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(_): 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()))