def load_model():
dir = os.getcwd()
model_dir = dir + f'/Channel/encoder_16_VAE_1_epoch30.pth'
model = Encoder()
model.load_state_dict(torch.load(model_dir, map_location=device))
encoder_model = model.to(device)
return encoder_model
def __init__(self, device):
self.device = device
self.prediction = Prediction(64, self.device)
dir = os.getcwd()
model_dir16 = dir + f'/Gaussian/generative_model/encoder_16_VAE_0.5_epoch30.pth'
model16 = Encoder()
model16.load_state_dict(
torch.load(model_dir16, map_location=self.device), False)
self.encoder_model16 = model16.to(self.device)
self.encoder_model16.eval()
def load_model(size):
dir = os.getcwd()
if size == 16:
model_dir = dir+f'/Channel/encoder_16_VAE_1_epoch30.pth'
model = Encoder()
elif size == 32:
model_dir = dir+f'/Channel/encoder_16_32_VAE_1_0.7_epoch50.pth'
model = Encoder()
model.load_state_dict(torch.load(model_dir, map_location=device))
encoder_model =model.to(device)
return encoder_model
def __init__(self, device):
self.device = device
self.prediction = Prediction(64, self.device)
dir = os.getcwd()
model_dir16 = dir + f'/Channel/generative_model/encoder_16_VAE_1_epoch30.pth'
model16 = Encoder()
model16.load_state_dict(
torch.load(model_dir16, map_location=self.device), False)
self.encoder_model16 = model16.to(self.device)
self.encoder_model16.eval()
dir = os.getcwd()
model_dir_16_32 = dir + f'/Channel/generative_model/encoder_16_32_VAE_1_0.7_epoch50.pth'
model_16_32 = Encoder()
model_16_32.load_state_dict(
torch.load(model_dir_16_32, map_location=self.device), False)
self.encoder_model_16_32 = model_16_32.to(self.device)
self.encoder_model_16_32.eval()