def main():
doers = _get_doers(shell)
doers.update(_get_doers(utils))
possible_actions = doers.keys() + ['start', 'stop', 'status']
args = arguments.get_args(possible_actions)
if args.action is None:
print "No action"
sys.exit(1)
apiclient = None
if args.no_api is False:
os_options = arguments.OpenstackOptions(args, os.environ)
if args.debug:
print os_options
apiclient = client.Client(username=os_options.username,
password=os_options.password,
tenant_name=os_options.tenant_name,
endpoint=os_options.endpoint,
auth_url=os_options.auth_url)
if args.client_id:
apiclient.client_id = args.client_id
if args.action in doers:
try:
return doers[args.action](apiclient, args)
except Exception as e:
print ('ERROR {0}'.format(e))
return 1
create_dir(args.jobs_dir, do_log=False)
freezer_scheduler = FreezerScheduler(apiclient=apiclient,
interval=int(args.interval),
job_path=args.jobs_dir)
daemon = Daemon(daemonizable=freezer_scheduler)
if args.action == 'start':
daemon.start(log_file=args.log_file)
elif args.action == 'stop':
daemon.stop()
elif args.action == 'reload':
daemon.reload()
elif args.action == 'status':
daemon.status()
return os.EX_OK
def main():
"""
Main entry point for the program.
"""
args = get_args(get_version())
if not args.solution_file:
args.solution_file = default_solution()
# create a new MonoTool object using our project path.
mt = MonoTool(args.solution_file)
method = getattr(mt, args.method)
res = method(**args.__dict__)
if res:
print res
def main():
doers = _get_doers(shell)
doers.update(_get_doers(utils))
possible_actions = doers.keys() + ['start', 'stop', 'status']
args = arguments.get_args(possible_actions)
if args.action is None:
print ('No action')
return os.EX_DATAERR
apiclient = None
if args.no_api is False:
apiclient = client.Client(opts=args)
if args.client_id:
apiclient.client_id = args.client_id
if args.action in doers:
try:
return doers[args.action](apiclient, args)
except Exception as e:
print ('ERROR {0}'.format(e))
return os.EX_SOFTWARE
freezer_scheduler = FreezerScheduler(apiclient=apiclient,
interval=int(args.interval),
job_path=args.jobs_dir)
if args.no_daemon:
print ('Freezer Scheduler running in no-daemon mode')
daemon = NoDaemon(daemonizable=freezer_scheduler)
else:
daemon = Daemon(daemonizable=freezer_scheduler)
if args.action == 'start':
daemon.start(log_file=args.log_file)
elif args.action == 'stop':
daemon.stop()
elif args.action == 'reload':
daemon.reload()
elif args.action == 'status':
daemon.status()
return os.EX_OK
def main():
"""Main training program."""
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Timer.
timers = Timers()
# Arguments.
args = get_args()
args.mem_length = args.mem_length if args.transformer_xl else 0
if args.load:
args.experiment_name = os.path.basename(os.path.normpath(args.load))
else:
args.experiment_name = args.experiment_name + datetime.now().strftime(
"%m-%d-%H-%M")
if args.save:
args.save = os.path.join(args.save, args.experiment_name)
# Pytorch distributed.
initialize_distributed(args)
# Random seeds for reproducability.
set_random_seed(args.seed)
# Data stuff.
train_data, val_data, test_data, args.vocab_size, \
args.eod_token = get_train_val_test_data(args)
# Model, optimizer, and learning rate.
model, optimizer, lr_scheduler = setup_model_and_optimizer(args)
if args.load is not None:
with FileLock("/root/checkpoint_lock", timeout=-1):
args.iteration = load_checkpoint(model, optimizer, lr_scheduler,
args)
else:
args.iteration = 0
torch.distributed.barrier()
summary_writer = None
if torch.distributed.get_rank() == 0:
print('Pretrain GPT2 model')
print_args(args)
summary_writer = get_sample_writer(base=args.summary_dir,
name=args.experiment_name,
iteration=args.iteration)
# Resume data loader if necessary.
if args.resume_dataloader:
if train_data is not None:
train_data.batch_sampler.start_iter = args.iteration % \
len(train_data)
if val_data is not None:
start_iter_val = (args.train_iters // args.save_interval) * \
args.eval_interval
val_data.batch_sampler.start_iter = start_iter_val % \
len(val_data)
if train_data is not None:
train_data_iterator = iter(train_data)
else:
train_data_iterator = None
if val_data is not None:
val_data_iterator = iter(val_data)
else:
val_data_iterator = None
# TODO: figure out how to properly set this especially when resuming training
iteration = 0
if args.train_iters > 0:
if args.do_train:
with ExitStack() as stack:
def save_on_exit(args_, model_, optimizer_, lr_scheduler_):
save_checkpoint(args_.iteration, model_, optimizer_,
lr_scheduler_, args_)
# stack.callback(save_on_exit, args, model, optimizer, lr_scheduler)
iteration, skipped = train(model,
optimizer,
lr_scheduler,
train_data_iterator,
val_data_iterator,
timers,
args,
summary_writer=summary_writer)
if args.do_valid:
prefix = 'the end of training for val data'
val_loss = evaluate_and_print_results(prefix, val_data_iterator,
model, args, timers, False)
if args.save and iteration != 0:
save_checkpoint(iteration, model, optimizer, lr_scheduler, args)
if test_data is not None:
test_data_iterator = iter(test_data)
else:
test_data_iterator = None
if args.do_test:
