def main(_):
config = flags.FLAGS
if config.mode == "train":
while True:
try:
train(config)
except Exception:
print("exception...")
print('restart....')
pass
else:
break
elif config.mode == "prepro":
prepro(config)
elif config.mode == "debug":
config.num_steps = 2
config.val_num_batches = 1
config.checkpoint = 1
config.period = 1
train(config)
elif config.mode == "test":
test(config)
elif config.mode == "demo":
demo(config)
else:
print("Unknown mode")
exit(0)
def main(_):
config = flags.FLAGS
if config.mode == "train":
# train(config)
cv_train(config)
elif config.mode == "prepro":
prepro(config)
elif config.mode == "test":
cv_test(config)
elif config.mode == "demo":
demo(config)
def do_POST(self):
if self.path == "/send":
form = cgi.FieldStorage(
fp=self.rfile,
headers=self.headers,
environ={'REQUEST_METHOD': 'POST',
'CONTENT_TYPE': self.headers['Content-Type'],
})
image_link = form["image_link"].value
result1, result2 = demo(image_link)
print "Image Link: %s" % image_link
self.send_response(200)
self.end_headers()
self.wfile.write('<html><header><title>Image Captioning</title></header>')
self.wfile.write('<body><h1>Welcome to Generating Natural Description for images!</h1>')
self.wfile.write('<h2>Final Project CS519 Deep Learning - Author: Khoi Nguyen</h2>')
self.wfile.write('<p>You can search on Google Image or somewhere else for image link and paste link here</p>')
self.wfile.write('<form method="POST" action="/send">')
self.wfile.write('<label>Insert Image Link: </label>')
self.wfile.write('<input type="text" name="image_link" value="'+image_link+'"/>')
self.wfile.write('<input type="submit" value="Submit"/></form>')
self.wfile.write('<h4>Result from model 1: '+result1+'</h4>')
self.wfile.write('<h4>Result from model 2: '+result2+'</h4></form>')
self.wfile.write('<img src="'+image_link+'" height="350">')
self.wfile.write('</body></html>')
return
def main(_):
config = flags.FLAGS
if config.mode == "train":
train(config)
elif config.mode == "prepro":
prepro(config)
elif config.mode == "debug":
config.num_steps = 2
config.val_num_batches = 1
config.checkpoint = 1
config.period = 1
train(config)
elif config.mode == "test":
test(config)
elif config.mode == "demo":
demo(config)
else:
print("Unknown mode")
exit(0)
def main(_):
config = flags.FLAGS
if config.mode == "train":
train(config)
elif config.mode == "prepro":
prepro(config)
elif config.mode == "debug":
config.num_steps = 2
config.val_num_batches = 1
config.checkpoint = 1
config.period = 1
train(config)
elif config.mode == "test":
test(config)
elif config.mode == "demo":
demo(config)
else:
print("Unknown mode")
exit(0)
def main(_):
jieba.re_han_default = re.compile("([\u4E00-\u9FD5a-zA-Z0-9+#&\._%\xd7]+)",
re.U)
config = flags.FLAGS
if config.mode == "train":
train(config)
elif config.mode == "prepro":
prepro(config)
elif config.mode == "debug":
config.num_steps = 2
config.val_num_batches = 1
config.checkpoint = 1
config.period = 1
train(config)
elif config.mode == "test":
test(config)
elif config.mode == "demo":
demo(config)
else:
print("Unknown mode")
exit(0)
def main(_):
config = flags.FLAGS
if config.mac == "m40":
config.ta_w2v = os.path.join(home, "data", "word2vec", "race_word_vector_300.txt")
config.ta_c2v = os.path.join(home, "data", "word2vec", "single_w2v_300.txt")
if config.mode == "train":
train(config)
elif config.mode == "prepro":
prepro(config)
elif config.mode == "debug":
config.num_steps = 1
config.val_num_batches = 1
config.checkpoint = 1
config.period = 1
config.batch_size = 2
train(config)
elif config.mode == "test":
test(config)
elif config.mode == "demo":
demo(config)
else:
print("Unknown mode")
exit(0)
# -*- coding:Utf-8 -*-
from PIL import Image,ImageDraw
print(dir())
from main import demo
if __name__=="__main__":
(pack,blocks)=demo()
im=Image.new("RGBA",(pack.root.w, pack.root.h))
w =ImageDraw.ImageDraw(im) #Canvas(master, width=pack.root.w, height=pack.root.h)
# #print(dir(w))
# w.configure(background='black')
# w.bind("<Motion>", showxy)
# w.pack()
for n in range(len(blocks)):
block = blocks[n];
if (block.fit):
#print(block.fit.x, block.fit.y, block.w, block.h,block.used)
w.rectangle((block.fit.x, block.fit.y,block.fit.x+block.w, block.fit.y+block.h),fill="green", outline="black")
w.text((block.fit.x,block.fit.y+10),text=str(n))
