def create_video_subproc(
frames: List[np.ndarray],
path: str,
filename: str,
extension: str = ".gif",
fps: int = 15,
daemon: bool = True,
):
"""
Create video with a subprocess, since it takes a lot of time for ``moviepy``
to encode the video file.
See Also:
:func:`.create_video`
Note:
if ``daemon`` is true, then this function cannot be used in a
daemonic subprocess.
Args:
frames: A list of numpy arrays of shape (H, W, C) or (H, W), and with
``dtype`` = any float or any int.
When a frame is float type, its value range should be [0, 1].
When a frame is integer type, its value range should be [0, 255].
path: Directory to save the video.
filename: File name.
extension: File extension.
fps: frames per second.
daemon: Whether launching the saving process as a daemonic process.
Returns:
A wait function, once called, block until creation has finished.
"""
def wait():
pass
if frames:
p = get_context("spawn").Process(
target=create_video, args=(frames, path, filename, extension, fps)
)
p.daemon = daemon
p.start()
def wait():
p.join()
return wait
def create_image_subproc(
image: np.array,
path: str,
filename: str,
extension: str = ".png",
daemon: bool = True,
):
"""
Create image with a subprocess.
See Also:
:func:`.create_image`
Note:
if ``daemon`` is true, then this function cannot be used in a
daemonic subprocess.
Args:
image: A numpy array of shape (H, W, C) or (H, W), and with
``dtype`` = any float or any int.
When a frame is float type, its value range should be [0, 1].
When a frame is integer type, its value range should be [0, 255].
path: Directory to save the image.
filename: File name.
extension: File extension.
daemon: Whether launching the saving process as a daemonic process.
Returns:
A wait function, once called, block until creation has finished.
"""
p = get_context("spawn").Process(
target=create_image, args=(image, path, filename, extension)
)
p.daemon = daemon
p.start()
def wait():
p.join()
return wait
from machin.parallel.pickle import dumps, loads
from machin.parallel.process import Process
from machin.parallel import get_context
import torch as t
def print_tensor_sub_proc(tens):
print(loads(tens))
def exec_sub_proc(func):
loads(func)()
if __name__ == "__main__":
spawn_ctx = get_context("spawn")
fork_ctx = get_context("fork")
# cpu tensor, not in shared memory
# If you would like to pass this tensor to a sub process
# set copy_tensor to `True`, otherwise only a pointer to
# memory will be passed to the subprocess.
# However, if you do this in the same process, no SEGFAULT
# will happen, because memory map is the same.
tensor = t.ones([10])
p = Process(target=print_tensor_sub_proc,
args=(dumps(tensor, copy_tensor=True), ),
ctx=fork_ctx)
p.start()
p.join()
# cpu tensor, in shared memory