Source code for matx.vision.tv_transforms.normalize

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from typing import Any, Tuple, List
import sys
matx = sys.modules['matx']
from .. import ASYNC, NormalizeOp

from ._base import BaseInterfaceClass, BatchBaseClass


[docs]class Normalize(BaseInterfaceClass):
[docs] def __init__(self, mean: List[float], std: List[float], global_scale: float = 1.0, device_id: int = -2, sync: int = ASYNC) -> None: super().__init__(device_id=device_id, sync=sync) self._mean: List[float] = mean self._std: List[float] = std self._global_scale: float = global_scale
[docs] def __call__(self, device: Any, device_str: str, sync: int) -> Any: return NormalizeImpl(device, device_str, self._mean, self._std, self._global_scale, sync)
class NormalizeImpl(BatchBaseClass): def __init__(self, device: Any, device_str: str, mean: List[float], std: List[float], global_scale: float = 1.0, sync: int = ASYNC) -> None: super().__init__() self.global_scale: float = global_scale self.mean: List[float] = [i / global_scale for i in mean] self.std: List[float] = [i / global_scale for i in std] self.device_str: str = device_str self.op: NormalizeOp = NormalizeOp(device, self.mean, self.std) self.sync: int = sync self.name: str = "Normalize" def _process(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: return self.op(imgs, sync=self.sync) def __repr__(self) -> str: return self.name + '(mean={0}, std={1}, device={2}, sync={3})'.format( self.mean, self.std, self.device_str, self.sync)