# Run on test data.
prefix = 'the end of training for test data'
evaluate_and_print_results(prefix, test_data_iterator, model, args,
timers, True)
import torch.nn.functional as F
import torch.optim as optim
import algo
from arguments import get_args
from envs import make_vec_envs
from model_nomodulation import Policy
from storage import RolloutStorage
from utils import get_vec_normalize
from visualize import visdom_plot
from tensorboardX import SummaryWriter
#####################################
# prepare
args = get_args()
assert args.algo in ['a2c', 'ppo', 'acktr']
if args.recurrent_policy:
assert args.algo in ['a2c', 'ppo'], \
'Recurrent policy is not implemented for ACKTR'
num_updates = int(args.num_frames) // args.num_steps // args.num_processes
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
try:
os.makedirs(args.log_dir)
except OSError:
def load_populations(
dataset_name,
base_path="../logs-2020-neurips/log_when_to_stop/_recorded",
filter_func=None):
result_info_dict = {}
data_path = os.path.join(base_path, dataset_name)
for i, exp_dir in enumerate(os.listdir(data_path)):
if not filter_func(exp_dir):
continue
full_path = os.path.join(data_path, exp_dir)
exp_tuple = exp_dir.split("+")[0].split("-")[:3]
try:
args_kwargs = {
"model_name":
exp_tuple[0],
"dataset_class":
"Planetoid" if exp_tuple[1] != "PPI" else "PPI",
"dataset_name":
exp_tuple[1],
"custom_key":
exp_tuple[2] + ("" if exp_tuple[1] != "PubMed" else "-500") +
"-ES",
}
args = get_args(**args_kwargs)
result_info = {"full_path": full_path, "args": args}
for perf_file in os.listdir(full_path):
if "val_loss" in perf_file:
result_info["val_loss"] = os.path.join(
full_path, perf_file)
elif "val_perf" in perf_file:
result_info["val_perf"] = os.path.join(
full_path, perf_file)
elif "test_perf" in perf_file:
result_info["test_perf"] = os.path.join(
full_path, perf_file)
result_info_dict[exp_dir] = result_info
except Exception as e:
cprint(f"Exception in {full_path} and {exp_tuple}, {e}", "red")
for exp_dir, v in result_info_dict.items():
args = v["args"]
val_loss_matrix = np.load(v["val_loss"], allow_pickle=True)
val_perf_matrix = np.load(v["val_perf"], allow_pickle=True)
test_perf_matrix = np.load(v["test_perf"], allow_pickle=True)
if "PPI" in exp_dir:
val_loss_matrix = val_loss_matrix[:30, :]
val_perf_matrix = val_perf_matrix[:30, :]
test_perf_matrix = test_perf_matrix[:30, :]
test_perf_at_best_val_list = simulate_early_stop(
val_loss_matrix, val_perf_matrix, test_perf_matrix,
args.early_stop_patience, args.early_stop_queue_length,
args.early_stop_threshold_loss, args.early_stop_threshold_perf,
args.epochs)
result_info_dict[exp_dir][
"test_perf_at_best_val_list"] = test_perf_at_best_val_list
return result_info_dict
def main():
# load hyper parameters
args = get_args()
num_updates = int(args.num_frames // args.num_steps)
start = time.time()
record = {'steps': [0], 'max': [0], 'mean': [0], 'min': [0], 'query': [0]}
config = configparser.ConfigParser()
config.read('config.ini', encoding='utf-8')
data_index = config.getint('data', 'data_index')
actions = [0, 1, 2, 3, 4, 5, 11, 12]
key_map = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 11, 7: 12, 8: -1}
# query_cnt counts queries to the expert
query_cnt = data_index
# environment initial
envs = Env(args.env_name, args.num_stacks)
# action_shape is the size of the discrete action set, here is 18
# Most of the 18 actions are useless, find important actions
# in the tips of the homework introduction document
action_shape = envs.action_space.n
# observation_shape is the shape of the observation
# here is (210,160,3)=(height, weight, channels)
observation_shape = envs.observation_space.shape
print(action_shape, observation_shape)
# agent initial
# you should finish your agent with DaggerAgent
# e.g. agent = MyDaggerAgent()
agent = ExampleAgent()
# You can play this game yourself for fun
if args.play_game:
obs = envs.reset()
while True:
im = Image.fromarray(obs)
im.save('imgs/' + str('screen') + '.jpeg')
action = int(input('input action'))
while action < 0 or action >= action_shape:
action = int(input('re-input action'))
obs_next, reward, done, _ = envs.step(action)
obs = obs_next
if done:
obs = envs.reset()
data_set = {'data': [], 'label': []}
# start train your agent
for i in range(data_index):
data_path = 'data/data_batch_' + str(i) + '/'
for j in range(args.num_steps):
pic_path = data_path + str(j) + '.jpeg'
data_set['data'].append(cv2.imread(pic_path))
with open(data_path + 'label.txt', 'r') as f:
for label_tmp in f.readlines():
data_set['label'].append(int(label_tmp))
agent.update(data_set['data'], data_set['label'])
with open('performance.txt') as f:
record_temp = eval(f.readline())
if record_temp is not None:
record = record_temp
for i in range(data_index, num_updates):
# an example of interacting with the environment
# we init the environment and receive the initial observation
obs = envs.reset()
# we get a trajectory with the length of args.num_steps
for step in range(args.num_steps):
# Sample actions
epsilon = 0.05
if np.random.rand() < epsilon:
# we choose a random action
action = envs.action_space.sample()
else:
# we choose a special action according to our model
action = agent.select_action(obs)
# interact with the environment
# we input the action to the environments and it returns some information
# obs_next: the next observation after we do the action
# reward: (float) the reward achieved by the action
# down: (boolean) whether it’s time to reset the environment again.
# done being True indicates the episode has terminated.
obs_next, reward, done, _ = envs.step(action)
# we view the new observation as current observation
obs = obs_next
# if the episode has terminated, we need to reset the environment.
if done:
envs.reset()
# an example of saving observations
if args.save_img:
im = Image.fromarray(obs)
im.save('imgs/' + str(step) + '.jpeg')
data_set['data'].append(obs)
# You need to label the images in 'imgs/' by recording the right actions in label.txt
with open('imgs/label.txt', 'w+') as f:
img_set = data_set['data'][-args.num_steps:]
for img in img_set:
cv2.imshow('Current Frame', img)
cmd_in = cv2.waitKey(0) - 48
while cmd_in not in key_map.keys():
pass
cmd_in = key_map.get(cmd_in)
print(cmd_in)
if cmd_in is -1:
f.write(str(actions[random.randint(0, 7)]) + '\n')
else:
f.write(str(cmd_in) + '\n')
if not os.path.exists('data/data_batch_' + str(data_index) + '/'):
shutil.copytree('./imgs', 'data/data_batch_' + str(data_index))
data_index += 1
config.set('data', 'data_index', str(data_index))
config.write(open('config.ini', 'w'))
# After you have labeled all the images, you can load the labels
# for training a model
with open('imgs/label.txt', 'r') as f:
for label_tmp in f.readlines():
data_set['label'].append(int(label_tmp))
# design how to train your model with labeled data
agent.update(data_set['data'], data_set['label'])
query_cnt += 1
if (i + 1) % args.log_interval == 0:
total_num_steps = (i + 1) * args.num_steps
obs = envs.reset()
reward_episode_set = []
reward_episode = 0
# evaluate your model by testing in the environment
for step in range(args.test_steps):
action = agent.select_action(obs)
# you can render to get visual results
# envs.render()
obs_next, reward, done, _ = envs.step(action)
reward_episode += reward
obs = obs_next
if done:
reward_episode_set.append(reward_episode)
reward_episode = 0
envs.reset()
if len(reward_episode_set) == 0:
reward_episode_set.append(0)
end = time.time()
print(
"TIME {} Updates {}, num timesteps {}, FPS {} \n query {}, avrage/min/max reward {:.1f}/{:.1f}/{:.1f}"
.format(
time.strftime("%Hh %Mm %Ss",
time.gmtime(time.time() - start)), i,
total_num_steps, int(total_num_steps / (end - start)),
query_cnt, np.mean(reward_episode_set),
np.min(reward_episode_set), np.max(reward_episode_set)))
record['steps'].append(total_num_steps)
record['mean'].append(np.mean(reward_episode_set))
record['max'].append(np.max(reward_episode_set))
record['min'].append(np.min(reward_episode_set))
record['query'].append(query_cnt)
plot(record)
import random
import pickle
#import visdom
from utils import *
from loader import *
from model import BiLSTM_CRF
from arguments import get_args
from processor import generate_batch_data, generate_batch_para, generate_batch_rep
t = time.time()
opts, parameters=get_args()
experiment=None
models_path = "models/"
use_gpu = parameters['use_gpu']
mapping_file = 'models/mapping.pkl'
name = parameters['name']
model_name = models_path + name #get_name(parameters)
tmp_model = model_name + '.tmp'
if not os.path.exists(models_path):
os.makedirs(models_path)
def initialize_agent_and_env(is_test=False):
"""
Read and parse commandline arguments to the args variable.
