def __init__(self, inputs_list, model):
super().__init__()
self.node = model
######### Set up node #########
# Freeze parameters of node
for params in self.node.parameters():
params.requires_grad = False
self.indices_node = np.arange(len(self.node.amplitude))
######### set learnable parameters #########
control_list = self.set_controls(inputs_list)
###### Set everything as torch Tensors and send to DEVICE ######
self.inputs_list = TorchUtils.get_tensor_from_list(
inputs_list, torch.int64)
self.control_list = TorchUtils.get_tensor_from_list(
control_list, torch.int64
) # IndexError: tensors used as indices must be long, byte or bool tensors
def load_model(self, configs):
"""Loads a pytorch model from a directory string."""
self.info, state_dict = self.load_file(configs['torch_model_dict'], 'pt')
if 'smg_configs' in self.info.keys():
model_dict = self.info['smg_configs']['processor']
else:
model_dict = self.info
super().__init__(model_dict)
self.configs = configs
self.load_state_dict(state_dict)
self.init_max_and_min_values()
self.amplification = TorchUtils.get_tensor_from_list(self.info['data_info']['processor']['amplification'])
self.init_noise_configs()
def init_max_and_min_values(self):
self.offset = TorchUtils.get_tensor_from_list(self.info['data_info']['input_data']['offset'])
self.amplitude = TorchUtils.get_tensor_from_list(self.info['data_info']['input_data']['amplitude'])
self.min_voltage = self.offset - self.amplitude
self.max_voltage = self.offset + self.amplitude