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dagger_train.py
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dagger_train.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import tensorflow as tf
import threading
import numpy as np
import signal
import random
import os
import time
from dagger_policy_generators import SmashNet
from dagger_training_thread import SmashNetTrainingThread
from scene_loader import THORDiscreteEnvironment as Environment
from utils.ops import log_uniform
from utils.rmsprop_applier import RMSPropApplier
from dagger_constants import ACTION_SIZE, PARALLEL_SIZE, INITIAL_ALPHA_LOW, INITIAL_ALPHA_HIGH, INITIAL_ALPHA_LOG_RATE, INITIAL_DIFFIDENCE_RATE, MAX_TIME_STEP, CHECKPOINT_DIR, LOG_FILE, RMSP_EPSILON, RMSP_ALPHA, GRAD_NORM_CLIP, USE_GPU, NUM_CPU, TASK_TYPE, TRAIN_TASK_LIST, VALID_TASK_LIST, DYNAMIC_VALIDATE, ENCOURAGE_SYMMETRY
if __name__ == '__main__':
device = "/gpu:0" if USE_GPU else "/cpu:0"
network_scope = TASK_TYPE
list_of_tasks = TRAIN_TASK_LIST
list_of_val_tasks = VALID_TASK_LIST
scene_scopes = list_of_tasks.keys()
global_t = 0
stop_requested = False
if not os.path.exists(CHECKPOINT_DIR): os.mkdir(CHECKPOINT_DIR)
initial_learning_rate = log_uniform(INITIAL_ALPHA_LOW,
INITIAL_ALPHA_HIGH,
INITIAL_ALPHA_LOG_RATE)
initial_diffidence_rate_seed = INITIAL_DIFFIDENCE_RATE # TODO: hyperparam
global_network = SmashNet(action_size = ACTION_SIZE,
device = device,
network_scope = network_scope,
scene_scopes = scene_scopes)
branches = []
branch_val = []
for scene in scene_scopes:
for task in list_of_tasks[scene]:
branches.append((scene, task)) # single scene, task pair for now
branch_val.append(False)
if DYNAMIC_VALIDATE:
for task in list_of_val_tasks[scene]:
branches.append((scene, task))
branch_val.append(True)
print("Total navigation tasks: %d" % len(branches))
NUM_TASKS = len(branches)
assert PARALLEL_SIZE >= NUM_TASKS, "Not enough threads for multitasking: at least {} threads needed.".format(NUM_TASKS)
learning_rate_input = tf.placeholder("float")
grad_applier = RMSPropApplier(learning_rate = learning_rate_input,
decay = RMSP_ALPHA,
momentum = 0.0,
epsilon = RMSP_EPSILON,
clip_norm = GRAD_NORM_CLIP,
device = device)
# instantiate each training thread
# each thread is training for one target in one scene
training_threads = [] # 1 training thread for the single target
for i in range(PARALLEL_SIZE):
scene, task = branches[i%NUM_TASKS]
if USE_GPU:
device = "/gpu:0"
else:
device = "/cpu:{:d}".format(i%NUM_CPU)
mode = "val" if branch_val[i % NUM_TASKS] else "train"
training_thread = SmashNetTrainingThread(i,
global_network,
initial_learning_rate,
learning_rate_input,
grad_applier,
MAX_TIME_STEP,
device,
initial_diffidence_rate_seed,
mode=mode,
network_scope = "thread-%d"%(i+1),
scene_scope = scene,
task_scope = task,
encourage_symmetry= ENCOURAGE_SYMMETRY)
training_threads.append(training_thread)
print("Total train threads: %d" % len(training_threads))
# prepare session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True))
init = tf.global_variables_initializer()
sess.run(init)
# create tensorboard summaries
summary_op = dict()
summary_placeholders = dict()
for i in range(PARALLEL_SIZE):
scene, task = branches[i%NUM_TASKS]
key = scene + "-" + task
if branch_val[i % NUM_TASKS]:
key = scene + "-val-" + task
# summary for tensorboard
episode_length_input = tf.placeholder("float")
episode_pi_sim_input = tf.placeholder("float")
episode_loss_input = tf.placeholder("float")
scalar_summaries = [
tf.summary.scalar(key+"/Episode Length", episode_length_input),
tf.summary.scalar(key+"/Episode Pi Similarity", episode_pi_sim_input),
tf.summary.scalar(key+"/Episode Loss", episode_loss_input),
]
summary_op[key] = tf.summary.merge(scalar_summaries)
summary_placeholders[key] = {
"episode_length_input": episode_length_input,
"episode_pi_sim_input": episode_pi_sim_input,
"episode_loss_input": episode_loss_input,
}
summary_writer = tf.summary.FileWriter(LOG_FILE + '/' + time.strftime("%Y-%m-%d-%H%M%S"), sess.graph)
# init or load checkpoint with saver
# if you don't need to be able to resume training, use the next line instead.
# it will result in a much smaller checkpoint file.
# saver = tf.train.Saver(max_to_keep=10, var_list=global_network.get_vars())
saver = tf.train.Saver(max_to_keep=10)
checkpoint = tf.train.get_checkpoint_state(CHECKPOINT_DIR)
if checkpoint and checkpoint.model_checkpoint_path:
saver.restore(sess, checkpoint.model_checkpoint_path)
print("checkpoint loaded: {}".format(checkpoint.model_checkpoint_path))
tokens = checkpoint.model_checkpoint_path.split("-")
# set global step
global_t = int(tokens[1])
print(">>> global step set: {}".format(global_t))
else:
print("Could not find old checkpoint")
def train_function(parallel_index):
global global_t
training_thread = training_threads[parallel_index]
last_global_t = 0
scene, task = branches[parallel_index % NUM_TASKS]
key = scene + "-" + task
if branch_val[parallel_index % NUM_TASKS]:
key = scene + "-val-" + task
while global_t < MAX_TIME_STEP and not stop_requested:
diff_global_t = training_thread.process(sess, global_t, summary_writer,
summary_op[key], summary_placeholders[key])
global_t += diff_global_t
# periodically save checkpoints to disk
if parallel_index == 0 and global_t - last_global_t > 1000000:
print('Save checkpoint at timestamp %d' % global_t)
saver.save(sess, CHECKPOINT_DIR + '/' + 'checkpoint', global_step = global_t)
last_global_t = global_t
def signal_handler(signal, frame):
global stop_requested
print('You pressed Ctrl+C!')
stop_requested = True
train_threads = [threading.Thread(target=train_function, args=(i,)) for i in range(PARALLEL_SIZE)]
signal.signal(signal.SIGINT, signal_handler)
# start each training thread
for t in train_threads: t.start()
print('Press Ctrl+C to stop.')
signal.pause()
# wait for all threads to finish
for t in train_threads: t.join()
print('Now saving data. Please wait.')
saver.save(sess, CHECKPOINT_DIR + '/' + 'checkpoint', global_step = global_t)
summary_writer.close()