Initiate an agent and environment based on the arguments.
:return: agent, env, args
"""
args = get_args(is_test)
# set schema
cfg.schema = args.schema
# set dataset nubmer
cfg.dataset_number = args.dataset_number
# set number of steps in session
cfg.MAX_NUM_OF_STEPS = args.episode_length
# hack to change the default value of the max_steps argument in the __init__ of ATENAEnvCont to cfg.MAX_NUM_OF_STEPS
atena_init_default_args = list(ATENAEnvCont.__init__.__defaults__)
atena_init_default_args[0] = cfg.MAX_NUM_OF_STEPS
ATENAEnvCont.__init__.__defaults__ = tuple(atena_init_default_args)
# set env settings
cfg.stack_obs_num = args.stack_obs_num
cfg.obs_with_step_num = args.obs_with_step_num
cfg.no_back = args.no_back
cfg.bins_sizes = args.bins_sizes
#filter_terms_bins_sizes_helper(FilterTermsBinsSizes(cfg.bins_sizes))
#paremetric_softmax_idx_action_maps_helper()
# set reward types to use
cfg.no_diversity = args.no_diversity
cfg.no_interestingness = args.no_inter
cfg.use_humans_reward = args.use_humans_reward
cfg.humans_reward_interval = args.humans_reward_interval
cfg.count_data_driven = args.count_data_driven
# set number of hidden units for gaussian policy
cfg.n_hidden_channels = args.n_hidden_channels
# set architecture type
cfg.arch = args.arch
cfg.beta = args.beta
# optimization settings
cfg.max_nn_tokens = args.max_nn_tokens
cfg.cache_dfs_size = args.cache_dfs_size
cfg.cache_tokenization_size = args.cache_tokenization_size
cfg.cache_distances_size = args.cache_distances_size
# set reward coefficients
cfg.humanity_coeff = args.humanity_coeff
cfg.diversity_coeff = args.diversity_coeff
cfg.kl_coeff = args.kl_coeff
cfg.compaction_coeff = args.compaction_coeff
args.outdir = chainerrl.experiments.prepare_output_dir(args, args.outdir)
cfg.outdir = args.outdir
# https://stackoverflow.com/questions/13479295/python-using-basicconfig-method-to-log-to-console-and-file
# logging file path
log_path = os.path.join(args.outdir, 'training_results.log')
# set up logging to file
logging.basicConfig(
level=logging.INFO,
format='[%(asctime)s] {%(pathname)s:%(lineno)d} %(levelname)s - %(message)s',
filename=log_path,
datefmt='%H:%M:%S'
)
# set up logging to console
console = logging.StreamHandler()
console.setLevel(args.logger_level)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
# set logging of the entire episode every LOG_INTERVAL steps
cfg.log_interval = args.log_interval
cfg.num_envs = args.num_envs
ATENAEnvCont.LOG_INTERVAL = int(args.log_interval / args.num_envs)
# TODO (baelo): delete it
# Set filter term bins
#filter_terms_bins_sizes_helper(FilterTermsBinsSizes(cfg.bins_sizes))
#paremetric_softmax_idx_action_maps_helper()
# Set random seed
chainerrl.misc.set_random_seed(args.seed, gpus=(args.gpu,))
# create environment
env = make_env(args, args.env, args.seed, args.render, args.outdir)
# choose algorithm
args.algo = AlgoName(args.algo)
if args.algo is AlgoName.CAPG_PPO: # capg
model = PPOModel(env,
args.gpu,
args.n_hidden_channels,
args.adam_lr,
args.ppo_update_interval,
args.outdir,
args.load,
args.use_clipped_gaussian)
elif args.algo is AlgoName.CAPG_TRPO: # capg
model = TRPOModel(env,
args.gpu,
args.n_hidden_channels,
args.trpo_update_interval,
args.outdir,
args.load,
args.use_clipped_gaussian)
elif args.algo == AlgoName.CHAINERRL_PPO:
model = PPOchianerrl(args, env)
else:
raise NotImplementedError
agent = model.agent
return agent, env, args
def load_ocnli_data(data_path, data_type, tokenizer):
args = get_args()
filename = os.path.join(data_path, data_type+'.json')
objs = []
with open(filename) as fin:
for line in fin:
objs.append(json.loads(line.strip()))
pad_id = tokenizer.encoder['<pad>']
args.eod_token = tokenizer.encoder['<eod>']
all_tokens_1 = []
all_masks_1 = []
all_tokens_2 = []
all_masks_2 = []
all_tokens_3 = []
all_masks_3 = []
all_labels = []
for obj in objs:
if obj['label'] == '-':
continue
prompt = "{}？对，".format(obj['sentence1'])
prompt_tokens = tokenizer.encode(prompt)
prompt_len = len(prompt_tokens)
tokens = prompt_tokens + tokenizer.encode(obj['sentence2'])
second_mask = [0] * (args.seq_length-1)
for idx in range(prompt_len-1, len(tokens)-1):
second_mask[idx] = 1
all_masks_1.append(second_mask)
token_length = len(tokens)
assert token_length < args.seq_length
tokens.extend([pad_id] * (args.seq_length - token_length))
all_tokens_1.append(tokens)
prompt = "{}？错，".format(obj['sentence1'])
prompt_tokens = tokenizer.encode(prompt)
prompt_len = len(prompt_tokens)
tokens = prompt_tokens + tokenizer.encode(obj['sentence2'])
second_mask = [0] * (args.seq_length-1)
for idx in range(prompt_len-1, len(tokens)-1):
second_mask[idx] = 1
all_masks_2.append(second_mask)
token_length = len(tokens)
assert token_length < args.seq_length
tokens.extend([pad_id] * (args.seq_length - token_length))
all_tokens_2.append(tokens)
prompt = "{}？也许，".format(obj['sentence1'])
prompt_tokens = tokenizer.encode(prompt)
prompt_len = len(prompt_tokens)
tokens = prompt_tokens + tokenizer.encode(obj['sentence2'])
second_mask = [0] * (args.seq_length-1)
for idx in range(prompt_len-1, len(tokens)-1):
second_mask[idx] = 1
all_masks_3.append(second_mask)
token_length = len(tokens)
assert token_length < args.seq_length
tokens.extend([pad_id] * (args.seq_length - token_length))
all_tokens_3.append(tokens)
if obj['label'] == 'entailment':
all_labels.append([0])
elif obj['label'] == 'contradiction':
all_labels.append([1])
else:
all_labels.append([2])
all_tokens_1 = torch.tensor(all_tokens_1, dtype=torch.long)
all_masks_1 = torch.tensor(all_masks_1, dtype=torch.float)
all_tokens_2 = torch.tensor(all_tokens_2, dtype=torch.long)
all_masks_2 = torch.tensor(all_masks_2, dtype=torch.float)
all_tokens_3 = torch.tensor(all_tokens_3, dtype=torch.long)
all_masks_3 = torch.tensor(all_masks_3, dtype=torch.float)
all_labels = torch.tensor(all_labels, dtype=torch.long)
dataset = TensorDataset(all_tokens_1, all_masks_1, all_tokens_2, all_masks_2, all_tokens_3, all_masks_3, all_labels)
