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saving/loading to google drive implemented
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@AGKhalil
AGKhalil committed Aug 17, 2019
1 parent 86acc50 commit 8e19730
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155 changes: 85 additions & 70 deletions manual_cl.py
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Expand Up @@ -18,89 +18,104 @@
from stable_baselines.bench import Monitor
from stable_baselines.results_plotter import load_results, ts2xy
from stable_baselines.common.tf_util import save_state
from stable_baselines.common import pydrive_util
import xml.etree.ElementTree as ET


def alter_env(exp_type, variant):
xml_path = os.path.join(gym_real.__path__[0], "envs/assets/real.xml")
xml_path = os.path.join(gym_real.__path__[0], "envs/assets/real.xml")

tree = ET.parse(xml_path)
root = tree.getroot()
if exp_type == 'LEG_LENGTH':
for geom in root.findall("worldbody/body/body/body/body/geom"):
geom.set("fromto", "0 0 0 0 0 " + str(variant))
tree = ET.parse(xml_path)
root = tree.getroot()
if exp_type == 'LEG_LENGTH':
for geom in root.findall("worldbody/body/body/body/body/geom"):
geom.set("fromto", "0 0 0 0 0 " + str(variant))

for pos in root.findall("worldbody/body/[@name='torso']"):
pos.set("pos", "-10.0 0 " + str(abs(variant) + 0.7))
elif exp_type == 'GOAL_DIS':
for pos in root.findall("worldbody/body/[@name='torso']"):
pos.set("pos", str(variant) + " 0 " + str(abs(-0.1) + 0.7))
for pos in root.findall("worldbody/body/[@name='torso']"):
pos.set("pos", "-10.0 0 " + str(abs(variant) + 0.7))
elif exp_type == 'GOAL_DIS':
for pos in root.findall("worldbody/body/[@name='torso']"):
pos.set("pos", str(variant) + " 0 " + str(abs(-0.1) + 0.7))

tree.write(xml_path)

tree.write(xml_path)

def load_checkpoint(checkpoint, run_path):
checkpoint_log = run_path + '/checkpoint'
checkpoint_keeper = []
with open(checkpoint_log) as csv_file:
csv_reader = csv.reader(csv_file)
for row in csv_reader:
checkpoint_keeper.append(row[0])
return run_path + '/' + checkpoint_keeper[checkpoint] + '.pkl'
checkpoint_keeper = []
with open('tmp/tmp_file') as csv_file:
csv_reader = csv.reader(csv_file)
for row in csv_reader:
checkpoint_keeper.append(row[0])
return run_path + '/' + checkpoint_keeper[checkpoint] + '.pkl'


def log_experiments(exp_num, exp_type, variants, model_names, exp_log, log_dict, drive):
file = shelve.open(exp_log)
file['exp' + exp_num] = [exp_type, variants, model_names]
file.close()
print('exp' + exp_num)
pydrive_util.upload_file(drive, 'tf_save/' + exp_log)
# with open(exp_log, 'a') as csv_file:
# csv_writer = csv.writer(csv_file, delimiter=',')
# if os.stat(exp_log).st_size == 0:
# csv_writer.writerow(['exp', 'type', 'variants', 'models'])
# csv_writer.writerow(['exp' + str(exp_num), exp_type, variants, model_names])

def log_experiments(exp_num, exp_type, variants, model_names, exp_log, log_dict):
file = shelve.open(exp_log)
file['exp' + exp_num] = [exp_type, variants, model_names]
file.close()
print('exp' + exp_num)
# with open(exp_log, 'a') as csv_file:
# csv_writer = csv.writer(csv_file, delimiter=',')
# if os.stat(exp_log).st_size == 0:
# csv_writer.writerow(['exp', 'type', 'variants', 'models'])
# csv_writer.writerow(['exp' + str(exp_num), exp_type, variants, model_names])

def run_experiment(exp_num, exp_type, variants, n_cpu, step_total, exp_log, log_dict):
model_names = []
run_path = ''
for order, variant in enumerate(variants):
alter_env(exp_type, variant)
env = gym.make("Real-v0")
env = Monitor(env, 'tf_save', allow_early_resets=True)
env = SubprocVecEnv([lambda: env for i in range(n_cpu)])
if order == 0:
model = PPO2(MlpPolicy, env, verbose=0, tensorboard_log="./tensorboard_log/")
else:
load_name = load_checkpoint(-1, run_path)
model = PPO2.load(load_name, env=env)
model_names.append(model.model_name)
run_path = model.graph_dir
model.learn(total_timesteps=step_total)
env.close()
del model, env
log_experiments(exp_num, exp_type, variants, model_names, exp_log, log_dict)
def run_experiment(exp_num, exp_type, variants, n_cpu, step_total, exp_log, log_dict, drive, og_dir):
model_names = []
run_path = ''
for order, variant in enumerate(variants):
alter_env(exp_type, variant)
env = gym.make("Real-v0")
env = Monitor(env, 'tf_save', allow_early_resets=True)
env = SubprocVecEnv([lambda: env for i in range(n_cpu)])
if order == 0:
model = PPO2(MlpPolicy, env, verbose=0,
tensorboard_log="./tensorboard_log/", drive=drive, og_dir=og_dir)
else:
pydrive_util.download_file(drive, run_path + '/checkpoint')
load_name = load_checkpoint(-1, run_path)
pydrive_util.download_file(drive, load_name)
model = PPO2.load('tmp/tmp_file', env=env, drive=drive, og_dir=og_dir)
model_names.append(model.model_name)
run_path = model.graph_dir
model.learn(total_timesteps=step_total)
pydrive_util.upload_file(drive, model.checkpoint_log)
env.close()
del model, env
log_experiments(exp_num, exp_type, variants,
model_names, exp_log, log_dict, drive)

