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from typing import Any
from .constants._sync_mode import ASYNC
from .constants._data_format import *
from ..native import make_native_object
import sys
matx = sys.modules['matx']
class _TransposeOpImpl:
""" Impl: Convert image tensor layout, this operators only support gpu backend.
"""
def __init__(self,
device: Any,
input_layout: str,
output_layout: str) -> None:
self.op: matx.NativeObject = make_native_object(
"VisionTransposeGeneralOp", device())
self.input_layout: str = input_layout
self.output_layout: str = output_layout
def __call__(self,
images: matx.runtime.NDArray,
sync: int = ASYNC) -> matx.runtime.NDArray:
return self.op.process(images, self.input_layout, self.output_layout, sync)
[文档]class TransposeOp:
""" Convert image tensor layout, this operators only support gpu backend.
"""
[文档] def __init__(self,
device: Any,
input_layout: str,
output_layout: str) -> None:
""" Initialize TransposeOp
Args:
device (Any): the matx device used for the operation.
input_layout (str): the input image tensor layout. only suppport NCHW or NHWC.
output_layout (str): the desired image tensor layout. only support NCHW or NHWC.
"""
self.op: _TransposeOpImpl = matx.script(
_TransposeOpImpl)(device, input_layout, output_layout)
[文档] def __call__(self,
images: matx.runtime.NDArray,
sync: int = ASYNC) -> matx.runtime.NDArray:
""" Transpose image tensor.
Args:
images (matx.runtime.NDArray): input 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: Transpose images.
Example:
>>> import cv2
>>> import matx
>>> import numpy as np
>>> from matx.vision import TransposeOp
>>> # Get origin_image.jpeg from https://github.com/bytedance/matxscript/tree/main/test/data/origin_image.jpeg
>>> image = cv2.imread("./origin_image.jpeg")
>>> batch_image = np.stack([image, image, image, image])
>>> device_id = 0
>>> device_str = "gpu:{}".format(device_id)
>>> device = matx.Device(device_str)
>>> # Create a NHWC image tensor
>>> nds = matx.array.from_numpy(batch_image, device_str)
>>> op = TransposeOp(device=device,
input_layout=matx.vision.NHWC,
output_layout=matx.vision.NCHW)
>>> ret = op(nds)
"""
return self.op(images, sync)