# Data parallel arguments.
world_size = mpu.get_data_parallel_world_size()
rank = mpu.get_data_parallel_rank()
global_batch_size = args.batch_size * world_size
num_workers = args.num_workers
# Use a random sampler with distributed batch sampler.
if data_type == 'train':
sampler = RandomSampler(dataset)
else:
sampler = torch.utils.data.SequentialSampler(dataset)
batch_sampler = DistributedBatchSampler(sampler=sampler,
batch_size=global_batch_size,
drop_last=True,
rank=rank,
world_size=world_size)
# Torch dataloader.
return torch.utils.data.DataLoader(dataset,
batch_sampler=batch_sampler,
num_workers=num_workers,
pin_memory=True)
# NOTE: Here's where you can set hyperparameters for PPO. I don't include them as part of
# ArgumentParser because it's too annoying to type them every time at command line. Instead, you can change them here.
# To see a list of hyperparameters, look in ppo.py at function _init_hyperparameters
hyperparameters = {
'timesteps_per_batch': 2048,
'max_timesteps_per_episode': 200,
'gamma': 0.99,
'n_updates_per_iteration': 10,
'lr': 3e-4,
'clip': 0.2
}
# Creates the environment we'll be running. If you want to replace with your own
# custom environment, note that it must inherit Gym and have both continuous
# observation and action spaces.
env = gym.make('Pendulum-v0')
# Train or test, depending on the mode specified
if args.mode == 'train':
train(env=env,
hyperparameters=hyperparameters,
actor_model=args.actor_model,
critic_model=args.critic_model)
else:
test(env=env, actor_model=args.actor_model)
if __name__ == '__main__':
args = get_args() # Parse arguments from command line
main(args)
def main():
"""Main training program."""
print('Pretrain BERT model')
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Arguments.
args = get_args()
# Pytorch distributed.
initialize_distributed(args)
set_random_seed(args.seed)
print(args)
# Data stuff.
data_config = configure_data()
data_config.set_defaults(data_set_type='BERT', transpose=False)
(train_data, val_data), tokenizer = data_config.apply(args)
args.train_iters = len(train_data)
evaluate.best_val_loss = float("inf")
# Model, optimizer, and learning rate.
model, optimizer, lr_scheduler, criterion = setup_model_and_optimizer(
args, tokenizer)
# evaluate(val_data, model, tokenizer, criterion, args)
# At any point you can hit Ctrl + C to break out of training early.
try:
total_iters = 0
skipped_iters = 0
start_epoch = 1
best_val_loss = float('inf')
# Resume data loader if necessary.
if args.resume_dataloader:
start_epoch = args.epoch
total_iters = args.total_iters
# For all epochs.
for epoch in range(start_epoch, args.epochs + 1):
timers = Timers()
# if args.shuffle:
# train_data.batch_sampler.sampler.set_epoch(epoch + args.seed)
timers('epoch time').start()
iteration, skipped = train_epoch(epoch, model, tokenizer,
optimizer, train_data, val_data,
lr_scheduler, criterion, timers,
args)
elapsed_time = timers('epoch time').elapsed()
total_iters += iteration
skipped_iters += skipped
lm_loss, nsp_loss = evaluate(val_data, model, tokenizer, criterion,
args)
val_loss = lm_loss + nsp_loss
print('-' * 100)
print(
'| end of epoch {:3d} | time: {:.3f}s | valid loss {:.3f} | '
'valid LM Loss {:.3f} | valid LM PPL {:.3f} | valid NSP Loss {:.3f}'
.format(epoch, elapsed_time, val_loss, lm_loss,
math.exp(lm_loss), nsp_loss))
print('-' * 100)
if val_loss < evaluate.best_val_loss:
evaluate.best_val_loss = val_loss
if args.save:
best_path = 'checkpoints-best.pt'
print('saving best model to:',
os.path.join(args.save, best_path))
save_checkpoint(best_path, epoch + 1, 0, model, optimizer,
lr_scheduler, args)
except KeyboardInterrupt:
print('-' * 100)
print('Exiting from training early')
if args.save:
cur_path = 'checkpoints-last.pt'
print('saving current model to:',
os.path.join(args.save, cur_path))
save_checkpoint(cur_path, epoch, args.cur_iteration, model,
optimizer, lr_scheduler, args)
exit()
import torch
from torch.utils.tensorboard import SummaryWriter
import numpy as np
from preprocess import * #Preprocess, ChampDataset
from model import * #CNNModel, MUCNNModel, RNNModel, MURNNModel, OHModel, MUOHModel
from arguments import get_args
import train
from datetime import datetime
now = datetime.now()
import os
import pdb
preprocess = Preprocess()
arg = get_args()
'''
def oneHotEncodding(labels):
onehot_encoded = list()
for value in labels:
target = [0 for _ in range(2)]
target[value] = 1
onehot_encoded.append(target)
return onehot_encoded
'''
def main():
allChamp, matchComp, blueWin = preprocess.lolDataSet(arg, "train")
_, test_x, test_y = preprocess.lolDataSet(arg, "test")
def visualize_attention_metric_for_multiple_models(
name_prefix_and_kwargs: List[Tuple[str, Dict]],
unit_width_per_name=3,
extension="png"):
res = None
total_args, num_layers, custom_key_list, name_prefix_list = None, None, [], []
kld1_list, kld2_list, jsd_list, ent_list = [], [], [], [] # [L * M, N]
for name_prefix, kwargs in name_prefix_and_kwargs:
args = get_args(**kwargs)
custom_key_list.append(args.custom_key)
num_layers = args.num_layers
train_d, val_d, test_d = get_dataset_or_loader(
args.dataset_class,
args.dataset_name,
args.data_root,
batch_size=args.batch_size,
seed=args.seed,
)
if val_d is None and test_d is None:
data_list = [train_d[0]]
else:
data_list = []
for _data in chain(train_d, val_d, test_d):
if _data.x.size(0) != len(_data.agreement_dist):
_data.agreement_dist = [
_ad for _ad in _data.agreement_dist[0]
]
_data.uniform_att_dist = [
_uad for _uad in _data.uniform_att_dist[0]
]
data_list.append(_data)
