def make_streamer_schedule(self):
apps = []
for app, num_frozen, target_fps \
in zip(self.apps, self.num_frozen_list, self.target_fps_list):
a = app.copy()
a["num_frozen"] = num_frozen
a["target_fps"] = target_fps
apps.append(a)
s = Schedule.StreamerSchedule()
num_apps_done = 0
last_shared_layer = 1
parent_net = Schedule.NeuralNet(-1, -1, self.model, end=1)
while (num_apps_done < len(apps)):
min_frozen = min([app["num_frozen"] \
for app in apps if app["num_frozen"] > last_shared_layer])
min_apps = [app for app in apps \
if app["num_frozen"] == min_frozen]
future_apps = [app for app in apps \
if app["num_frozen"] > min_frozen]
# Check if we need to share part of the NN, and make a base NN
# If so, we make it and set it as the parent
if len(future_apps) > 0 or len(apps) == len(min_apps):
# Set target_fps depending on children target_fps
if len(future_apps) > 0:
parent_target_fps = max(
[app["target_fps"] for app in future_apps])
else:
parent_target_fps = max(
[app["target_fps"] for app in min_apps])
net = Schedule.NeuralNet(s.get_id(), -1, self.model,
parent_net.net_id, last_shared_layer,
min_frozen, True, parent_target_fps,
min_apps[0]["model_path"][min_frozen])
s.add_neural_net(net)
parent_net = net
# Make app-specific NN that is branched off the parent
# Parent is either nothing or the last shared branch
for app in min_apps:
net = Schedule.NeuralNet(s.get_id(), app["app_id"], self.model,
parent_net.net_id, parent_net.end,
self.model.final_layer, False,
app["target_fps"],
app["model_path"][min_frozen])
s.add_neural_net(net)
num_apps_done += 1
last_shared_layer = parent_net.end
return s.schedule
def make_streamer_schedule_no_sharing(self):
s = Schedule.StreamerSchedule()
for app in self.apps:
num_frozen = min(app["accuracies"].keys())
net = Schedule.NeuralNet(s.get_id(), app["app_id"], self.model, -1,
1, self.model.final_layer, False,
self.video_desc["stream_fps"],
app["model_path"][num_frozen])
s.add_neural_net(net)
return s.schedule
