def __init__(self, model: d5.ops.OnnxModel, device: d5.DeviceType,
events: List[d5.ExecutorEvent] = None):
super(TensorflowGraphExecutor, self).__init__(TensorflowNetwork(device),
events)
self.setup_done = False
self.model = model
self.sess = None
visitor = TensorflowVisitor()
model.accept(visitor, self.network)
self.is_training = visitor.is_training
def __init__(self,
model: d5.ops.OnnxModel,
device_option: DeviceOption,
events: List[d5.ExecutorEvent] = []):
super(Caffe2GraphExecutor, self).__init__(Caffe2Network(device_option),
events)
self.device_option = device_option
self.model = model
with core.DeviceScope(self.device_option):
model.accept(Caffe2Visitor(device_option), self.network)
def __init__(self, model: d5.ops.OnnxModel, device: d5.DeviceType,
events: List[d5.ExecutorEvent] = []):
super().__init__(model, events)
self.devname = 'cuda' if device is None or device.is_gpu() else 'cpu'
self.network = PyTorchNetwork(device)
self.visitor = PyTorchVisitor()
self.is_training = False
model.accept(self.visitor, self.network)
self.model = self.visitor.model.to(self.devname)
self.model.eval()
new_network = PyTorchNativeNetwork(self.model)
new_network.outputs = self.network.outputs
self.network = new_network
torch.cuda.empty_cache()