gpu_id = [
int(
np.random.choice([
g for g in range(args.num_gpus_total)
if g not in args.gpu_deny_list
], 1))
][0]
if args.verbose >= 1:
pprint_args(args)
cprint("Use GPU the ID of which is {}".format(gpu_id), "yellow")
device = "cpu" if gpu_id is None \
else torch.device('cuda:{}'.format(gpu_id) if torch.cuda.is_available() else 'cpu')
model, ret = run(args, gpu_id=gpu_id, return_model=True)
kld1_layer, kld2_layer, jsd_layer, ent_layer, *res = \
get_attention_metric_for_single_model_and_multiple_data(model, data_list, device)
kld1_list += kld1_layer
kld2_list += kld2_layer
jsd_list += jsd_layer
ent_list += ent_layer
name_prefix_list.append(name_prefix)
total_args = args
torch.cuda.empty_cache()
total_args.custom_key = "-".join(sorted(custom_key_list))
plot_kld_jsd_ent(kld1_list,
kld2_list,
jsd_list,
ent_list,
*res,
num_layers=num_layers,
model_args=total_args,
epoch=-1,
name_prefix_list=name_prefix_list,
unit_width_per_name=unit_width_per_name,
extension=extension,
flierprops={
"marker": "x",
"markersize": 12
})
def eval_networks():
# get args
args = get_args()
seed = set_seed(args.seed, args.use_cuda)
_, testset, nr_channels, mlp_input_neurons, classes = get_dataset(args)
testloader = torch.utils.data.DataLoader(testset,
batch_size=5,
shuffle=False,
num_workers=1)
# get student and teacher models
student_model_class = get_model_class(args.student_model)
teacher_model_class = get_model_class(args.teacher_model)
if "MLP" in args.student_model:
stud_model_simple = student_model_class(mlp_input_neurons, 10,
args.dropout)
stud_model_teacher = student_model_class(mlp_input_neurons, 10,
args.dropout)
teacher_model = teacher_model_class(mlp_input_neurons, 10,
args.dropout)
else:
stud_model_simple = student_model_class(nr_channels, 10, args.dropout)
stud_model_teacher = student_model_class(nr_channels, 10, args.dropout)
teacher_model = teacher_model_class(nr_channels, 10, args.dropout)
if torch.cuda.is_available() and args.use_cuda:
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
with open(
args.dataset + "_teacher_network_" + args.teacher_model + "_" +
str(seed), "rb") as f:
teacher_model.load_state_dict(torch.load(f))
with open(
args.dataset + "_student_network_simple" + args.student_model +
str(seed) + "_10", "rb") as f:
stud_model_simple.load_state_dict(torch.load(f))
with open(
args.dataset + "_student_network_teacher" + args.student_model +
str(seed) + "_10", "rb") as f:
stud_model_teacher.load_state_dict(torch.load(f))
stud_model_simple.to(device)
stud_model_teacher.to(device)
teacher_model.to(device)
stud_model_simple.eval()
stud_model_teacher.eval()
teacher_model.eval()
print("Eval teacher model")
show_results(testloader, teacher_model, classes, use_cuda=True)
print("Eval student model simple")
#show_results(testloader, stud_model_simple, classes, use_cuda=True)
print("Eval student model twacher")
def analyze_rpg_by_degree_and_homophily(degree_list: List[float],
homophily_list: List[float],
legend_list: List[str],
model_list: List[str],
custom_key_list: List[str],
att_lambda_list: List[float],
l2_lambda_list: List[float],
num_total_runs: int,
num_nodes_per_class: int = 500,
num_classes: int = 10,
verbose=2,
is_test=False,
plot_part_by_part=False,
draw_plot=True,
draw_diff_between_first=False,
extension="pdf"):
def to_log10(v, eps=1e-5):
return float(np.log10(v + eps))
base_key = "analysis_rpg" + ("" if not is_test else "_test")
base_path = os.path.join("../figs", base_key)
best_meta_dict = defaultdict(dict)
deg_and_legend_to_mean_over_hp_list, deg_and_legend_to_std_over_hp_list = {}, {}
for deg in degree_list:
avg_deg_ratio = deg / num_nodes_per_class
for legend, model, key in zip(legend_list, model_list,
custom_key_list):
base_kwargs = {
"model_name": model,
"dataset_class": "RandomPartitionGraph",
"dataset_name": f"rpg-{num_classes}-{num_nodes_per_class}-h-d",
"custom_key": key,
}
args = get_args(**base_kwargs)
args.verbose = verbose
deg_and_legend = (deg, legend)
if is_test:
args.epochs = 2
mean_over_hp_list, std_over_hp_list = [], []
for hp in homophily_list:
args.dataset_name = f"rpg-{num_classes}-{num_nodes_per_class}-{hp}-{avg_deg_ratio}"
model_key, model_path = _get_key_and_makedirs(
args=args, base_path=base_path, args_prefix=legend)
max_mean_perf = -1
for att_lambda in att_lambda_list:
for l2_lambda in l2_lambda_list:
args.att_lambda = att_lambda
args.l2_lambda = l2_lambda
pprint_args(args)
result_key = (att_lambda, l2_lambda)
result_path = os.path.join(
model_path,
"ms_result_{}.pkl".format(s_join("-", result_key)))
try:
many_seeds_result = pickle.load(
open(result_path, "rb"))
cprint("Load: {}".format(result_path), "blue")
except FileNotFoundError:
many_seeds_result = run_with_many_seeds_with_gpu(
args, num_total_runs)
with open(result_path, "wb") as f:
pickle.dump(many_seeds_result, f)
cprint("Dump: {}".format(result_path), "green")
garbage_collection_cuda()
cprint("Garbage collected", "green")
cur_mean_perf = float(
np.mean(
many_seeds_result["test_perf_at_best_val"]))
cur_std_perf = float(
np.std(many_seeds_result["test_perf_at_best_val"]))
if cur_mean_perf > max_mean_perf:
max_mean_perf = cur_mean_perf
best_meta_dict[model_key][
"mean_perf"] = cur_mean_perf
best_meta_dict[model_key][
"std_perf"] = cur_std_perf
best_meta_dict[model_key][
"att_lambda"] = att_lambda
best_meta_dict[model_key]["l2_lambda"] = l2_lambda
best_meta_dict[model_key][
"many_seeds_result"] = many_seeds_result
if not args.is_super_gat:
break
mean_over_hp_list.append(
best_meta_dict[model_key]["mean_perf"])
