print("batch_size: " + str(args.batch_size))
print("data_worker: " + str(args.data_worker))
print("model: " + str(args.model))
print("port: " + str(args.port))
#torch.manual_seed(1)
bm.register('get_epoch_event')
bm.register('get_global_event')
bm.register('get_grad_queue')
bm.register('get_grad_queue2')
bm.register('get_targets_queue')
bm.register('get_save_event')
bm.register('get_backward_event')
bm.register('get_start_thread_event')
bm.register('get_start_thread_event2')
m = bm(address=(args.ip, args.port), authkey=b'xpipe')
m.connect()
global_event = m.get_global_event()
epoch_event = m.get_epoch_event()
grad_queue = m.get_grad_queue()
targets_queue = m.get_targets_queue()
save_event = m.get_save_event()
start_event = m.get_start_thread_event()
grad_queue2 = m.get_grad_queue2()
start_event2 = m.get_start_thread_event2()
# node_cfg_0 = [64, 64, 'M', 128, 128, 'M']
# node_cfg_1 = [256, 256, 256, 256, 'M']
# node_cfg_2 = [512, 512, 512, 512, 'M']
# node_cfg_3 = [512, 512, 512, 512, 'M']
def get_best_acc(self):
return self.best_acc
def get_epoch_event():
return epoch_event
def get_global_event():
return global_event
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-ip', help='the ip of manager server', default='89.72.2.41')
parser.add_argument('-path', help='the path of share system')
args = parser.parse_args()
if os.path.exists(args.path):
os.remove(args.path)
bm.register('get_epoch_event', callable=get_epoch_event)
bm.register('get_global_event', callable=get_global_event)
bm.register('get_acc', Acc)
m = bm(address=(args.ip, 5000), authkey=b'xpipe')
m.start()
g_e = m.get_global_event()
print("master run......")
g_e.wait()
m.shutdown()
print("master shutdown........")
parser.add_argument('--weights', '-w', type=str, default='weights/yolov3.weights', help='weights file')
parser.add_argument('--config', '-c', type=str, default='cfg/yolo_v3.cfg', help='net configure file')
parser.add_argument('--cuda', action='store_true', default=False, help='if specified, gpu will be used')
parser.add_argument('--detect', action='store_true', default=False, help='if specified, frames with detecting bounding box will be outputed')
parser.add_argument('--save', '-s', type=str, default='video_frames', help='the path to save detection results')
parser.add_argument('--conf_thresh', '-ct', type=float, default=0.3, help='confidence thresh')
parser.add_argument('--nms_thresh', '-nt', type=float, default=0.4, help='nms thresh')
parser.add_argument('--class_names', '-n', type = str, default='data/coco.names', help = 'specify the file which contains the class names')
parser.add_argument('--size', type=int, default = 416, help = 'the size of image, must be the times of 32')
args, _ = parser.parse_known_args()
send_end_signal = Value('i', 0)
send_end = Value('i', 0)
pro_end = Value('i', 0)
ori_q = queue.Queue()
det_q = queue.Queue()
bm.register('get_ori', callable = lambda: ori_q)
bm.register('get_det', callable = lambda: det_q)
bm.register('add_send_end', callable = add_send_end)
bm.register('get_send_end', callable = get_send_end)
bm.register('add_pro_end', callable = add_pro_end)
bm.register('get_pro_end', callable = get_pro_end)
m = bm(address=('10.66.30.45',10000),authkey = b'abc')
m.start()
p1 = Process(target = process_img, args = (args, ))
p2 = Process(target = super_send_end)
p1.start()
p2.start()
p1.join()
p2.join()
parser.add_argument('--save_path',
'-sp',
type=str,
default=None,
help='the path to save your detection video results')
args, _ = parser.parse_known_args()
pro_end_signal = Value('i', 0)
bm.register('get_ori')
bm.register('get_det')
bm.register('add_send_end')
bm.register('get_send_end')
bm.register('add_pro_end')
bm.register('get_pro_end')
m = bm(address=('geeekvr.com', 8014), authkey=b'abc')
m.connect()
if args.video:
p1 = Process(target=send_frames_video,
args=(args.video_file_path, args.frame_size))
else:
p1 = Process(target=send_frames,
args=(args.frame_path, args.frame_size))
p2 = Process(target=rcv_frames,
args=(args.write_video, args.save_path, args.frame_size))
p3 = Process(target=super_pro_end)
p1.start()
p2.start()
p3.start()
p1.join()
import psutil
import gc
import numpy as np
"""
pipeline ResNet script for Tianhe-2 with gpu cluster
"""
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-flag', type=int, help='the size of queue', default=2)
args = parser.parse_args()
bm.register('get_queue')
m = bm(address=('127.0.0.1', 5000), authkey=b'xpipe')
m.connect()
queue = m.get_queue()
if args.flag == 0:
for i in range(10):
#value = np.random.rand(2,2)
value = torch.randn([128, 6, 6, 64])
#print(value)
print("----")
queue.put(value)
time.sleep(1)
else:
for i in range(10):
value = queue.get()
#print(value)
print("----")