if __name__ == "__main__":
n_cpu = 20
n_step = 128
desired_log_pts = 1000
step_total = desired_log_pts * n_cpu * n_step
leg_lengths = [i * -0.1 for i in range(1, 5)]
goal_diss = [i * -2 for i in range(2, 6)]
n_cpu = 8
n_step = 128
desired_log_pts = 10
step_total = desired_log_pts * n_cpu * n_step
leg_lengths = [i * -0.1 for i in range(1, 5)]
goal_diss = [i * -2 for i in range(2, 6)]

exp_log = 'experiment_logs'
log_dict = {}
drive = pydrive_util.drive_auth()
og_dir = os.path.dirname(os.path.realpath(__file__))
os.makedirs(os.path.join(og_dir, 'tmp'), exist_ok=True)

leg_type = 'LEG_LENGTH'
dis_type = 'GOAL_DIS'
exp_types = [leg_type, dis_type]
for i in range(5):
for j, exp_type in enumerate(exp_types):
if exp_type == leg_type:
variant = leg_lengths
elif exp_type == dis_type:
variant = goal_diss
perm_2 = list(permutations(variant, 2))
perm_4 = list(permutations(variant))
perms = perm_2 + perm_4
for k, perm in enumerate(perms):
run_experiment(str(i) + '_' + str(j) + '_' + str(k), exp_type, perm, n_cpu, step_total, exp_log, log_dict)
exp_log = 'experiment_logs'
log_dict = {}
model_names = []

leg_type = 'LEG_LENGTH'
dis_type = 'GOAL_DIS'
exp_types = [leg_type, dis_type]
for i in range(5):
for j, exp_type in enumerate(exp_types):
if exp_type == leg_type:
variant = leg_lengths
elif exp_type == dis_type:
variant = goal_diss
perm_2 = list(permutations(variant, 2))
perm_4 = list(permutations(variant))
perms = perm_2 + perm_4
for k, perm in enumerate(perms):
run_experiment(str(i) + '_' + str(j) + '_' + str(
k), exp_type, perm, n_cpu, step_total, exp_log, log_dict, drive, og_dir)
pydrive_util.clean_up('tf_save')
25 changes: 15 additions & 10 deletions plotter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import matplotlib.pyplot as plt
import numpy as np
import shelve
from stable_baselines.common import pydrive_util

def moving_average(values, window):
"""
Expand All @@ -15,15 +16,11 @@ def moving_average(values, window):
return np.convolve(values, weights, 'valid')

def load_checkpoint(run_path, checkpoint=-1):
checkpoint_log = run_path + '/checkpoint'
checkpoint_keeper = []
with open(checkpoint_log) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=' ')
line_count = 0
with open('tmp/tmp_plt_file') as csv_file:
csv_reader = csv.reader(csv_file)
for row in csv_reader:
if line_count != 0:
checkpoint_keeper.append(row[0])
line_count += 1
checkpoint_keeper.append(row[0])
return run_path + '/' + checkpoint_keeper[checkpoint] + '.csv'

def get_plot_csv(plot_log):
Expand Down Expand Up @@ -60,8 +57,12 @@ def plot_graph(plot_name, save_name, length, reward):
plot_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "plot_saves/")
os.makedirs(plot_dir, exist_ok=True)

exp_logger = 'experiment_logs'
file = shelve.open(exp_logger)
drive = pydrive_util.drive_auth()

exp_logger = 'tf_save/experiment_logs'
pydrive_util.download_file(drive, exp_logger, plot=True, db=True)

file = shelve.open('tmp/tmp_db_file')
keys = list(file.keys())
keys.sort()
for exp in keys:
Expand All @@ -73,9 +74,13 @@ def plot_graph(plot_name, save_name, length, reward):
save_name = plot_dir + file[exp][0] + str(file[exp][1]) + str(file[exp][2])
for model_name in model_names:
run_path = 'tf_save/' + model_name
l, r = get_plot_csv(load_checkpoint(run_path))
pydrive_util.download_file(drive, run_path + '/checkpoint', plot=True)
load_name = load_checkpoint(run_path)
pydrive_util.download_file(drive, load_name, plot=True)
l, r = get_plot_csv('tmp/tmp_plt_file')
length.extend(l + cum_l)
reward.extend(r)
cum_l += l[-1]
plot_graph(plot_name, save_name, length, reward)
file.close()
pydrive_util.clean_up('tmp')

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