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OmniGibson
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Plot functions

This commit is contained in:
hzyjerry 2017-11-13 22:59:43 -08:00
parent 399783108d
commit 4ad089cfde
3 changed files with 226 additions and 77 deletions
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3
.gitignore vendored
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@ -46,4 +46,5 @@ checkpoint
examples/train/models/
log_*/
log_*/
monitor_*

76
examples/scripts/plot_reward_local.py
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@ -14,29 +14,31 @@ all_log_dirs = [os.path.join(root_dir, d) for d in all_log_dirs if "openai" in d
filename = "0.monitor.csv"
local_logs = [os.path.join(all_log_dirs[-1], filename)]
local_logs = [os.path.join(all_log_dirs[-1], filename), os.path.join(all_log_dirs[-2], filename)]
log_dir = [all_log_dirs[-1]]
print(log_dir)
for i in range(len(log_dir)):
local_logs.append(str(i) + filename)
def smooth_median(rewards, factor=10):
def smooth_median(rewards):
rew_median = []
i = 0
while i < len(rewards):
cur_list = rewards[i: min(i + factor, len(rewards))]
while len(cur_list) < factor:
cur_list.append(rewards[-1])
while i < len(rewards) - MEDIAN_FC:
cur_list = rewards[i: min(i + MEDIAN_FC, len(rewards))]
while len(cur_list) < MEDIAN_FC:
cur_list.append(rewards[-1])
i = i + 1
rew_median.append(np.median(cur_list))
return rew_median
def smooth_max(rewards, factor=10):
def smooth_max(rewards):
rew_median = []
i = 0
while i < len(rewards):
cur_list = rewards[i: min(i + factor, len(rewards))]
while len(cur_list) < factor:
while i < len(rewards) - MEDIAN_FC:
cur_list = rewards[i: min(i + MEDIAN_FC, len(rewards))]
while len(cur_list) < MEDIAN_FC:
cur_list.append(rewards[-1])
i = i + 1
rew_median.append(np.max(cur_list))
@ -62,9 +64,59 @@ def plot(index, smooth_fc=smooth_median):
all_times = num_rows[row_index, 1].tolist()
t_range = np.arange(0, len(all_rewards), 1)
print("Total number of steps:", sum(all_times))
print('Average time length', np.mean(np.array(all_times)))
plt.plot(t_range, all_rewards, '.', t_range, smooth_fc(all_rewards, args.smooth), '-')
def plot2(index1, index2, smooth_fc=smooth_median):
with open(local_logs[index1], 'r') as csvfile:
csv_rows = csv.reader(csvfile, delimiter=' ', quotechar='|')
str_rows = []
num_rows = []
for row in csv_rows:
str_rows.append(row[0])
for row in str_rows:
try:
num_rows.append([float(num) for num in row.split(',')])
except:
continue
num_rows = np.array(num_rows)
row_index = num_rows[:, 0] > -200 ## Avoid plotting too low points
num_rows[row_index, 0] = num_rows[row_index, 0] + 1.2 ## change of initial coordinate
all_rewards_1 = num_rows[row_index, 0].tolist()
all_times_1 = num_rows[row_index, 1].tolist()
#print("Total number of steps:", sum(all_times))
#print('Average time length', np.mean(np.array(all_times)))
sm_rewards_1 = smooth_fc(all_rewards_1)
t_range_1 = np.arange(0, len(sm_rewards_1), 1)
plt.plot(t_range_1, sm_rewards_1, '-')
with open(local_logs[index2], 'r') as csvfile:
csv_rows = csv.reader(csvfile, delimiter=' ', quotechar='|')
str_rows = []
num_rows = []
for row in csv_rows:
str_rows.append(row[0])
for row in str_rows:
try:
num_rows.append([float(num) for num in row.split(',')])
except:
continue
num_rows = np.array(num_rows)
row_index = num_rows[:, 0] > -200 ## Avoid plotting too low points
all_rewards_2 = num_rows[row_index, 0].tolist()
