def __init__(self, model):
super(PytorchEmitter, self).__init__()
if isinstance(model, _string_types):
network_path = model
else:
network_path = model[0]
weight_path = model[1]
self.init_code = str()
self.IR_graph = IRGraph(network_path)
self.IR_graph.build()
self._load_weights(weight_path)
folder = Folder(self.IR_graph, self.weights_dict)
folder.fold()
def __init__(self, model):
super(TensorflowEmitter, self).__init__()
from six import string_types as _string_types
if isinstance(model, _string_types):
network_path = model
else:
network_path = model[0]
self._load_weights(model[1])
self.IR_graph = IRGraph(network_path)
super(TensorflowEmitter, self)._build()
folder = Folder(self.IR_graph, self.weights_dict)
folder.fold()
def __init__(self, model):
super(Keras2Emitter, self).__init__()
from six import string_types as _string_types
if isinstance(model, _string_types):
network_path = model
else:
network_path = model[0]
weight_path = model[1]
self._load_weights(weight_path)
self.IR_graph = IRGraph(network_path)
self.IR_graph.build()
self.yolo_parameter = []
self.region_parameter = []
self.layers_codes_count = dict()
folder = Folder(self.IR_graph, self.weights_dict)
folder.fold()
def __init__(self, model):
super(MXNetEmitter, self).__init__()
from six import string_types as _string_types
if isinstance(model, _string_types):
network_path = model
self.weight_loaded = False
elif len(model) == 3:
network_path = model[0]
weight_path = model[1]
self.output_weights_file = model[2]
self.weights = np.load(weight_path).item()
self.weight_loaded = True
self.output_weights = dict()
else:
raise ValueError("the # of input arguments [{}] is not supported" % len(model))
self.IR_graph = IRGraph(network_path)
self.IR_graph.build()
folder = Folder(self.IR_graph, self.weights)
folder.fold()