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How to use genericLogger method in fMBT
Best Python code snippet using fMBT_python
train.py
Source:train.py 
1"""2This file is used for training the DQN (DQfD)3"""4import neptune.new as neptune5import torch6import numpy as np7import os8from agents import *9from env import construct_task_env10# from utils import *11from core import *12from my_logging import *13nep_logger = GenericLogger(2000)14# FAST_DOWNWARD_PATH = "/fast_downward/"15"""16Hyperparameters used for the training17"""18# learning_rate = 1e-4 # learning rate reduced for training model after 5170 episodes19# max_episodes = 100000020# pretrain_epochs = 1000021# t_max = 60022# print_interval= 2023# target_update = 10 # episode(s)24# train_steps = 1025def create_agent(test_case_id, *args, **kwargs):26 '''27 Method that will be called to create your agent during testing.28 You can, for example, initialize different class of agent depending on test case.29 '''30 return DQfDAgent(test_case_id=test_case_id)31def train(agent, env, weights_path=None, tag=None, disable_il=False):32 agent_init = {'fast_downward_path': FAST_DOWNWARD_PATH, 'agent_speed_range': (-3,-1), 'gamma' : 1}33 agent.initialize(**agent_init)34 num_actions = len(env.actions)35 if disable_il:36 agent.init_train()37 else:38 # agent.init_train(expert_demo_path=os.path.join("agent", "expert_dem_combined_easy_10_20.pt"))39 agent.init_train(expert_demo_path=os.path.join("agent", "expert_dem_combined.pt"))40 rewards = []41 losses = {42 'J_E': [],43 'J_DQ': [],44 'Total': []45 }46 optimizer = torch.optim.Adam(agent.model.parameters(), lr=learning_rate, weight_decay=1e-4)47 GenericLogger.initialize_writer()48 GenericLogger.add_params({49 "weight_decay": 1e-4,50 "learning_rate": learning_rate,51 "target_update": target_update,52 "train_steps": train_steps,53 "epsilon_decay": epsilon_decay,54 "gamma": gamma,55 "max_epsilon": max_epsilon,56 "min_epsilon": min_epsilon,57 "batch_size": batch_size,58 "buffer_limit": buffer_limit,59 "min_buffer": min_buffer,60 })61 if opt.tag is not None:62 GenericLogger.add_tag(opt.tag)63 agent.model.train()64 epoch = 065 if weights_path is None and not disable_il:66 print("Beginning Expert pretraining phase...")67 # Phase 1: Expert initialization phase68 for epoch in range(pretrain_epochs):69 loss, J_E, J_DQ = agent.optimize(optimizer, expert_phase=True)70 if epoch % (print_interval * 10) == 0 and epoch > 0:71 print("[Epoch {}]\tavg loss: : {:.6f}\tavg J_E loss: {:.6f}\tavg J_DQ loss: {:.6f}".format(epoch, loss.item(), J_E.item(), J_DQ.item()))72 # print("[Epoch {}]\tavg loss: : {:.6f}".format(epoch, np.mean(losses[-print_interval*5:])))73 # Update target network every once in a while74 if epoch % target_update == 0:75 agent.update_tgt_train()76 # if epoch % 2 == 0 and epoch > 0:77 # break78 elif not weights_path is None:79 print("Loading preexisting weights....")80 agent.model.load_state_dict(torch.load(weights_path))81 agent.update_tgt_train()82 else:83 print("Imitation learning is disabled.")84 # torch.save(agent.target.state_dict(), "phase1.pt")85 manual = False86 # Phase 2: Exploration with some sampling of expert data87 print("Beginning exploration and training phase...")88 episode_lens = []89 prefix = f"last_model_{INPUT_SHAPE[1]}_{INPUT_SHAPE[2]}_"90 for episode in range(max_episodes):91 epsilon = compute_epsilon(episode)92 state = env.reset()93 episode_rewards = 0.094 experiences = []95 # hidden_state, cell_state = agent.model.reset_hidden_states(1)96 # Try the epsiode97 for t in range(t_max):98 # action, hidden_state, cell_state = agent.act(state, hidden_state, cell_state, epsilon=epsilon, env=env, manual=manual)99 action = agent.act(state, epsilon=epsilon, env=env, manual=manual)100 next_state, reward, done, info = env.step(action)101 experiences.append(Transition(state, [action], [reward], next_state, [done]))102 episode_rewards += reward103 if done:104 if t > 50:105 print(f"Weird value t: {t}")106 break107 state = next_state108 if len(experiences) > 50:109 print(f"Found anomaly {len(experiences)}")110 rewards.append(episode_rewards)111 # Record down all the stuff related to episodes112 episode_lens.append(len(experiences))113 # Store all episodes into the replay buffer using the agent's store method114 if len(experiences) > NUM_LOOKBACK_TIMESTEPS + 2:115 agent.record_episode_train(experiences)116 if agent.enough_memory_train():117 manual = False118 for i in range(train_steps):119 epoch += 1120 loss, J_E, J_DQ = agent.optimize(optimizer)121 losses['Total'].append(loss.item())122 losses['J_DQ'].append(J_DQ.item())123 losses['J_E'].append(J_E.item())124 if episode % print_interval == 0 and episode > 0:125 GenericLogger.add_scalar('Mean episode length', np.mean(episode_lens[-print_interval:]))126 # GenericLogger.add_scalar('Min episode length', np.min(episode_lens[-print_interval:]))127 # GenericLogger.add_scalar('Max episode length', np.sum(episode_lens[-print_interval:]))128 GenericLogger.add_scalar('Mean episode reward', np.mean(rewards[-print_interval:]))129 print("[Episode {}]\tavg rewards : {:.3f},\tavg loss: : {:.6f},\tavg J_E loss {:.6f},\tavg J_DQ loss {:.6f}, \130 \tbuffer size : {},\t epsilon: {}".format(131 episode, np.mean(rewards[-print_interval:]), np.mean(losses['Total'][-print_interval*10:]),132 np.mean(losses['J_E'][-print_interval*10:]), np.mean(losses['J_DQ'][-print_interval*10:]), 133 len(agent.memory), epsilon * 100))134 135 print("TOTAL: [Episode {}]\tavg rewards : {:.3f},\tavg loss: : {:.6f},\tavg J_E loss {:.6f},\tavg J_DQ loss {:.6f}, \136 \tbuffer size : {},\t epsilon: {}".format(137 