all_times_2 = num_rows[row_index, 1].tolist()
sm_rewards_2 = smooth_fc(all_rewards_2)
t_range_2 = np.arange(0, len(sm_rewards_2), 1)
plt.plot(t_range_2, sm_rewards_2, '-')
def smooth(rewards, factor=10):
smoothed_rew = []
for i in range(0, len(rewards) - factor + 1):
@ -72,8 +124,9 @@ def smooth(rewards, factor=10):
return smoothed_rew
def main():
plot(0, smooth_median)
#plot(0, smooth_median)
#plot(0, smooth_max)
plot2(0, 1, smooth_median)
plt.show()
if __name__ == '__main__':
@ -81,4 +134,5 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--smooth', type=int, default=50)
args = parser.parse_args()
MEDIAN_FC = args.smooth
main()

224
examples/scripts/plot_reward_openai.py
View File

@ -2,124 +2,133 @@ import os
import matplotlib.pyplot as plt
import csv
import numpy as np
import subprocess
import sys
## sudo /usr/bin/X :0
#sudo service lightdm stop
#sudo pkill XOrg
#sudo /usr/bin/X :0 &
## DISPLAY=:0 python examples/train/train_ant_climb_ppo1.py --mode RGBD
## DISPLAY=:0 python examples/train/train_ant_climb_ppo1.py --mode DEPTH
## DISPLAY=:0 python examples/train/train_ant_climb_ppo1.py --mode SENSOR
## scp -i /home/jerry/Dropbox/CVGL/universe.pem ubuntu@ec2-34-215-160-202.us-west-2.compute.amazonaws.com:/tmp/openai-2017-11-12-13-16-28-113773/0.monitor.csv
cmd = "scp -i /home/jerry/Dropbox/CVGL/universe.pem ubuntu@{}:/tmp/{}/{} {}"
## ssh -i /home/jerry/Dropbox/CVGL/universe.pem ubuntu@ec2-34-215-160-202.us-west-2.compute.amazonaws.com ls -l /tmp/
aws_addr = []
log_dir = []
aws_names = []
'''
Universe ant climb 1
ssh -i universe.pem ubuntu@ec2-34-215-160-202.us-west-2.compute.amazonaws.com
'''
aws_addr.append("ec2-34-215-160-202.us-west-2.compute.amazonaws.com")
aws_names.append("RGB & Sensor, small init random, small lr")
'''
Universe ant climb 2
ssh -i universe.pem ubuntu@ec2-52-24-76-187.us-west-2.compute.amazonaws.com
'''
aws_addr.append("ec2-52-24-76-187.us-west-2.compute.amazonaws.com")
aws_names.append("RGBD & Sensor, small init random, large lr")
'''
Universe ant climb 3
ec2-52-42-249-133.us-west-2.compute.amazonaws.com
'''
aws_addr.append("ec2-52-42-249-133.us-west-2.compute.amazonaws.com")
aws_names.append("Depth & Sensor, small init random, small lr")
'''
Universe ant climb 4
ec2-52-38-25-41.us-west-2.compute.amazonaws.com
'''
aws_addr.append("ec2-52-38-25-41.us-west-2.compute.amazonaws.com")
aws_names.append("Depth & Sensor, small init random, large lr")
'''
Universe ant climb 5
ec2-34-214-189-116.us-west-2.compute.amazonaws.com
'''
aws_addr.append("ec2-34-214-189-116.us-west-2.compute.amazonaws.com")
aws_names.append("RGBD & Sensor, large init random, small lr")
'''
Universe ant climb 6
ec2-34-216-35-8.us-west-2.compute.amazonaws.com
'''
aws_addr.append("ec2-34-216-35-8.us-west-2.compute.amazonaws.com")
aws_names.append("RGBD & Sensor, large init random, large lr")
'''
Universe ant climb 7
ec2-34-213-149-40.us-west-2.compute.amazonaws.com
'''
aws_addr.append("ec2-34-213-149-40.us-west-2.compute.amazonaws.com")
aws_names.append("Depth & Sensor, large init random, small lr")
'''
Universe ant climb 8
ec2-52-89-201-217.us-west-2.compute.amazonaws.com
'''
aws_addr.append("ec2-52-89-201-217.us-west-2.compute.amazonaws.com")
aws_names.append("Depth & Sensor, large init random, large lr")
'''
Universe ant climb 9
ec2-52-38-59-13.us-west-2.compute.amazonaws.com
'''
aws_addr.append("ec2-52-38-59-13.us-west-2.compute.amazonaws.com")
