Esempio n. 1
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 def __log_files_and_configs(self):
     self.results_dir_path = tools.create_results_dir("train_seq")
     self.curr_dir_path = os.path.dirname(os.path.realpath(__file__))
     tools.log_file_content(self.results_dir_path, [os.path.realpath(__file__),
                                                    os.path.join(self.curr_dir_path, "data_roller.py"),
                                                    os.path.join(self.curr_dir_path, "model.py"),
                                                    os.path.join(self.curr_dir_path, "losses.py"),
                                                    os.path.join(self.curr_dir_path, "train.py"),
                                                    os.path.join(self.curr_dir_path, "train_seq.py"),
                                                    os.path.join(self.curr_dir_path, "config.py")])
     tools.set_log_file(os.path.join(self.results_dir_path, "print_logs.txt"))
     config.print_configs(self.cfg)
Esempio n. 2
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    def __log_files_and_configs(self):
        self.results_dir_path = tools.create_results_dir("train_seq")
        self.curr_dir_path = os.path.dirname(os.path.realpath(__file__))

        files_to_log = []
        for filename in glob.iglob(os.path.join(self.curr_dir_path, "**"),
                                   recursive=True):
            if "/results/" not in filename and filename.endswith(".py"):
                files_to_log.append(filename)
        tools.log_file_content(self.results_dir_path, files_to_log)
        tools.set_log_file(
            os.path.join(self.results_dir_path, "print_logs.txt"))
        config.print_configs(self.cfg)
Esempio n. 3
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        )

        _fc_losses = sess.run(fc_losses,
                              feed_dict={
                                  inputs: batch_data,
                                  fc_labels: fc_ground_truth,
                                  is_training: False
                              })

        fc_losses_history.append(_fc_losses)

    return fc_losses_history, sum(fc_losses_history) / len(fc_losses_history)


# =================== SAVING/LOADING DATA ========================
results_dir_path = tools.create_results_dir("train_pair")
tf_saver = tf.train.Saver()
restore_model_file = None

# =================== TRAINING ========================
with tf.Session() as sess:
    if restore_model_file:
        tools.printf("Restoring model weights from %s..." % restore_model_file)
        tf_saver.restore(sess, restore_model_file)
    else:
        tools.printf("Initializing variables...")
        sess.run(tf.global_variables_initializer())

    # Visualization
    writer = tf.summary.FileWriter('graph_viz/')
    writer.add_graph(tf.get_default_graph())
        lr = tf.placeholder(tf.float32, name="se3_lr", shape=[])
        trainer = tf.train.AdamOptimizer(learning_rate=lr).minimize(
            total_losses, colocate_gradients_with_ops=True)

# with tf.device(tf.train.replica_device_setter(ps_tasks=1, ps_device='/job:localhost/replica:0/task:0/device:CPU:0', worker_device='/job:localhost/replica:0/task:0/device:GPU:1')):
#     val_inputs, val_lstm_init, val_initial_poses, val_is_training, val_fc_outputs, val_se3_outputs, val_lstm_states = simple_model.build_seq_model(
#         val_cfg, True)
#     val_se3_labels, val_fc_labels = simple_model.model_labels(val_cfg)
#
#     with tf.variable_scope("Val_Losses"):
#         se3_losses_val, _, _ = losses.se3_losses(val_se3_outputs, val_se3_labels, val_cfg.k_se3)
#         fc_losses_val, _, _, _, _, _ = losses.pair_train_fc_losses(val_fc_outputs, val_fc_labels, val_cfg.k_fc)
#         total_losses_val = (1 - alpha) * se3_losses_val + alpha * fc_losses_val

# =================== SAVING/LOADING DATA ========================
results_dir_path = tools.create_results_dir("train_seq")
tools.log_file_content(results_dir_path, os.path.realpath(__file__))

tf_checkpoint_saver = tf.train.Saver(max_to_keep=3)
tf_best_saver = tf.train.Saver(max_to_keep=2)

tf_restore_saver = tf.train.Saver()
# restore_model_file = None
restore_model_file = "/home/ben/School/e2e_results/train_seq_20180419-00-46-05_timesteps20_no_reverse/best_val/model_best_val_checkpoint-49"

# just for restoring pre trained cnn weights
cnn_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
                                  "^cnn_layer.*")
cnn_init_tf_saver = tf.train.Saver(cnn_variables)
cnn_init_model_file = None
# cnn_init_model_file = "/home/cs4li/Dev/end_to_end_visual_odometry/results/train_seq_20180414-01-33-38_simplemodel1lstmseq0f2f/model_epoch_checkpoint-199"