std_over_hp_list.append(best_meta_dict[model_key]["std_perf"])
deg_and_legend_to_mean_over_hp_list[
deg_and_legend] = mean_over_hp_list
deg_and_legend_to_std_over_hp_list[
deg_and_legend] = std_over_hp_list
pprint(deg_and_legend_to_mean_over_hp_list)
if not draw_plot:
return
plot_line_with_std(
tuple_to_mean_list=
deg_and_legend_to_mean_over_hp_list, # (deg, legend) -> List[perf] by homophily
tuple_to_std_list=deg_and_legend_to_std_over_hp_list,
x_label="Homophily",
y_label="Test Accuracy",
name_label_list=["Avg. Degree", "Model"],
x_list=homophily_list,
hue="Model",
style="Model",
col="Avg. Degree",
hue_order=legend_list,
x_lim=(0, None),
custom_key=base_key,
extension=extension,
)
hp_and_legend_to_mean_over_deg_list, hp_and_legend_to_std_over_deg_list = defaultdict(
list), defaultdict(list)
legend_to_mean_std_num_agreed_neighbors_list = defaultdict(list)
for deg, legend in deg_and_legend_to_mean_over_hp_list.keys():
mean_over_hp_list = deg_and_legend_to_mean_over_hp_list[(deg, legend)]
std_over_hp_list = deg_and_legend_to_std_over_hp_list[(deg, legend)]
for hp, mean_of_hp, std_of_hp in zip(homophily_list, mean_over_hp_list,
std_over_hp_list):
hp_and_legend = (hp, legend)
hp_and_legend_to_mean_over_deg_list[hp_and_legend].append(
mean_of_hp)
hp_and_legend_to_std_over_deg_list[hp_and_legend].append(std_of_hp)
legend_to_mean_std_num_agreed_neighbors_list[legend].append(
(mean_of_hp, std_of_hp, hp * deg))
mean_perf_list = []
num_agreed_neighbors_list = []
model_legend_list = []
for legend, mean_std_num_agr_neighbors_list in legend_to_mean_std_num_agreed_neighbors_list.items(
):
for mean_perf, std_perf, num_agr_neighbors in sorted(
mean_std_num_agr_neighbors_list, key=lambda t: t[2]):
mean_perf_list.append(mean_perf)
model_legend_list.append(legend)
num_agreed_neighbors_list.append(num_agr_neighbors)
plot_scatter(
xs=num_agreed_neighbors_list,
ys=mean_perf_list,
hues=model_legend_list,
xlabel="Avg. Number of Agreed Neighbors",
ylabel="Test Performance (Acc.)",
hue_name="Model",
custom_key=base_key,
)
plot_line_with_std(
tuple_to_mean_list=hp_and_legend_to_mean_over_deg_list,
tuple_to_std_list=hp_and_legend_to_std_over_deg_list,
x_label="Avg. Degree (Log10)", # Log
y_label="Test Accuracy",
name_label_list=["Homophily", "Model"],
x_list=[to_log10(d) for d in degree_list], # Log
hue="Model",
style="Model",
col="Homophily",
aspect=0.75,
hue_order=legend_list,
x_lim=(None, None),
custom_key=base_key,
extension=extension,
)
if plot_part_by_part: # manual.
# deg: [2.5, 5.0, 10.0, 25.0, 50.0, 75.0, 100.0]
def filtered_by_hp(hp_list, num_deg=None):
return ({
(hp, legend):
(mean_list if not num_deg else mean_list[:num_deg])
for (hp, legend), mean_list in
hp_and_legend_to_mean_over_deg_list.items() if hp in hp_list
}, {(hp, legend): (std_list if not num_deg else std_list[:num_deg])
for (hp, legend
), std_list in hp_and_legend_to_std_over_deg_list.items()
if hp in hp_list})
def get_mean_diff(h_and_l_to_m_over_d_list, first_legend, x100=True):
h_and_l_to_mean_diff_over_d_list = dict()
for (hp, legend), mean_list in h_and_l_to_m_over_d_list.items():
if legend == first_legend:
continue
mean_list_of_first = h_and_l_to_m_over_d_list[(hp,
first_legend)]
mean_diff_list = (np.asarray(mean_list) -
np.asarray(mean_list_of_first))
if x100:
mean_diff_list = 100 * mean_diff_list
mean_diff_list = mean_diff_list.tolist()
h_and_l_to_mean_diff_over_d_list[(hp, legend)] = mean_diff_list
return h_and_l_to_mean_diff_over_d_list
if 0.1 in degree_list:
b1, b2, b3, b4 = [0.1, 0.3, 0.5], [0.7], [0.9], [0.7, 0.9]
else:
b1, b2, b3, b4 = [0.2, 0.4], [0.6], [0.8], [0.6, 0.8]
hp135_and_legend_to_mean_over_deg_list, hp135_and_legend_to_std_over_deg_list = filtered_by_hp(
b1)
hp7_and_legend_to_mean_over_deg_list, hp7_and_legend_to_std_over_deg_list = filtered_by_hp(
b2)
hp9_and_legend_to_mean_over_deg_list, hp9_and_legend_to_std_over_deg_list = filtered_by_hp(
b3)
hp79_and_legend_to_mean_over_deg_list, hp79_and_legend_to_std_over_deg_list = filtered_by_hp(
b4)
if draw_diff_between_first:
lf = legend_list[0]
hp135_and_legend_to_mean_over_deg_list = get_mean_diff(
hp135_and_legend_to_mean_over_deg_list, lf)
hp7_and_legend_to_mean_over_deg_list = get_mean_diff(
hp7_and_legend_to_mean_over_deg_list, lf)
hp79_and_legend_to_mean_over_deg_list = get_mean_diff(
hp79_and_legend_to_mean_over_deg_list, lf)
hp9_and_legend_to_mean_over_deg_list = get_mean_diff(
hp9_and_legend_to_mean_over_deg_list, lf)
hp135_and_legend_to_std_over_deg_list = None
hp7_and_legend_to_std_over_deg_list = None
hp79_and_legend_to_std_over_deg_list = None
hp9_and_legend_to_std_over_deg_list = None
legend_list = legend_list[1:]
y_lim = None
y_label = "Diff. of Test Acc. vs. GO (%p)"
else:
y_lim = None
y_label = "Test Accuracy",
degree_list = np.log10(degree_list).tolist()
palette = ["grey", "#1976D2", "#D32F2F"]
plot_line_with_std(
tuple_to_mean_list=hp135_and_legend_to_mean_over_deg_list,
tuple_to_std_list=hp135_and_legend_to_std_over_deg_list,
x_label="Avg. Degree (Log10)",
y_label=y_label,
name_label_list=["Homophily", "Model"],
x_list=degree_list,
hue="Model",
style="Model",
col="Homophily",
aspect=0.9,
hue_order=legend_list,
legend=False,
x_lim=(0, None),
y_lim=y_lim,
palette=palette,
custom_key=base_key + "_part135",
extension=extension,
)
plot_line_with_std(
tuple_to_mean_list=hp79_and_legend_to_mean_over_deg_list,
tuple_to_std_list=hp79_and_legend_to_std_over_deg_list,
x_label="Avg. Degree (Log10)",
y_label="Test Accuracy",
name_label_list=["Homophily", "Model"],
x_list=degree_list,
hue="Model",
style="Model",
col="Homophily",
