def initialize_episode(self,
cars,
gamma,
people,
pos_x,
pos_y,
tensor,
seq_len=30,
rewards=(1, 1, 1, 1, 1, 1),
width=0):
# tensor, people_e, cars_e, pos_x, pos_y, gamma, seq_len, reward_weights
setup = run_settings()
setup.run_2D = True
episode = SimpleEpisode(tensor,
people,
cars,
pos_x,
pos_y,
gamma,
seq_len,
rewards,
agent_size=[width, width, width],
adjust_first_frame=False,
run_2D=True)
setup.shape_s = [0, 0, 0]
setup.agent_shape = [width, width, width]
setup.net_size = [1 + 2 * width, 1 + 2 * width, 1 + 2 * width]
setup.agent_s = [width, width, width]
#tf.reset_default_graph()
net = Seg_2d_softmax(setup)
agent = SimplifiedAgent(setup)
agent = NetAgent(setup, net, None)
return agent, episode, net
def initialize_episode_cont(self,
cars,
gamma,
people,
pos_x,
pos_y,
tensor,
seq_len=30,
rewards=np.zeros(NBR_REWARD_WEIGHTS),
agent_size=(0, 0, 0),
people_dict={},
init_frames={}):
setup = run_settings()
setup.action_freq = 1
setup.useHeroCar = False
setup.useRLToyCar = False
# tensor, people_e, cars_e, pos_x, pos_y, gamma, seq_len, reward_weights
agent, episode = self.initialize_episode(cars, gamma, people, pos_x,
pos_y, tensor, seq_len,
rewards, agent_size,
people_dict, init_frames)
episode.vel_init = np.zeros(3)
agent = ContinousAgent(setup)
return agent, episode
def initialize_episode(self, cars, gamma, people, pos_x, pos_y, tensor,seq_len=30, rewards=[], agent_size=(0,0,0), people_dict={}, init_frames={}):
settings = run_settings()
settings.useHeroCar = False
settings.useRLToyCar=False
settings.multiplicative_reward=False
if len(rewards)==0:
rewards = self.get_reward()
episode = SimpleEpisode(tensor, people, cars, pos_x, pos_y, gamma, seq_len, rewards, agent_size=agent_size,
people_dict=people_dict, init_frames=init_frames, defaultSettings=settings)
agent=SimplifiedAgent(settings)
return agent, episode
def get_episode(self, cars, gamma, people, tensor, seq_len=30):
pos_x = 0
pos_y = 0
rewards = self.get_reward()
episode = SimpleEpisode(tensor,
people,
cars,
pos_x,
pos_y,
gamma,
seq_len,
rewards,
agent_size=(0, 0, 0),
defaultSettings=run_settings())
return episode
def initialize_episode(self,
cars,
gamma,
people,
pos_x,
pos_y,
tensor,
seq_len=30,
rewards=np.zeros(NBR_REWARD_WEIGHTS),
agent_size=(0, 0, 0),
people_dict={},
init_frames={}):
# tensor, people_e, cars_e, pos_x, pos_y, gamma, seq_len, reward_weights
setup = run_settings()
setup.action_freq = 1
setup.useHeroCar = False
setup.useRLToyCar = False
frameRate, frameTime = setup.getFrameRateAndTime()
episode = SimpleEpisode(tensor,
people,
cars,
pos_x,
pos_y,
gamma,
seq_len,
rewards,
agent_size=agent_size,
people_dict=people_dict,
init_frames=init_frames,
adjust_first_frame=False,
seq_len_pfnn=seq_len * 60 // frameRate,
defaultSettings=setup)
episode.action = np.zeros(len(episode.action))
episode.vel_init = np.zeros(3)
agent = AgentPFNN(setup, None) #, None, None, None)
return agent, episode
import tensorflow as tf
from RL.settings import RANDOM_SEED
tf.set_random_seed(RANDOM_SEED)
from net_2d import SimpleSoftMaxNet_2D
from RL.settings import run_settings
# a = tf.random_uniform([1])
# b = tf.random_normal([1])
weights = tf.get_variable('weights_last', [9, 2],
initializer=tf.contrib.layers.xavier_initializer())
# Repeatedly running this block with the same graph will generate the same
# sequences of 'a' and 'b'.
net = SimpleSoftMaxNet_2D(run_settings())
init = tf.global_variables_initializer()
print("Session 1")
with tf.Session(graph=tf.get_default_graph()) as sess1:
# print(sess1.run(a)) # generates 'A1'
# print(sess1.run(a)) # generates 'A2'
# print(sess1.run(b)) # generates 'B1'
# print(sess1.run(b)) # generates 'B2'
sess1.run(init)
[weights_l] = sess1.run([net.tvars])
#[weights_l] = sess1.run([weights])
print weights_l
print("Session 2")
with tf.Session(graph=tf.get_default_graph()) as sess2:
# print(sess2.run(a)) # generates 'A1'
# print(sess2.run(a)) # generates 'A2'
# print(sess2.run(b)) # generates 'B1'
# if training:
# train_itr[itr]=train_counter
# if itr in special_cases:
# test_points[itr] = train_counter
# if itr not in iterations_cars:
# train_counter=train_counter+1
# else:
# test_points[itr]= train_counter
# if itr in special_cases:
# train_itr[itr] = train_counter
# print "----------------------------------------------------------------------------"
#print train_itr
# print "----------------------------------------------------------------------------"
#print sorted(test_points)
settings = run_settings()
test_points = {0: 0}
scenes = [
"15646511153936256674_1620_000_1640_000",
"18311996733670569136_5880_000_5900_000"
]
filespath = settings.waymo_path
epoch = 0
filename_list = []
# Get files to run on.
ending_local = "test_*"
for scene in scenes:
filename_list.append(os.path.join(filespath, scene))
saved_files_counter = 0
print(filename_list)