class Model(object):
def __init__(self):
self.model = CAE()
model_dict = torch.load('models/CAE_model', map_location='cpu')
self.model.load_state_dict(model_dict)
self.model.eval
def decoder(self, img, s, z):
img = img / 128.0 - 1.0
img = np.transpose(img, (2, 0, 1))
img = torch.FloatTensor([img])
s = torch.FloatTensor([s])
z = torch.FloatTensor([z])
context = (img, s, z)
a_tensor = self.model.decoder(context)
a_numpy = a_tensor.detach().numpy()[0]
return list(a_numpy)
class Model(object):
def __init__(self):
self.model = CAE()
model_dict = torch.load('models/CAE_best_model', map_location='cpu')
self.model.load_state_dict(model_dict)
self.model.eval
def decoder(self, z, s):
if abs(z[0][0]) < 0.01:
return [0.0] * 6
# z = np.asarray([z])
z = np.asarray(z)
# print(s.shape)
z_tensor = torch.FloatTensor(np.concatenate((z,s),axis=1))
# print(z_tensor.shape)
a_tensor = self.model.decoder(z_tensor)
return a_tensor.tolist()
def encoder(self, a, s):
x = np.concatenate((a,s),axis=1)
x_tensor = torch.FloatTensor(x)
z = self.model.encoder(x_tensor)
return z.tolist()