test_AE.py File Reference

Classes
class	test_AE.Tester

Namespaces
	test_AE

Variables
bool	test_AE.TEST_TRAINED = True
int	test_AE.seed = 3
string	test_AE.name = 'Billiard_AE'
	test_AE.device = torch.device('cpu')
string	test_AE.load_path_AES = '/home/giuseppe/src/taxons/experiments/{}_Surprise/{}'
string	test_AE.load_path_AEN = '/home/giuseppe/src/taxons/experiments/{}_Novelty/{}'
string	test_AE.load_path_Mixed = '/home/giuseppe/src/taxons/experiments/{}_Mixed/{}'
string	test_AE.load_path_NT = '/home/giuseppe/src/taxons/experiments/{}_NoTrain/{}'
string	test_AE.env_tag = "Billiard-v0"
int	test_AE.number_of_samples = 10
	test_AE.AEN = Tester(load_path_AEN, device)
	test_AE.AES = Tester(load_path_AES, device)
	test_AE.Mixed = Tester(load_path_Mixed, device)
	test_AE.NT = Tester(load_path_NT, device)
list	test_AE.x_test = []
	test_AE.env = gym.make(env_tag)
	test_AE.RANDOM_BALL_INIT_POSE
	test_AE.RANDOM_ARM_INIT_POSE
	test_AE.CoM = np.array([env.env.data.qpos[:2]])
	test_AE.tmp = env.render(mode='rgb_array')
	test_AE.images_test = torch.Tensor(x_test).permute(0, 3, 1, 2).to(device)
	test_AE.fig
	test_AE.ax
	test_AE.N_loss
	test_AE.N_f
	test_AE.N_y = N_y.permute(0, 2, 3, 1)[0]
	test_AE.S_loss
	test_AE.S_f
	test_AE.S_y = S_y.permute(0, 2, 3, 1)[0]
	test_AE.M_loss
	test_AE.M_f
	test_AE.M_y = M_y.permute(0, 2, 3, 1)[0]
	test_AE.NT_loss
	test_AE.NT_f
	test_AE.NT_y = NT_y.permute(0, 2, 3, 1)[0]
	test_AE.subs = AEN.selector.subsample(images_test[k:k+1])
	test_AE.img = np.array(N_y.cpu().data * 255)