aspect=0.9,
hue_order=legend_list,
legend="full",
x_lim=(0, None),
y_lim=y_lim,
use_ylabel=False,
palette=palette,
custom_key=base_key + "_part79",
extension=extension,
)
plot_line_with_std(
tuple_to_mean_list=hp7_and_legend_to_mean_over_deg_list,
tuple_to_std_list=hp7_and_legend_to_std_over_deg_list,
x_label="Avg. Degree (Log10)",
y_label="Test Accuracy",
name_label_list=["Homophily", "Model"],
x_list=degree_list,
hue="Model",
style="Model",
col="Homophily",
aspect=1.0,
hue_order=legend_list,
legend=False,
x_lim=(0, None),
y_lim=y_lim,
use_ylabel=False,
palette=palette,
custom_key=base_key + "_part7",
extension=extension,
)
plot_line_with_std(
tuple_to_mean_list=hp9_and_legend_to_mean_over_deg_list,
tuple_to_std_list=hp9_and_legend_to_std_over_deg_list,
x_label="Avg. Degree (Log10)",
y_label="Test Accuracy",
name_label_list=["Homophily", "Model"],
x_list=degree_list,
hue="Model",
style="Model",
col="Homophily",
aspect=1.0,
hue_order=legend_list,
legend="full",
x_lim=(0, None),
y_lim=y_lim,
use_ylabel=False,
palette=palette,
custom_key=base_key + "_part9",
extension=extension,
)
def main():
args = get_args()
device = torch.device('cuda' if args.cuda else 'cpu')
env = gym.make(args.env_name)
input_size = env.observation_space.shape # 4
output_size = env.action_space.n # 2
if 'Breakout' in args.env_name:
output_size -= 1
env.close()
is_render = False
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
model_path = os.path.join(args.save_dir, args.env_name + '.model')
predictor_path = os.path.join(args.save_dir, args.env_name + '.pred')
target_path = os.path.join(args.save_dir, args.env_name + '.target')
writer = SummaryWriter(log_dir=args.log_dir)
reward_rms = RunningMeanStd()
obs_rms = RunningMeanStd(shape=(1, 1, 84, 84))
discounted_reward = RewardForwardFilter(args.ext_gamma)
model = CnnActorCriticNetwork(input_size, output_size, args.use_noisy_net)
rnd = RNDModel(input_size, output_size)
model = model.to(device)
rnd = rnd.to(device)
optimizer = optim.Adam(list(model.parameters()) + list(rnd.predictor.parameters()), lr=args.lr)
if args.load_model:
if args.cuda:
model.load_state_dict(torch.load(model_path))
else:
model.load_state_dict(torch.load(model_path, map_location='cpu'))
works = []
parent_conns = []
child_conns = []
for idx in range(args.num_worker):
parent_conn, child_conn = Pipe()
work = AtariEnvironment(
args.env_name,
is_render,
idx,
child_conn,
sticky_action=args.sticky_action,
p=args.sticky_action_prob,
max_episode_steps=args.max_episode_steps)
work.start()
works.append(work)
parent_conns.append(parent_conn)
child_conns.append(child_conn)
states = np.zeros([args.num_worker, 4, 84, 84])
sample_env_index = 0 # Sample Environment index to log
sample_episode = 0
sample_rall = 0
sample_step = 0
sample_i_rall = 0
global_update = 0
global_step = 0
# normalize observation
print('Initializes observation normalization...')
next_obs = []
for step in range(args.num_step * args.pre_obs_norm_steps):
actions = np.random.randint(0, output_size, size=(args.num_worker,))
for parent_conn, action in zip(parent_conns, actions):
parent_conn.send(action)
for parent_conn in parent_conns:
next_state, reward, done, realdone, log_reward = parent_conn.recv()
next_obs.append(next_state[3, :, :].reshape([1, 84, 84]))
if len(next_obs) % (args.num_step * args.num_worker) == 0:
next_obs = np.stack(next_obs)
obs_rms.update(next_obs)
next_obs = []
print('Training...')
while True:
total_state, total_reward, total_done, total_next_state, total_action, total_int_reward, total_next_obs, total_ext_values, total_int_values, total_action_probs = [], [], [], [], [], [], [], [], [], []
global_step += (args.num_worker * args.num_step)
global_update += 1
# Step 1. n-step rollout
for _ in range(args.num_step):
actions, value_ext, value_int, action_probs = get_action(model, device, np.float32(states) / 255.)
for parent_conn, action in zip(parent_conns, actions):
parent_conn.send(action)
next_states, rewards, dones, real_dones, log_rewards, next_obs = [], [], [], [], [], []
for parent_conn in parent_conns:
next_state, reward, done, real_done, log_reward = parent_conn.recv()
next_states.append(next_state)
rewards.append(reward)
dones.append(done)
real_dones.append(real_done)
log_rewards.append(log_reward)
next_obs.append(next_state[3, :, :].reshape([1, 84, 84]))
next_states = np.stack(next_states)
rewards = np.hstack(rewards)
dones = np.hstack(dones)
real_dones = np.hstack(real_dones)
next_obs = np.stack(next_obs)
# total reward = int reward + ext Reward
intrinsic_reward = compute_intrinsic_reward(rnd, device,
((next_obs - obs_rms.mean) / np.sqrt(obs_rms.var)).clip(-5, 5))
intrinsic_reward = np.hstack(intrinsic_reward)
sample_i_rall += intrinsic_reward[sample_env_index]
total_next_obs.append(next_obs)
total_int_reward.append(intrinsic_reward)
total_state.append(states)
total_reward.append(rewards)
total_done.append(dones)
total_action.append(actions)
total_ext_values.append(value_ext)
total_int_values.append(value_int)
total_action_probs.append(action_probs)
states = next_states[:, :, :, :]
sample_rall += log_rewards[sample_env_index]
sample_step += 1
if real_dones[sample_env_index]:
sample_episode += 1
writer.add_scalar('data/reward_per_epi', sample_rall, sample_episode)
writer.add_scalar('data/reward_per_rollout', sample_rall, global_update)
writer.add_scalar('data/step', sample_step, sample_episode)
sample_rall = 0
sample_step = 0
sample_i_rall = 0
# calculate last next value
_, value_ext, value_int, _ = get_action(model, device, np.float32(states) / 255.)
