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update detection handoff to use shared memory

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This commit is contained in:
Blake Blackshear 2020-09-24 06:58:23 -05:00
parent ec4d048905
commit 574ee2a46f
2 changed files with 54 additions and 30 deletions
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43
benchmark.py
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@ -11,7 +11,7 @@ labels = load_labels('/labelmap.txt')
###### ######
# Minimal same process runner # Minimal same process runner
###### ######
# object_detector = ObjectDetector() # object_detector = LocalObjectDetector()
# tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0) # tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
# start = datetime.datetime.now().timestamp() # start = datetime.datetime.now().timestamp()
@ -40,8 +40,8 @@ labels = load_labels('/labelmap.txt')
###### ######
# Separate process runner # Separate process runner
###### ######
def start(id, num_detections, detection_queue): def start(id, num_detections, detection_queue, event):
object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue) object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue, event)
start = datetime.datetime.now().timestamp() start = datetime.datetime.now().timestamp()
frame_times = [] frame_times = []
@ -54,26 +54,35 @@ def start(id, num_detections, detection_queue):
print(f"{id} - Processed for {duration:.2f} seconds.") print(f"{id} - Processed for {duration:.2f} seconds.")
print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms") print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
edgetpu_process = EdgeTPUProcess() event = mp.Event()
edgetpu_process = EdgeTPUProcess({'1': event})
# start(1, 1000, edgetpu_process.detect_lock, edgetpu_process.detect_ready, edgetpu_process.frame_ready) start(1, 1000, edgetpu_process.detection_queue, event)
print(f"Average raw inference speed: {edgetpu_process.avg_inference_speed.value*1000:.2f}ms")
#### ####
# Multiple camera processes # Multiple camera processes
#### ####
camera_processes = [] # camera_processes = []
for x in range(0, 10):
camera_process = mp.Process(target=start, args=(x, 100, edgetpu_process.detection_queue))
camera_process.daemon = True
camera_processes.append(camera_process)
start = datetime.datetime.now().timestamp() # pipes = {}
# for x in range(0, 10):
# pipes[x] = mp.Pipe(duplex=False)
for p in camera_processes: # edgetpu_process = EdgeTPUProcess({str(key): value[1] for (key, value) in pipes.items()})
p.start()
for p in camera_processes: # for x in range(0, 10):
p.join() # camera_process = mp.Process(target=start, args=(x, 100, edgetpu_process.detection_queue, pipes[x][0]))
# camera_process.daemon = True
# camera_processes.append(camera_process)
duration = datetime.datetime.now().timestamp()-start # start = datetime.datetime.now().timestamp()
print(f"Total - Processed for {duration:.2f} seconds.")
# for p in camera_processes:
# p.start()
# for p in camera_processes:
# p.join()
# duration = datetime.datetime.now().timestamp()-start
# print(f"Total - Processed for {duration:.2f} seconds.")

