint(ds)
ds = td.MapDataComponent(ds, lambda x: cv2.imdecode(x, cv2.IMREAD_COLOR), 0)
ds = td.AugmentImageComponent(ds, imagenet_augmentors)
ds = td.PrefetchDataZMQ(ds, num_workers)
self.ds = td.BatchData(ds, batch_size)
self.ds.reset_state()
self.batch_size = batch_size
self.num_workers = num_workers
self.cuda = cuda
#self.drop_last = drop_last 示例2: get_imagenet_dataflow
# 需要導入模塊: from tensorpack import dataflow [as 別名]
# 或者: from tensorpack.dataflow import PrefetchDataZMQ [as 別名]
def get_imagenet_dataflow(datadir,
is_train,
batch_size,
augmentors,
parallel=None):
"""
See explanations in the tutorial:
http://tensorpack.readthedocs.io/en/latest/tutorial/efficient-dataflow.html
"""
assert datadir is not None
assert isinstance(augmentors, list)
if parallel is None:
parallel = min(40, multiprocessing.cpu_count() // 2) # assuming hyperthreading
if is_train:
ds = dataset.ILSVRC12(datadir, "train", shuffle=True)
ds = AugmentImageComponent(ds, augmentors, copy=False)
if parallel < 16:
logging.warning("DataFlow may become the bottleneck when too few processes are used.")
ds = PrefetchDataZMQ(ds, parallel)
ds = BatchData(ds, batch_size, remainder=False)
else:
ds = dataset.ILSVRC12Files(datadir, "val", shuffle=False)
aug = imgaug.AugmentorList(augmentors)
def mapf(dp):
fname, cls = dp
im = cv2.imread(fname, cv2.IMREAD_COLOR)
im = np.flip(im, axis=2)
# print("fname={}".format(fname))
im = aug.augment(im)
return im, cls
ds = MultiThreadMapData(ds, parallel, mapf, buffer_size=2000, strict=True)
# ds = MapData(ds, mapf)
ds = BatchData(ds, batch_size, remainder=True)
ds = PrefetchDataZMQ(ds, 1)
# ds = PrefetchData(ds, 1)
return ds 示例3: get_data
# 需要導入模塊: from tensorpack import dataflow [as 別名]
# 或者: from tensorpack.dataflow import PrefetchDataZMQ [as 別名]
def get_data(split, option):
is_training = split == 'train'
parallel = multiprocessing.cpu_count() // 2
ds = get_data_flow(split, is_training, option)
augmentors = fbresnet_augmentor(is_training, option)
ds = AugmentImageCoordinates(ds, augmentors, coords_index=2, copy=False)
if is_training:
ds = PrefetchDataZMQ(ds, parallel)
ds = BatchData(ds, option.batch_size, remainder=not is_training)
return ds 示例4: get_data
# 需要導入模塊: from tensorpack import dataflow [as 別名]
# 或者: from tensorpack.dataflow import PrefetchDataZMQ [as 別名]
def get_data(lmdb_path, txt_path):
if txt_path:
ds = arod_dataflow_from_txt.Triplets(lmdb_path, txt_path, IMAGE_HEIGHT, IMAGE_WIDTH)
else:
ds = arod_provider.Triplets(lmdb_path, IMAGE_HEIGHT, IMAGE_WIDTH)
ds.reset_state()
cpu = min(10, multiprocessing.cpu_count())
ds = PrefetchDataZMQ(ds, cpu)
ds = BatchData(ds, BATCH_SIZE)
return ds 示例5: lmdb_dataflow
# 需要導入模塊: from tensorpack import dataflow [as 別名]
# 或者: from tensorpack.dataflow import PrefetchDataZMQ [as 別名]
def lmdb_dataflow(lmdb_path, batch_size, input_size, output_size, is_training, test_speed=False):
df = dataflow.LMDBSerializer.load(lmdb_path, shuffle=False)
size = df.size()
if is_training:
df = dataflow.LocallyShuffleData(df, buffer_size=2000)
df = dataflow.PrefetchData(df, nr_prefetch=500, nr_proc=1)
df = BatchData(df, batch_size, input_size, output_size)
if is_training:
df = dataflow.PrefetchDataZMQ(df, nr_proc=8)
df = dataflow.RepeatedData(df, -1)
if test_speed:
dataflow.TestDataSpeed(df, size=1000).start()
df.reset_state()
return df, size