本文整理匯總了Python中tensorpack.dataflow.common.MapData方法的典型用法代碼示例。如果您正苦於以下問題：Python common.MapData方法的具體用法？Python common.MapData怎麽用？Python common.MapData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.common的用法示例。
在下文中一共展示了common.MapData方法的6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: get_dataflow
# 需要導入模塊: from tensorpack.dataflow import common [as 別名]
# 或者: from tensorpack.dataflow.common import MapData [as 別名]
def get_dataflow(path, is_train, img_path=None):
ds = CocoPose(path, img_path, is_train) # read data from lmdb
if is_train:
ds = MapData(ds, read_image_url)
ds = MapDataComponent(ds, pose_random_scale)
ds = MapDataComponent(ds, pose_rotation)
ds = MapDataComponent(ds, pose_flip)
ds = MapDataComponent(ds, pose_resize_shortestedge_random)
ds = MapDataComponent(ds, pose_crop_random)
ds = MapData(ds, pose_to_img)
# augs = [
# imgaug.RandomApplyAug(imgaug.RandomChooseAug([
# imgaug.GaussianBlur(max_size=3)
# ]), 0.7)
# ]
# ds = AugmentImageComponent(ds, augs)
ds = PrefetchData(ds, 1000, multiprocessing.cpu_count() * 4)
else:
ds = MultiThreadMapData(ds, nr_thread=16, map_func=read_image_url, buffer_size=1000)
ds = MapDataComponent(ds, pose_resize_shortestedge_fixed)
ds = MapDataComponent(ds, pose_crop_center)
ds = MapData(ds, pose_to_img)
ds = PrefetchData(ds, 100, multiprocessing.cpu_count() // 4)
return ds 示例2: sample_augmentations
# 需要導入模塊: from tensorpack.dataflow import common [as 別名]
# 或者: from tensorpack.dataflow.common import MapData [as 別名]
def sample_augmentations():
ds = CocoPose('/data/public/rw/coco-pose-estimation-lmdb/', is_train=False, only_idx=0)
ds = MapDataComponent(ds, pose_random_scale)
ds = MapDataComponent(ds, pose_rotation)
ds = MapDataComponent(ds, pose_flip)
ds = MapDataComponent(ds, pose_resize_shortestedge_random)
ds = MapDataComponent(ds, pose_crop_random)
ds = MapData(ds, pose_to_img)
augs = [
imgaug.RandomApplyAug(imgaug.RandomChooseAug([
imgaug.GaussianBlur(3),
imgaug.SaltPepperNoise(white_prob=0.01, black_prob=0.01),
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.8, 1.2), clip=False),
imgaug.Contrast((0.8, 1.2), clip=False),
# imgaug.Saturation(0.4, rgb=True),
]),
]), 0.7),
]
ds = AugmentImageComponent(ds, augs)
ds.reset_state()
for l1, l2, l3 in ds.get_data():
CocoPose.display_image(l1, l2, l3) 示例3: get_dataflow
# 需要導入模塊: from tensorpack.dataflow import common [as 別名]
# 或者: from tensorpack.dataflow.common import MapData [as 別名]
def get_dataflow(path, is_train, img_path=None):
ds = CocoPose(path, img_path, is_train) # read data from lmdb
if is_train:
ds = MapData(ds, read_image_url)
ds = MapDataComponent(ds, pose_random_scale)
ds = MapDataComponent(ds, pose_rotation)
ds = MapDataComponent(ds, pose_flip)
ds = MapDataComponent(ds, pose_resize_shortestedge_random)
ds = MapDataComponent(ds, pose_crop_random)
ds = MapData(ds, pose_to_img)
# augs = [
# imgaug.RandomApplyAug(imgaug.RandomChooseAug([
# imgaug.GaussianBlur(max_size=3)
# ]), 0.7)
# ]
# ds = AugmentImageComponent(ds, augs)
ds = PrefetchData(ds, 1000, multiprocessing.cpu_count() * 1)
else:
ds = MultiThreadMapData(ds, nr_thread=16, map_func=read_image_url, buffer_size=1000)
ds = MapDataComponent(ds, pose_resize_shortestedge_fixed)
ds = MapDataComponent(ds, pose_crop_center)
ds = MapData(ds, pose_to_img)
ds = PrefetchData(ds, 100, multiprocessing.cpu_count() // 4)
return ds 示例4: get_dataflow
# 需要導入模塊: from tensorpack.dataflow import common [as 別名]
# 或者: from tensorpack.dataflow.common import MapData [as 別名]
def get_dataflow(self, cfg):
df = Pose(cfg)
df = MapData(df, self.augment)
df = MapData(df, self.compute_target_part_scoremap)
num_cores = multiprocessing.cpu_count()
num_processes = int(num_cores * self.cfg["processratio"])
if num_processes <= 1:
num_processes = 2 # recommended to use more than one process for training
if os.name == "nt":
df2 = MultiProcessRunner(
df, num_proc=num_processes, num_prefetch=self.cfg["num_prefetch"]
)
else:
df2 = MultiProcessRunnerZMQ(
df, num_proc=num_processes, hwm=self.cfg["num_prefetch"]
)
return df2 示例5: get_dataflow
# 需要導入模塊: from tensorpack.dataflow import common [as 別名]
# 或者: from tensorpack.dataflow.common import MapData [as 別名]
def get_dataflow(path, is_train, img_path=None):
ds = CocoPose(path, img_path, is_train) # read data from lmdb
if is_train:
ds = MapData(ds, read_image_url)
ds = MapDataComponent(ds, pose_random_scale)
ds = MapDataComponent(ds, pose_rotation)
ds = MapDataComponent(ds, pose_flip)
ds = MapDataComponent(ds, pose_resize_shortestedge_random)
ds = MapDataComponent(ds, pose_crop_random)
ds = MapData(ds, pose_to_img)
# augs = [
# imgaug.RandomApplyAug(imgaug.RandomChooseAug([
# imgaug.GaussianBlur(max_size=3)
# ]), 0.7)
# ]
# ds = AugmentImageComponent(ds, augs)
ds = PrefetchData(ds, 1000, multiprocessing.cpu_count()-1)
else:
ds = MultiThreadMapData(ds, nr_thread=16, map_func=read_image_url, buffer_size=1000)
ds = MapDataComponent(ds, pose_resize_shortestedge_fixed)
ds = MapDataComponent(ds, pose_crop_center)
ds = MapData(ds, pose_to_img)
ds = PrefetchData(ds, 100, multiprocessing.cpu_count() // 4)
return ds 示例6: _get_dataflow_onlyread
# 需要導入模塊: from tensorpack.dataflow import common [as 別名]
# 或者: from tensorpack.dataflow.common import MapData [as 別名]
def _get_dataflow_onlyread(path, is_train, img_path=None):
ds = CocoPose(path, img_path, is_train) # read data from lmdb
ds = MapData(ds, read_image_url)
ds = MapData(ds, pose_to_img)
# ds = PrefetchData(ds, 1000, multiprocessing.cpu_count() * 4)
return ds