def __init__(self,
filename="",
remote_filename="",
cores="4",
storage=None,
steps_per_frame=1):
"""
Initialize the tracker
"""
self.name = 'PVMtracker'
if filename == "":
filename = storage.get(remote_filename)
self.prop_dict = CoreUtils.load_model(filename)
logging.info("Loaded the dictionary %s", filename)
PVM_Create.upgrade_dictionary_to_ver1_0(self.prop_dict)
self.prop_dict['num_proc'] = int(cores)
for k in range(len(self.prop_dict['learning_rates'])):
self.prop_dict['learning_rates'][k][0] = 0.0
logging.info("Setting learning rate in layer %d to zero" % k)
self.prop_dict["readout_learning_rate"][0] = 0.0
logging.info("Setting readout learning rate to zero")
self.manager = Manager(self.prop_dict, 1000)
self.executor = CoreUtils.ModelExecution(prop_dict=self.prop_dict,
manager=self.manager,
port=9100)
self.executor.start(blocking=False)
self.threshold = 32
self.image_size = self.prop_dict['input_array'].shape[:2][::-1]
self.readout_heatmap = np.zeros(self.image_size, dtype=np.float)
self.step_per_frame = steps_per_frame
def run_demo():
"""
In this very simple demo a set of workers operate on a 500x500 image domain by randomly flipping
selected bits. To make things faster the bit/byte flipping function is written in cython.
:return:
"""
filename = "demo00_state.p.gz"
if os.path.isfile(filename):
state_dict = CoreUtils.load_model(filename)
else:
state_dict = generate_dict()
manager = Manager(state_dict, 1000)
executor = CoreUtils.ModelExecution(prop_dict=state_dict, manager=manager)
executor.start(blocking=True)
CoreUtils.save_model(state_dict, filename)
print("Saving and running again in non blocking mode")
executor.start(blocking=False)
while manager.running():
executor.step()
executor.finish()
CoreUtils.save_model(state_dict, filename)