Fix processes without forking for CUDA

This commit is contained in:
Christos Falas 2025-01-01 16:03:38 +00:00
parent 5f6824378c
commit efe8923c51
3 changed files with 64 additions and 73 deletions

View File

@ -1,5 +1,5 @@
import logging
import multiprocessing as mp
import torch.multiprocessing as mp
from typing import NamedTuple
from . import ingest
@ -14,35 +14,42 @@ logging.basicConfig(
)
mp.set_start_method("spawn")
mp.log_to_stderr(logging.INFO)
class Receiver(NamedTuple):
ip: ingest.Host
receiver: ingest.FeitReceiver
queue: "mp.Queue[ingest.CSI]"
manager = mp.Manager()
receivers = [
Receiver(ip, ingest.FeitReceiver(ip, mp.Queue(config.SAMPLE_RATE)))
Receiver(ip, ingest.FeitReceiver(ip), manager.Queue(config.SAMPLE_RATE))
for ip in config.RECEIVE_HOSTS
]
# Start injecting CSI frames
transmitter = ingest.FeitTransmitter()
webapp_queue: "mp.Queue[aoa.AoA]" = mp.Queue(config.SAMPLE_RATE)
webapp_queue: "mp.Queue[aoa.AoA]" = manager.Queue(config.SAMPLE_RATE)
processor = ingest.CSIProcessor(
{r.ip: r.receiver.processing_queue for r in receivers}, webapp_queue
)
visualise.aoa_queue = webapp_queue
# Start webapp in background process
webapp = mp.Process(target=visualise.start, args=(webapp_queue,))
webapp = mp.Process(target=visualise.start)
webapp.start()
receiver_processes = [mp.Process(target=r.receiver.listen) for r in receivers]
receiver_processes = [
mp.Process(target=r.receiver.listen, args=(r.queue,)) for r in receivers
]
for proc in receiver_processes:
proc.start()
processing_thread = mp.Process(target=processor.process_forever)
processing_thread = mp.Process(
target=ingest.CSIProcessor.process_forever,
args=({r.ip: r.queue for r in receivers}, webapp_queue),
)
processing_thread.start()
processing_thread.join()

View File

@ -33,7 +33,7 @@ class FeitTransmitter:
class FeitReceiver:
def __init__(self, host: Host, processing_queue: "mp.Queue[CSI]"):
def __init__(self, host: Host):
self.host = host
self.server = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.server.connect(host)
@ -48,9 +48,8 @@ class FeitReceiver:
self.logger = logging.getLogger(
f"{__name__}.{self.__class__.__name__}-{self.host}"
)
self.processing_queue = processing_queue
def listen(self):
def listen(self, queue: "mp.Queue[CSI]"):
prev_time = datetime.now()
while True:
# This is the max size of a UDP packet. The size of the actual CSI
@ -63,83 +62,71 @@ class FeitReceiver:
f"Received CSI data after {datetime.now() - prev_time}"
)
prev_time = datetime.now()
self.processing_queue.put(csidata)
queue.put(csidata)
except struct.error:
self.logger.error("Failed to parse CSI data")
class CSIProcessor:
def __init__(
self,
receiver_connections: dict[Host, "mp.Queue[CSI]"],
webserver: "mp.Queue[AoA]",
):
self.pending_data: dict[Host, tuple[datetime, CSI]] = {}
self.pending_data_lock = mp.Lock()
self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")
self.last_processed = datetime.now()
def __init__(self):
self.preprocess = Preprocessor()
self.aoa = AoA()
self.connections = receiver_connections
self.webserver = webserver
def add_data(self, host: Host, data: CSI):
if (
host in self.pending_data
and self.last_processed < self.pending_data[host][0]
@staticmethod
def process_data(
data: dict[Host, tuple[datetime, CSI]],
webserver: "mp.Queue[AoA]",
preprocess: Preprocessor,
aoa: AoA,
logger: logging.Logger,
):
self.logger.warning(
f"Skipping data from {host} at {self.pending_data[host][0]}"
)
with self.pending_data_lock:
self.pending_data[host] = (datetime.now(), data)
# Useful for figuring out the correct antenna order - RSSI values will decrease
# when the specific antenna is disconnected
rssis = [
(ip, csi.header.rssi1, csi.header.rssi2)
for ip, (_, csi) in sorted(self.pending_data.items())
for ip, (_, csi) in sorted(data.items())
]
self.logger.debug("Antenna RSSI values: {}".format(rssis))
logger.debug("Antenna RSSI values: {}".format(rssis))
def is_ready(self):
for host in config.RECEIVE_HOSTS:
if (
host not in self.pending_data
or self.pending_data[host][0] <= self.last_processed
):
return False
return True
def process_data(self):
self.last_processed = datetime.now()
antenna_data = [
np.expand_dims(self.pending_data[ip][1].matrix[:, antenna], axis=2)
np.expand_dims(data[ip][1].matrix[:, antenna], axis=2)
for ip, antenna in config.ANTENNA_ORDER
]
# We have data from all servers
all_data = np.concat(antenna_data, axis=1)
self.logger.info(f"Got final CSI data with shape {all_data.shape}")
logger.info(f"Got final CSI data with shape {all_data.shape}")
processed = self.preprocess.preprocess(all_data)
processed = preprocess.preprocess(all_data)
processed_tensor = torch.tensor(processed, device=device)
# visualise.add_data(all_data_tensor, processed)
self.aoa.update(processed_tensor)
self.logger.info("Processed data")
if not self.webserver.full():
self.webserver.put(self.aoa)
aoa.update(processed_tensor)
logger.info("Processed data")
if not webserver.full():
webserver.put(aoa)
@staticmethod
def process_forever(
connections: dict[Host, "mp.Queue[CSI]"], webserver: "mp.Queue[AoA]"
):
latest_data: dict[Host, tuple[datetime, CSI]] = {}
preprocessor = Preprocessor()
aoa = AoA()
logger = mp.get_logger()
logger.info("Starting processing loop")
def process_forever(self):
while True:
for ip, queue in self.connections.items():
for ip, queue in connections.items():
while not queue.empty():
self.add_data(ip, queue.get())
if self.is_ready():
self.process_data()
latest_data[ip] = (datetime.now(), queue.get())
sample_ready = all(ip in latest_data for ip in connections)
if sample_ready:
CSIProcessor.process_data(
latest_data, webserver, preprocessor, aoa, logger
)
latest_data = {}
else:
self.logger.debug("Not all data is ready")
logger.debug("Not all data is ready")
time.sleep(0.001)

View File

@ -141,8 +141,5 @@ def aoa_tof():
)
def start(conn: "mp.Queue[AoA]"):
global aoa_queue
aoa_queue = conn
def start():
app.run(debug=True, use_reloader=False, host="0.0.0.0")