41
frigate/edgetpu.py
View File

@ -102,12 +102,21 @@ class LocalObjectDetector(ObjectDetector):
return detections return detections
def run_detector(detection_queue, result_connections: Dict[str, Connection], avg_speed, start, tf_device): def run_detector(detection_queue, out_events: Dict[str, mp.Event], avg_speed, start, tf_device):
print(f"Starting detection process: {os.getpid()}") print(f"Starting detection process: {os.getpid()}")
listen() listen()
frame_manager = SharedMemoryFrameManager() frame_manager = SharedMemoryFrameManager()
object_detector = LocalObjectDetector(tf_device=tf_device) object_detector = LocalObjectDetector(tf_device=tf_device)
outputs = {}
for name in out_events.keys():
out_shm = mp.shared_memory.SharedMemory(name=f"out-{name}", create=False)
out_np = np.ndarray((20,6), dtype=np.float32, buffer=out_shm.buf)
outputs[name] = {
'shm': out_shm,
'np': out_np
}
while True: while True:
connection_id = detection_queue.get() connection_id = detection_queue.get()
input_frame = frame_manager.get(connection_id, (1,300,300,3)) input_frame = frame_manager.get(connection_id, (1,300,300,3))
@ -115,20 +124,21 @@ def run_detector(detection_queue, result_connections: Dict[str, Connection], avg
if input_frame is None: if input_frame is None:
continue continue
# detect and put the output in the plasma store # detect and send the output
start.value = datetime.datetime.now().timestamp() start.value = datetime.datetime.now().timestamp()
# TODO: what is the overhead for pickling this result vs writing back to shared memory? # TODO: what is the overhead for pickling this result vs writing back to shared memory?
# I could try using an Event() and waiting in the other process before looking in memory... # I could try using an Event() and waiting in the other process before looking in memory...
detections = object_detector.detect_raw(input_frame) detections = object_detector.detect_raw(input_frame)
result_connections[connection_id].send(detections)
duration = datetime.datetime.now().timestamp()-start.value duration = datetime.datetime.now().timestamp()-start.value
outputs[connection_id]['np'][:] = detections[:]
out_events[connection_id].set()
start.value = 0.0 start.value = 0.0
avg_speed.value = (avg_speed.value*9 + duration)/10 avg_speed.value = (avg_speed.value*9 + duration)/10
class EdgeTPUProcess(): class EdgeTPUProcess():
def __init__(self, result_connections, tf_device=None): def __init__(self, out_events, tf_device=None):
self.result_connections = result_connections self.out_events = out_events
self.detection_queue = mp.Queue() self.detection_queue = mp.Queue()
self.avg_inference_speed = mp.Value('d', 0.01) self.avg_inference_speed = mp.Value('d', 0.01)
self.detection_start = mp.Value('d', 0.0) self.detection_start = mp.Value('d', 0.0)
@ -149,19 +159,21 @@ class EdgeTPUProcess():
self.detection_start.value = 0.0 self.detection_start.value = 0.0
if (not self.detect_process is None) and self.detect_process.is_alive(): if (not self.detect_process is None) and self.detect_process.is_alive():
self.stop() self.stop()
self.detect_process = mp.Process(target=run_detector, args=(self.detection_queue, self.result_connections, self.avg_inference_speed, self.detection_start, self.tf_device)) self.detect_process = mp.Process(target=run_detector, args=(self.detection_queue, self.out_events, self.avg_inference_speed, self.detection_start, self.tf_device))
self.detect_process.daemon = True self.detect_process.daemon = True
self.detect_process.start() self.detect_process.start()
class RemoteObjectDetector(): class RemoteObjectDetector():
def __init__(self, name, labels, detection_queue, result_connection: Connection): def __init__(self, name, labels, detection_queue, event):
self.labels = load_labels(labels) self.labels = load_labels(labels)
self.name = name self.name = name
self.fps = EventsPerSecond() self.fps = EventsPerSecond()
self.detection_queue = detection_queue self.detection_queue = detection_queue
self.result_connection = result_connection self.event = event
self.shm = mp.shared_memory.SharedMemory(name=self.name, create=True, size=300*300*3) self.shm = mp.shared_memory.SharedMemory(name=self.name, create=True, size=300*300*3)
self.np_shm = np.ndarray((1,300,300,3), dtype=np.uint8, buffer=self.shm.buf) self.np_shm = np.ndarray((1,300,300,3), dtype=np.uint8, buffer=self.shm.buf)
self.out_shm = mp.shared_memory.SharedMemory(name=f"out-{self.name}", create=True, size=20*6*4)
self.out_np_shm = np.ndarray((20,6), dtype=np.float32, buffer=self.out_shm.buf)
def detect(self, tensor_input, threshold=.4): def detect(self, tensor_input, threshold=.4):
detections = [] detections = []
@ -169,13 +181,16 @@ class RemoteObjectDetector():
# copy input to shared memory # copy input to shared memory
# TODO: what if I just write it there in the first place? # TODO: what if I just write it there in the first place?
self.np_shm[:] = tensor_input[:] self.np_shm[:] = tensor_input[:]
self.event.clear()
self.detection_queue.put(self.name) self.detection_queue.put(self.name)
if self.result_connection.poll(10): self.event.wait()
raw_detections = self.result_connection.recv()
else:
return detections
for d in raw_detections: # if self.result_connection.poll(10):
# raw_detections = self.result_connection.recv()
# else:
# return detections
for d in self.out_np_shm:
if d[1] < threshold: if d[1] < threshold:
break break
detections.append(( detections.append((