def train_blocking(self):
if (self.uobject.VerbosePythonLog):
ue.log(self.uobject.TensorFlowModule +
' training started on bt thread.')
#calculate the time it takes to train your network
start = time.time()
self.trained = self.tfapi.onBeginTraining()
stop = time.time()
if self.trained is None:
ue.log(
'Training Note: no summary object returned from training. See your onBeginTraining(): method'
)
return
if 'summary' in self.trained:
summary = self.trained['summary']
else:
summary = {}
summary['elapsed'] = stop - start
#run callbacks only if we're still in a valid game world
if (self.ValidGameWorld):
ue.run_on_gt(self.training_complete, summary)
def custom_event(self, event, data=None, useJson=False):
#embedd the data
eventdata = {}
eventdata['event'] = event
eventdata['data'] = data
eventdata['useJson'] = useJson
ue.run_on_gt(self.custom_event_gt, eventdata)
def setup_blocking(self):
#call the api setup (may be multi-threaded!)
self.tfapi.onSetup()
#run callbacks only if we're still in a valid game world
if (self.ValidGameWorld):
ue.run_on_gt(self.setup_complete)
def testaction(args=""):
ue.log('starting action with <' + str(args) + '>')
#onfinished()
#pretend you take time to finish
time.sleep(1)
ue.log('wait complete')
ue.run_on_gt(onfinished, args)
ue.run_on_gt(onfinishedempty)
def json_input_blocking(self, args):
#pass the raw json to the script to handle
resultJson = self.tfapi.onJsonInput(json.loads(args))
if (self.uobject.ShouldUseMultithreading):
#pass prediction json back
if (self.ValidGameWorld):
ue.run_on_gt(self.json_input_gt_callback, resultJson)
else:
self.json_input_gt_callback(resultJson)
def backgroundAction(args=None):
#ue.log(args)
action = args[0]
if len(args) > 1:
callback = args[1]
#call the blocking action
action()
#return the result if we have a callback
if callback:
ue.run_on_gt(callback)
def trainBlocking(self):
if(self.uobject.VerbosePythonLog):
ue.log(self.uobject.TensorFlowModule + ' training started on bt thread.')
#calculate the time it takes to train your network
start = time.time()
self.trained = self.tf.train()
stop = time.time()
if 'summary' in self.trained:
summary = self.trained['summary']
else:
summary = {}
summary['elapsed'] = stop-start
ue.run_on_gt(self.trainingComplete, summary)
def trainBlocking(self):
if (self.uobject.VerbosePythonLog):
ue.log(self.uobject.TensorFlowModule +
' training started on bt thread.')
#calculate the time it takes to train your network
start = time.time()
self.trained = self.tf.train()
stop = time.time()
if 'summary' in self.trained:
summary = self.trained['summary']
else:
summary = {}
summary['elapsed'] = stop - start
ue.run_on_gt(self.trainingComplete, summary)
def train_blocking(self):
if (self.uobject.VerbosePythonLog):
ue.log(self.uobject.TensorFlowModule +
' training started on bt thread.')
#calculate the time it takes to train your network
start = time.time()
self.trained = self.tfapi.onBeginTraining()
stop = time.time()
if hasattr(self.trained, 'summary'):
summary = self.trained['summary']
else:
summary = {}
summary['elapsed'] = stop - start
#run callbacks only if we're still in a valid game world
if (self.ValidGameWorld):
ue.run_on_gt(self.training_complete, summary)
def backgroundAction(args=None):
#ue.log(args)
action = args[0]
actionArgs = None
if len(args) > 1:
actionArgs = args[1]
if len(args) > 2:
callback = args[2]
#call the blocking action
if actionArgs:
result = action(actionArgs)
else:
result = action()
#return the result if we have a callback
if callback:
if result:
ue.run_on_gt(callback, result)
else:
ue.run_on_gt(callback)
def doLongTask():
ue.log('started my task')
for x in range(1, 10):
time.sleep(0.5)
ue.run_on_gt(progresscallback, x)
