def test(): p_l_pair_path, _ = get_modalality_path() context = p_l_pair_path[:, 1] p_model, l_model, _, _, _, _ = model(pointcloud_dim, language_dim, trajectory_dim) p_model.load_weights('Weights/joint_pl_p_weights.h5', by_name=True) l_model.load_weights('Weights/joint_pl_l_weights.h5', by_name=True) pc_data = np.load('Processed_data/pc_data_248.npy') l_data = np.load('Processed_data/l_data_248.npy') pc_embedd = p_model.predict(pc_data) l_embedd = l_model.predict(l_data) n_pair = 248 sim_matrix = np.zeros([n_pair, n_pair]) for i in range(n_pair): pc_vector = pc_embedd[i] sim_score = np.sum(pc_vector * l_embedd, axis=-1) sim_matrix[i] = sim_score print('context index:', i, 'magnitude:', np.linalg.norm(pc_vector)) print('--------------', context[i]) print('most relevant:', context[sim_score.argmax()]) print('most irrelevant:', context[sim_score.argmin()]) return
def test(): _, p_l_t_pair_path = get_modalality_path() context = p_l_t_pair_path[:, 1] _, _, _, _, p_l_embedding, traj_model = model(pointcloud_dim, language_dim, trajectory_dim) p_l_embedding.load_weights('Weights/joint_pltau_pl_weights.h5', by_name=True) traj_model.load_weights('Weights/joint_pltau_tau_weights.h5', by_name=True) pc_data = np.load('Processed_data/pc_data_1225.npy') l_data = np.load('Processed_data/l_data_1225.npy') traj_data = np.load('Processed_data/traj_data_1225.npy') distance_matrix = np.load('traj_distance_matrix.npy') pl_embedd = p_l_embedding.predict([pc_data, l_data]) traj_embedd = traj_model.predict(traj_data) n_pair = 1225 sim_matrix = np.zeros([n_pair, n_pair]) for i in range(n_pair): pl_vector = pl_embedd[i] sim_score = np.sum(pl_vector * traj_embedd, axis=-1) / np.linalg.norm( pl_vector) / np.linalg.norm(traj_embedd, axis=-1) sim_matrix[i] = sim_score print('traj index:', i, context[i]) print('most relevant:', context[sim_score.argmax()]) print('most irrelevant:', context[sim_score.argmin()], 'original:', context[distance_matrix[i].argmax()]) print( '-------------------------------------------------------------------------' ) return
def training_data_preparation(): ### prepare training data p_l_pair_path, p_l_t_pair_path = get_modalality_path() traj_paths = p_l_t_pair_path[:, 2] # list of all trajectory files: 1225 traj_samples = traj_paths.shape[0] x_train = np.zeros([traj_samples, trajectory_dim]) # -1~1 for i in range(traj_samples): ### preprocess trajectory traj_path = traj_paths[i] traj_vector = preprocess_trajectory(traj_path) ### feed into modal data x_train[i] = traj_vector return x_train
def training_data_preparation(): ### prepare training data p_l_pair_path, p_l_t_pair_path = get_modalality_path() p_paths = p_l_pair_path[:, 0] # list of all pointcloud paths: 248 p_samples = p_paths.shape[0] x_train = np.zeros([p_samples, pointcloud_dim]) for i in range(p_samples): # print(i) pc_path = p_paths[i] pc_vector = preprocess_pointcloud(pc_path) x_train[i] = pc_vector # np.save('Processed_data/pc_data_248.npy', x_train) # exit() return x_train
def training_data_preparation(): ### prepare training data p_l_pair_path, p_l_t_pair_path = get_modalality_path() context = p_l_pair_path[:, 1] # list of all language instructions: 248 sentences tokenizer, reverse_tokenizer = language_tokenizer(context, num_words=language_dim) l_samples = context.shape[0] x_train = np.zeros([l_samples, language_dim]) for i in range(l_samples): language = context[i] l_vector = preprocess_language(language, tokenizer) x_train[i] = l_vector # np.save('Processed_data/l_data_248.npy', x_train) # exit() return x_train