im.save("out.png")
def main():
demo('gym_copter:Lander2D-v0', heuristic,
(PositionHoldPidController(), DescentPidController()))
def main():
demo('gym_copter:Lander1D-v0', heuristic, (DescentPidController(),))
def main(_):
config = flags.FLAGS
#srun -p sugon --gres=gpu:1 python config-v1.py prepro Raw 2 128 50
mode = sys.argv[1]
model_name = sys.argv[2]
num_heads = sys.argv[3]
train_dir = 'train-v1'
if model_name in ['Soft_T5']:
fixed_c_maxlen = sys.argv[4]
learning_rate = sys.argv[5]
bucket_slop_min = float(sys.argv[6])
bucket_slop_max = float(sys.argv[7])
l1_width = int(sys.argv[8])
l2_width = int(sys.argv[9])
stddev = float(sys.argv[10])
soft_t5_activation = sys.argv[11]
trail = sys.argv[12]
dir_name = os.path.join(
train_dir, "_".join([
model_name,
str(num_heads),
str(fixed_c_maxlen),
str(learning_rate),
str(bucket_slop_min),
str(bucket_slop_max),
str(l1_width),
str(l2_width),
str(stddev), soft_t5_activation, trail
]))
elif model_name in ['Soft_T5_Nob']:
fixed_c_maxlen = sys.argv[4]
learning_rate = sys.argv[5]
soft_t5_activation = sys.argv[6]
trail = sys.argv[7]
dir_name = os.path.join(
train_dir, "_".join([
model_name,
str(num_heads),
str(fixed_c_maxlen),
str(learning_rate), soft_t5_activation, trail
]))
elif model_name in ['T5', 'T5_Nob']:
t5_num_buckets = int(sys.argv[4])
t5_max_distance = int(sys.argv[5])
trail = sys.argv[6]
dir_name = os.path.join(
train_dir, "_".join([
model_name,
str(num_heads),
str(t5_num_buckets),
str(t5_max_distance), trail
]))
else:
trail = sys.argv[4]
dir_name = os.path.join(train_dir,
"_".join([model_name,
str(num_heads), trail]))
if not os.path.exists(train_dir):
os.mkdir(train_dir)
if not os.path.exists(dir_name):
os.mkdir(dir_name)
event_log_dir = os.path.join(dir_name, "event")
save_dir = os.path.join(dir_name, "model")
answer_dir = os.path.join(dir_name, "answer")
answer_file = os.path.join(answer_dir, "answer.json")
if not os.path.exists(event_log_dir):
os.makedirs(event_log_dir)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if not os.path.exists(answer_dir):
os.makedirs(answer_dir)
config.mode = mode
config.model = model_name
config.num_heads = int(num_heads)
config.trail = trail
if config.model in ['Soft_T5', 'Soft_T5_TPE']:
config.fixed_c_maxlen = int(fixed_c_maxlen)
config.learning_rate = float(learning_rate)
config.bucket_slop_min = bucket_slop_min
config.bucket_slop_max = bucket_slop_max
config.soft_t5_activation = soft_t5_activation
config.l1_width = l1_width
config.l2_width = l2_width
config.stddev = stddev
if config.model in ['Soft_T5_Nob']:
config.fixed_c_maxlen = int(fixed_c_maxlen)
config.learning_rate = float(learning_rate)
config.soft_t5_activation = soft_t5_activation
if config.model in ['T5', 'T5_TPE', 'T5_Nob']:
config.t5_num_buckets = t5_num_buckets
config.t5_max_distance = t5_max_distance
config.event_log_dir = event_log_dir
config.save_dir = save_dir
config.answer_file = answer_file
if config.mode == "train":
train(config)
elif config.mode == "prepro":
prepro(config)
elif config.mode == "debug":
config.num_steps = 2
config.val_num_batches = 1
config.checkpoint = 1
config.period = 1
train(config)
elif config.mode == "test":
test(config)
elif config.mode == "demo":
demo(config)
else:
print("Unknown mode")
exit(0)
#!/usr/bin/env python3
'''
Heuristic demo for 2D Copter hovering
Copyright (C) 2021 Simon D. Levy
MIT License
'''
from pidcontrollers import AltitudeHoldPidController
from main import demo
def heuristic(state, pidcontrollers):
z, dz = state
alt_pid = pidcontrollers[0]
return (alt_pid.getDemand(z, dz), )
demo('gym_copter:Hover1D-v0', heuristic, (AltitudeHoldPidController(), ))
MIT License
'''
from pidcontrollers import AltitudeHoldPidController
from pidcontrollers import AngularVelocityPidController
from pidcontrollers import PositionHoldPidController
from main import demo
def heuristic(state, pidcontrollers):
y, dy, z, dz, phi, dphi = state
rate_pid, pos_pid, alt_pid = pidcontrollers
rate_todo = rate_pid.getDemand(dphi)
pos_todo = pos_pid.getDemand(y, dy)
phi_todo = rate_todo + pos_todo
hover_todo = alt_pid.getDemand(z, dz)
return hover_todo-phi_todo, hover_todo+phi_todo
demo('gym_copter:Hover2D-v0', heuristic,
(AngularVelocityPidController(),
PositionHoldPidController(),
AltitudeHoldPidController()))
