Source code for matx.vision.reduce_op

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from typing import Any, List
from .constants._sync_mode import ASYNC
from ..native import make_native_object

import sys
matx = sys.modules['matx']


class _SumOpImpl:
    """ Sum Impl """

    def __init__(self, device: Any, per_channel: bool = False) -> None:
        self.op: matx.NativeObject = make_native_object(
            "VisionSumOrMeanGeneralOp", device())
        self.per_channel: bool = per_channel

    def __call__(self,
                 images: List[matx.runtime.NDArray],
                 sync: int = ASYNC) -> matx.runtime.NDArray:
        return self.op.process(images, self.per_channel, False, sync)


[docs]class SumOp: """ Sum over each image. """
[docs] def __init__(self, device: Any, per_channel: bool = False) -> None: """ Initialize SumOp Args: device (Any) : the matx device used for the operation. per_channel (bool, optional) : if True, sum over each channel; if False, sum over the whole image. """ self.op: _SumOpImpl = matx.script(_SumOpImpl)(device, per_channel)
[docs] def __call__(self, images: List[matx.runtime.NDArray], sync: int = ASYNC) -> matx.runtime.NDArray: """ Sum over each image. Args: images (List[matx.runtime.NDArray]) : target images. sync (int, optional): sync mode after calculating the output. when device is cpu, the params makes no difference. ASYNC -- If device is GPU, the whole calculation process is asynchronous. SYNC -- If device is GPU, the whole calculation will be blocked until this operation is finished. SYNC_CPU -- If device is GPU, the whole calculation will be blocked until this operation is finished, and the corresponding CPU array would be created and returned. Defaults to ASYNC. Returns: matx.runtime.NDArray: summation result. For N images, the result would be shape Nx1 if per_channel is False, otherwise NxC where C is the image channel size. Example: >>> import cv2 >>> import matx >>> from matx.vision import SumOp >>> # Get origin_image.jpeg from https://github.com/bytedance/matxscript/tree/main/test/data/origin_image.jpeg >>> image = cv2.imread("./origin_image.jpeg") >>> device_id = 0 >>> device_str = "gpu:{}".format(device_id) >>> device = matx.Device(device_str) >>> # Create a list of ndarrays for batch images >>> batch_size = 3 >>> nds = [matx.array.from_numpy(image, device_str) for _ in range(batch_size)] >>> op = SumOp(device, per_channel = False) >>> ret = op(nds) """ return self.op(images, sync)
class _MeanOpImpl: """ Mean Impl """ def __init__(self, device: Any, per_channel: bool = False) -> None: self.op: matx.NativeObject = make_native_object( "VisionSumOrMeanGeneralOp", device()) self.per_channel: bool = per_channel def __call__(self, images: List[matx.runtime.NDArray], sync: int = ASYNC) -> matx.runtime.NDArray: return self.op.process(images, self.per_channel, True, sync)
[docs]class MeanOp: """ Calculate mean over each image. """
[docs] def __init__(self, device: Any, per_channel: bool = False) -> None: """ Initialize MeanOp Args: device (Any) : the matx device used for the operation. per_channel (bool, optional) : if True, calculate mean over each channel; if False, calculate mean over the whole image. """ self.op: _MeanOpImpl = matx.script(_MeanOpImpl)(device, per_channel)
[docs] def __call__(self, images: List[matx.runtime.NDArray], sync: int = ASYNC) -> matx.runtime.NDArray: """ Calculate mean over each image. Args: images (List[matx.runtime.NDArray]) : target images. sync (int, optional): sync mode after calculating the output. when device is cpu, the params makes no difference. ASYNC -- If device is GPU, the whole calculation process is asynchronous. SYNC -- If device is GPU, the whole calculation will be blocked until this operation is finished. SYNC_CPU -- If device is GPU, the whole calculation will be blocked until this operation is finished, and the corresponding CPU array would be created and returned. Defaults to ASYNC. Returns: matx.runtime.NDArray: mean result. For N images, the result would be shape Nx1 if per_channel is False, otherwise NxC where C is the image channel size. Example: >>> import cv2 >>> import matx >>> from matx.vision import MeanOp >>> # Get origin_image.jpeg from https://github.com/bytedance/matxscript/tree/main/test/data/origin_image.jpeg >>> image = cv2.imread("./origin_image.jpeg") >>> device_id = 0 >>> device_str = "gpu:{}".format(device_id) >>> device = matx.Device(device_str) >>> # Create a list of ndarrays for batch images >>> batch_size = 3 >>> nds = [matx.array.from_numpy(image, device_str) for _ in range(batch_size)] >>> op = MeanOp(device, per_channel = False) >>> ret = op(nds) """ return self.op(images, sync)