episode, np.mean(rewards[-print_interval*10:]), np.mean(losses['Total'][-print_interval*10:]),138 np.mean(losses['J_E'][-print_interval*10:]), np.mean(losses['J_DQ'][-print_interval*10:]), 139 len(agent.memory), epsilon * 100))140 agent.memory.distribution()141 if episode % (target_update*2000) == 0:142 save_filename = prefix + str(episode)143 torch.save(agent.target.state_dict(), f"{save_filename}.pt")144 # Update target network every once in a while145 if episode % target_update == 0:146 agent.update_tgt_train()147 # print(">>>>>>>>>>> Saving target network to disc")148if __name__ == "__main__":149 import sys150 import time151 import argparse152 from env import construct_task_env153 parser = argparse.ArgumentParser()154 parser.add_argument('--weights_path', default=None, type=str, help="Path to weights file")155 parser.add_argument('--tag', default=None, type=str, help="Tag for neptune ai logging")156 parser.add_argument('--disable-il', default=False, action="store_true", help="Disable imitation learning")157 opt = parser.parse_args()158 test_env = construct_task_env()159 agent = create_agent(0)160 train(agent, test_env, weights_path=opt.weights_path, tag=opt.tag, disable_il=opt.disable_il)...
LogHandler.py
Source:LogHandler.py
1#####################################################################################################2#####################################################################################################3## Created on Sep 01, 20134## @author: Gerasimos Kassaras5## E-mail: g.kassaras@gmail.com6## Nickname: Lamehacker Free Industries7## 8## Comment: This class is used as a centralized logger for the scanner9#######################################################################################################10#######################################################################################################11import logging12from bs4 import UnicodeDammit13genericLoggerName = 'CapCake_logger'14genericLogger = logging.getLogger(genericLoggerName)15genericLoggerHandler = logging.FileHandler('CapCake_logger.log')16formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')17genericLoggerHandler.setFormatter(formatter)18genericLogger.addHandler(genericLoggerHandler)19genericLogger.setLevel(logging.DEBUG)20class loggingHandler: # logs info,warning,error,critical,debug events.21 def __init__(self):22 '''23 Description: This class is used to manage the logging information from the scanner.24 Status: Finished.25 Usage: This is used to initialize with the proper logging level.26 '''27 self.logInfo("--- Package: LoggingManager - Module: LoggingHandler Class: loggingHandler Initiated ---")28 29#-------------------------------------------------------------------------------------------------------------------------------------------------------------------30 def logInfo(self,msg):31 genericLogger.info(msg)32#-------------------------------------------------------------------------------------------------------------------------------------------------------------------33 def logWarning(self,msg):34 genericLogger.warn(msg,exc_info=True)35#-------------------------------------------------------------------------------------------------------------------------------------------------------------------36 def logError(self,msg):37 genericLogger.error(msg,exc_info=True)38#-------------------------------------------------------------------------------------------------------------------------------------------------------------------39 def logCritical(self,msg):40 genericLogger.critical(msg,exc_info=True)41#-------------------------------------------------------------------------------------------------------------------------------------------------------------------42 def logDebug(self,msg):...
my_logging.py
Source:my_logging.py
1import neptune.new as neptune2default_update_interval = 0 # Not used3class NepLoggerEmpty():4 ''' NepLogger that does nothing '''5 def __init__(self, update_interval=default_update_interval):6 pass7 def __str__(self):8 return repr(self)9 def initialize_writer(self):10 pass11 def get_writer(self):12 pass13 def add_scalar(self, tag, item):14 pass15 def add_params(self, params):16 pass17 def add_tag(self, tag):18 pass19class NepLogger():20 ''' Logger that logs to Neptune.ai '''21 def __init__(self, update_interval=default_update_interval):22 self.logger = None23 self.update_interval = update_interval24 def __str__(self):25 return repr(self)26 def initialize_writer(self):27 self.logger = neptune.init(project='tenvinc/cs4246-project', 28 source_files=['agent/*.py', 'requirements.txt'])29 def get_writer(self):30 return self.logger31 def add_scalar(self, tag, item):32 self.logger[tag].log(item)33 def add_params(self, params):34 self.logger["parameters"] = params35 def add_tag(self, tag):36 self.logger["sys/tags"].add([tag])37class GenericLogger():38 ''' Generic logger singleton '''39 instance = None40 def __init__(self, update_interval=default_update_interval):41 if not GenericLogger.instance:42 GenericLogger.instance = NepLoggerEmpty(update_interval)43 print(GenericLogger.instance)44 45 def __getattr__(self, name):46 return getattr(self.instance, name)47 @classmethod48 def initialize_writer(cls):49 return cls.instance.initialize_writer()50 @classmethod51 def get_writer(cls):52 return cls.instance.get_writer()53 @classmethod54 def add_scalar(cls, tag, item):55 assert cls.instance is not None56 cls.instance.add_scalar(tag, item)57 @classmethod58 def add_params(cls, params):59 assert cls.instance is not None60 cls.instance.add_params(params)61 62 @classmethod63 def add_tag(cls, tag):64 assert cls.instance is not None...
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