def __load_data_set(self):
tools.printf("Loading training data...")
self.train_data_gen = data_roller.StatefulRollerDataGen(self.cfg, config.dataset_path, self.train_sequences,
frames=self.train_frames_range)
tools.printf("Loading validation data...")
self.val_data_gen = data_roller.StatefulRollerDataGen(self.cfg, config.dataset_path, [self.val_sequence],
frames=self.val_frames_range)
import data_roller
import config
cfg = config.SeqTrainConfigsSmallSteps
data_roller.StatefulRollerDataGen(cfg, "/home/cs4li/Dev/KITTI/dataset/",
["06"])
# val_merged_summary_op = tf.summary.merge([val_loss_sum, val_fc_sum, val_se3_sum])
val_loss_sum = tf.summary.scalar("training_loss_val", total_losses)
val_fc_sum = tf.summary.scalar("fc_losses_val", fc_losses)
val_se3_sum = tf.summary.scalar("se3_losses_val", se3_losses)
val_z_sum = tf.summary.scalar("z_loss_val", z_loss)
val_merged_summary_op = tf.summary.merge(
[val_loss_sum, val_fc_sum, val_se3_sum, val_z_sum])
# ================ LOADING DATASET ===================
tools.printf("Loading training data...")
train_sequences = ["00", "01", "02", "08", "09"]
train_data_gen = data.StatefulRollerDataGen(cfg,
config.dataset_path,
train_sequences,
frames=None)
tools.printf("Loading validation data...")
validation_sequences = ["07"]
val_data_gen = data.StatefulRollerDataGen(cfg,
config.dataset_path,
validation_sequences,
frames=[
range(500),
])
# ============== For Validation =============
def calc_val_loss(sess, writer, i_epoch, alpha_set, run_options, run_metadata):
curr_lstm_states = np.zeros(
[2, cfg.lstm_layers, cfg.batch_size, cfg.lstm_size])
tf.constant(np.zeros([2, cfg_si.lstm_layers, cfg_si.batch_size, cfg_si.lstm_size]),
dtype=tf.float32),
initial_poses_si,
imu_data_si,
np.zeros([cfg_si.batch_size, 17], dtype=np.float32),
0.01 * np.repeat(np.expand_dims(np.identity(17, dtype=np.float32), axis=0),
repeats=cfg_si.batch_size, axis=0),
tf.constant(0.1, dtype=tf.float32),
tf.constant(False, dtype=tf.bool), # is training
False,
tf.constant(True, dtype=tf.bool), # use initializer
cfg_si.use_ekf)
for kitti_seq in kitti_seqs:
tools.printf("Loading eval data...")
data_gen = data.StatefulRollerDataGen(cfg, config.dataset_path, [kitti_seq])
if cfg_si.use_init:
tools.printf("Loading eval data for initial LSTM states...")
data_gen_si = data.StatefulRollerDataGen(cfg_si, config.dataset_path, [kitti_seq],
frames=[range(0, cfg_si.timesteps + 1)])
# results_dir_path = os.path.join(config.save_path, dir_name)
results_dir_path = os.path.join(os.path.dirname(restore_model_file), dir_name)
if not os.path.exists(results_dir_path):
os.makedirs(results_dir_path)
# ==== Read Model Checkpoints =====
variable_to_load = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, "^(?!optimizer).*")
tf_restore_saver = tf.train.Saver(variable_to_load)
with tf.Session() as sess:
rel_disp = tf.concat(
[ekf_out_states[1:, :, 0:3], ekf_out_states[1:, :, 11:14]], axis=-1)
rel_covar = tf.concat([
tf.concat(
[ekf_out_covar[1:, :, 0:3, 0:3], ekf_out_covar[1:, :, 0:3, 11:14]],
axis=-1),
tf.concat(
[ekf_out_covar[1:, :, 11:14, 0:3], ekf_out_covar[1:, :, 11:14, 11:14]],
axis=-1)
],
axis=-2)
se3_outputs = model.se3_layer(rel_disp, initial_poses)
data_gen = data_roller.StatefulRollerDataGen(cfg,
config.dataset_path, [kitti_seq],
frames=frames)
results_dir_path = os.path.join(config.save_path, "ekf_debug")
if not os.path.exists(results_dir_path):
os.makedirs(results_dir_path)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
total_batches = data_gen.total_batches()
tools.printf("Start evaluation loop...")
prediction = np.zeros([total_batches + 1, 7])
ground_truths = np.zeros([total_batches + 1, 7])
ekf_states = np.zeros([total_batches + 1, 17])
# if kitti_seq in ["11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21"]:
# save_ground_truth = False
# else:
# save_ground_truth = True
save_ground_truth = True
cfg = config.SeqEvalLidarConfig
tools.printf("Building eval model....")
inputs, lstm_initial_state, initial_poses, \
is_training, fc_outputs, se3_outputs, lstm_states = model.build_seq_model(cfg)
for kitti_seq in kitti_seqs:
tools.printf("Loading training data...")
train_data_gen = data.StatefulRollerDataGen(cfg, config.dataset_path,
[kitti_seq])
results_dir_path = os.path.join(config.save_path, dir_name)
if not os.path.exists(results_dir_path):
os.makedirs(results_dir_path)
# ==== Read Model Checkpoints =====
restore_model_file = "/home/cs4li/Dev/end_to_end_visual_odometry/results/train_seq_20180418-16-37-02/best_val/model_best_val_checkpoint-143"
variable_to_load = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
"^(cnn_layer|rnn_layer|fc_layer).*")
tf_restore_saver = tf.train.Saver(variable_to_load)
with tf.Session() as sess:
tools.printf("Restoring model weights from %s..." % restore_model_file)
tf_restore_saver.restore(sess, restore_model_file)