aws_names.append("Sensor only, small lr")
'''
Universe ant climb 10
ec2-34-212-248-24.us-west-2.compute.amazonaws.com
'''
aws_addr.append("ec2-34-212-248-24.us-west-2.compute.amazonaws.com")
aws_names.append("Sensor only, large lr")
aws_dirs = []
file_names = []
remote_filename = "0.monitor.csv"
name_template = "monitor_{}_{}.csv"
aws_addr.append("ec2-34-215-160-202.us-west-2.compute.amazonaws.com")
log_dir.append("openai-2017-11-12-13-43-46-948548")
'''
Universe husky navigate 2
RGB + NO SENSOR + COLLISION
timesteps_per_actorbatch=1024,
clip_param=0.2, entcoeff=0.001,
optim_epochs=10, optim_stepsize=3e-4, optim_batchsize=64,
gamma=0.995, lam=0.95,
'''
aws_addr.append("ec2-52-24-76-187.us-west-2.compute.amazonaws.com")
log_dir.append("openai-2017-11-12-14-31-31-175329")
'''
Universe husky navigate 3
RGB + NO SENSOR + COLLISION
timesteps_per_actorbatch=1024,
clip_param=0.2, entcoeff=0.0001,
optim_epochs=10, optim_stepsize=3e-6, optim_batchsize=64,
gamma=0.995, lam=0.95,
'''
aws_addr.append("ec2-52-42-249-133.us-west-2.compute.amazonaws.com")
log_dir.append("openai-2017-11-12-14-27-49-793285")
'''
Universe husky navigate 4
RGB + SENSOR + NO COLLISION
'''
aws_addr.append("ec2-52-38-25-41.us-west-2.compute.amazonaws.com")
log_dir.append("openai-2017-11-12-14-03-35-785424")
filename = "0.monitor.csv"
local_logs = []
for i in range(len(log_dir)):
local_logs.append(str(i) + filename)
scp_cmd = "scp -i /home/jerry/Dropbox/CVGL/universe.pem ubuntu@{}:/tmp/{}/{} {}"
def download(index):
full_cmd = cmd.format(aws_addr[index], log_dir[index], filename, local_logs[index])
full_cmd = scp_cmd.format(aws_addr[index], aws_dirs[index], remote_filename, file_names[index])
print(full_cmd)
os.system(full_cmd)
def plot(index, axis, smooth_fc=6):
with open(local_logs[index], 'r') as csvfile:
def get_latest_openai_dir(host):
## ssh -i /home/jerry/Dropbox/CVGL/universe.pem ubuntu@ec2-34-215-160-202.us-west-2.compute.amazonaws.com ls -l /tmp/
# ls_cmd = "ssh -i /home/jerry/Dropbox/CVGL/universe.pem ubuntu@{} ls -l /tmp/"
COMMAND="ls -l /tmp/"
ssh = subprocess.Popen(["ssh", "-i", "/home/jerry/Dropbox/CVGL/universe.pem", "ubuntu@%s" % host, COMMAND],
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
results = ssh.stdout.readlines()
results = [str(r,'utf-8') for r in results]
results = [r.split()[-1].strip() for r in results if "openai" in r]
results = sorted(results)
return results[-1]
def plot(index, axis):
with open(file_names[index], 'r') as csvfile:
csv_rows = csv.reader(csvfile, delimiter=' ', quotechar='|')
str_rows = []
num_rows = []
@ -133,39 +142,124 @@ def plot(index, axis, smooth_fc=6):
num_rows = np.array(num_rows)
all_rewards = num_rows[:, 0].tolist()
all_times = num_rows[:, 2].tolist()
all_steps = num_rows[:, 1].tolist()
t_range = np.arange(0, len(all_rewards), 1)
if smooth_fc:
axis[index].plot(t_range, all_rewards, '.', t_range, smooth_median(all_rewards, smooth_fc), '-')
step_length = sum(all_steps)
total_time = num_rows[-1, 2]
time_length = sum(all_times)
if SMOOTH:
axis[index].plot(t_range, all_rewards, '.', t_range, smooth_median(all_rewards, SMOOTH_FC), '-')
axis[index].text(.5, .9, aws_names[index] + " ts: {}".format(step_length), horizontalalignment='center',
transform=axis[index].transAxes)
axis[index].text(.5, .1, "Time ts: {}".format(time_length), horizontalalignment='center',
transform=axis[index].transAxes)
else:
axis[index].plot(t_range, all_rewards, '.')
axis[index].text(.5, .9, aws_names[index] + " ts: {}".format(step_length), horizontalalignment='center',
transform=axis[index].transAxes)
axis[index].text(.5, .1, "Time ts: {}".format(time_length), horizontalalignment='center',
transform=axis[index].transAxes)
def smooth_median(rewards, factor=10):
def get_smooth(index):
with open(file_names[index], 'r') as csvfile:
csv_rows = csv.reader(csvfile, delimiter=' ', quotechar='|')
str_rows = []
num_rows = []
for row in csv_rows:
str_rows.append(row[0])
for row in str_rows:
try:
num_rows.append([float(num) for num in row.split(',')])
except:
continue
num_rows = np.array(num_rows)
all_rewards = num_rows[:, 0].tolist()
total_time = num_rows[-1, 2]
all_steps = num_rows[:, 1].tolist()
print("Total time", total_time, "averge step", np.mean(all_steps))
return smooth_median(all_rewards)
def smooth_median(rewards):
rew_median = []
i = 0
while i < len(rewards):
cur_list = rewards[i: min(i + factor, len(rewards))]
while len(cur_list) < factor:
while i < len(rewards) - SMOOTH_FC:
cur_list = rewards[i: min(i + SMOOTH_FC, len(rewards))]
while len(cur_list) < SMOOTH_FC:
cur_list.append(rewards[-1])
i = i + 1
rew_median.append(np.median(cur_list))
return rew_median
def smooth(rewards, factor=30):
def smooth_mean(rewards):
rew_mean = []
i = 0
while i < len(rewards) - SMOOTH_FC:
cur_list = rewards[i: min(i + SMOOTH_FC, len(rewards))]
while len(cur_list) < SMOOTH_FC:
cur_list.append(rewards[-1])
i = i + 1
rew_mean.append(np.mean(cur_list))
return rew_mean
def smooth(rewards):
smoothed_rew = []
for i in range(0, len(rewards) - factor + 1):
smoothed_rew.append(sum(rewards[i:i + factor]) / factor)
for i in range(0, len(rewards) - SMOOTH_FC + 1):
smoothed_rew.append(sum(rewards[i:i + SMOOTH_FC]) / SMOOTH_FC)
return smoothed_rew
def main():
for i in range(len(log_dir)):
download(i)
f, axarr = plt.subplots(len(log_dir), sharex=True)
for i in range(len(log_dir)):
f, axarr = plt.subplots(len(file_names), sharex=True)
for i in range(len(file_names)):
plot(i, axarr, 10)
plt.show()
def main2():
REW_MEDIAN_FACTOR = 10
MED_MEDIAN_FACTOR = 10
LENGTH_CAP = 700
smoothed = []
lengths = []
smoothed_names = []
for i in range(len(file_names)):
smooth_i = get_smooth(i)
#smooth_i = smooth_mean(smooth_i)
if len(smooth_i) < LENGTH_CAP:
smoothed.append(smooth_i)
lengths.append(len(smooth_i))
smoothed_names.append(aws_names[i])
max_time_length = max([len(series) for series in smoothed])
legends = []
for i in range(len(smoothed)):
patch, = plt.plot(np.arange(0, lengths[i], 1), smoothed[i], label=smoothed_names[i])
legends.append(patch)
plt.legend(handles=legends)
plt.show()
if __name__ == '__main__':
main()
LOCAL = True
SMOOTH = True
SMOOTH_FC = 10
if LOCAL:
for i in range(len(aws_addr)):
host = aws_addr[i]
remote_dir = get_latest_openai_dir(host)
file_names.append(name_template.format(host, remote_dir))
else:
for i in range(len(aws_addr)):
host = aws_addr[i]
remote_dir = get_latest_openai_dir(host)
print(host, remote_dir)
aws_dirs.append(remote_dir)
file_names.append(name_template.format(host, remote_dir))
download(i)
file_names = file_names[:]
main2()