total_ext_values.append(value_ext)
total_int_values.append(value_int)
# --------------------------------------------------
total_state = np.stack(total_state).transpose([1, 0, 2, 3, 4]).reshape([-1, 4, 84, 84])
total_reward = np.stack(total_reward).transpose().clip(-1, 1)
total_action = np.stack(total_action).transpose().reshape([-1])
total_done = np.stack(total_done).transpose()
total_next_obs = np.stack(total_next_obs).transpose([1, 0, 2, 3, 4]).reshape([-1, 1, 84, 84])
total_ext_values = np.stack(total_ext_values).transpose()
total_int_values = np.stack(total_int_values).transpose()
total_logging_action_probs = np.vstack(total_action_probs)
# Step 2. calculate intrinsic reward
# running mean intrinsic reward
total_int_reward = np.stack(total_int_reward).transpose()
total_reward_per_env = np.array([discounted_reward.update(reward_per_step) for reward_per_step in total_int_reward.T])
mean, std, count = np.mean(total_reward_per_env), np.std(total_reward_per_env), len(total_reward_per_env)
reward_rms.update_from_moments(mean, std ** 2, count)
# normalize intrinsic reward
total_int_reward /= np.sqrt(reward_rms.var)
writer.add_scalar('data/int_reward_per_epi', np.sum(total_int_reward) / args.num_worker, sample_episode)
writer.add_scalar('data/int_reward_per_rollout', np.sum(total_int_reward) / args.num_worker, global_update)
# -------------------------------------------------------------------------------------------
# logging Max action probability
writer.add_scalar('data/max_prob', total_logging_action_probs.max(1).mean(), sample_episode)
# Step 3. make target and advantage
# extrinsic reward calculate
ext_target, ext_adv = make_train_data(total_reward,
total_done,
total_ext_values,
args.ext_gamma,
args.gae_lambda,
args.num_step,
args.num_worker,
args.use_gae)
# intrinsic reward calculate
# None Episodic
int_target, int_adv = make_train_data(total_int_reward,
np.zeros_like(total_int_reward),
total_int_values,
args.int_gamma,
args.gae_lambda,
args.num_step,
args.num_worker,
args.use_gae)
# add ext adv and int adv
total_adv = int_adv * args.int_coef + ext_adv * args.ext_coef
# -----------------------------------------------
# Step 4. update obs normalize param
obs_rms.update(total_next_obs)
# -----------------------------------------------
# Step 5. Training!
train_model(args, device, output_size, model, rnd, optimizer,
np.float32(total_state) / 255., ext_target, int_target, total_action,
total_adv, ((total_next_obs - obs_rms.mean) / np.sqrt(obs_rms.var)).clip(-5, 5),
total_action_probs)
if global_step % (args.num_worker * args.num_step * args.save_interval) == 0:
print('Now Global Step :{}'.format(global_step))
torch.save(model.state_dict(), model_path)
torch.save(rnd.predictor.state_dict(), predictor_path)
torch.save(rnd.target.state_dict(), target_path)
def main():
"""Main training program."""
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Timer.
timers = Timers()
# Arguments.
args = get_args()
# Pytorch distributed.
initialize_distributed(args)
# Random seeds for reproducability.
set_random_seed(args.seed)
# get the tokenizer
tokenizer = GPT2Tokenizer(
os.path.join(args.tokenizer_path, 'vocab.json'),
os.path.join(args.tokenizer_path, 'chinese_vocab.model'))
# load train data
if args.do_train:
train_dataloader, _ = load_data(args, 'train', tokenizer, 1)
dev_dataloader, dev_dataset = load_data(args, 'dev', tokenizer, 1)
with open(args.deepspeed_config, "r") as f:
deepspeed_conf = json.load(f)
epoch = args.epoch
grad_acc = deepspeed_conf["gradient_accumulation_steps"]
args.train_iters = len(train_dataloader) * epoch / grad_acc
# Model, optimizer, and learning rate.
# TODO: maybe need to reinitialize optimizer
elif args.do_eval:
# Set an arbitrary positive integer since the optimizer and the scheduler will not be used when do eval.
args.train_iters = 1
model, optimizer, lr_scheduler = setup_model_and_optimizer(args)
device = torch.cuda.current_device()
# give a time stemp to the model
cur_time = time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime())
results_dir = os.path.join(args.results_dir,
"{}-{}".format(args.model_name, cur_time))
os.makedirs(results_dir, exist_ok=True)
if args.do_train and torch.distributed.get_rank() == 0:
with open(os.path.join(results_dir, "train_log.txt"), "w") as f:
f.write("Train losses:\n")
with open(os.path.join(results_dir, "dev_log.txt"), "w") as f:
f.write("Dev accs:\n")
torch.distributed.barrier()
if args.do_train:
cand_ids = torch.tensor(dev_dataset.cand_ids).to(device)
total_loss, logging_loss, best_acc = 0.0, 0.0, 0.0
global_step, total_step, best_step = 0, 0, 0
for e in range(epoch):
model.train()
for batch, no_model_batch in tqdm(
train_dataloader,
disable=(torch.distributed.get_rank() != 0)):
for k in batch:
batch[k] = batch[k].to(device)
for k in no_model_batch:
no_model_batch[k] = no_model_batch[k].to(device)
output = model(**batch)
# get the loss of the last token
output = torch.sum(
output * no_model_batch["loss_mask"].unsqueeze(-1),
1) / torch.sum(no_model_batch["loss_mask"],
-1).unsqueeze(-1)
# get the label of the last token
labels = no_model_batch["labels"].float()
labels = (torch.sum(labels * no_model_batch["loss_mask"], 1) /
torch.sum(no_model_batch["loss_mask"], -1)).long()
# cross_entropy loss
losses = mpu.vocab_parallel_cross_entropy(
output.unsqueeze(1).contiguous().float(),
labels.unsqueeze(1))
loss = torch.mean(losses)
model.backward(loss)
model.step()
torch.distributed.all_reduce(
loss.data, group=mpu.get_data_parallel_group())
loss.data = loss.data / mpu.get_data_parallel_world_size()
total_loss += loss.item() / grad_acc
if total_step % grad_acc == 0:
global_step += 1
if global_step != 0 and global_step % args.log_interval == 0:
# logging
if torch.distributed.get_rank() == 0:
train_log = "Epoch {}, global step {}, total step {}, train lm loss: {}".format(
e, global_step, epoch * len(train_dataloader),
(total_loss - logging_loss) /
args.log_interval)
yprint(train_log)
with open(
os.path.join(results_dir, "train_log.txt"),
"a") as f:
f.write(train_log + "\n")
logging_loss = total_loss
if global_step != 0 and global_step % args.eval_interval == 0:
# evaluate on the dev
acc, _, _ = evaluate(args,
model,
dev_dataloader,
cand_ids,
device,
mode="dev")
dev_results_dir = os.path.join(
results_dir, "dev_step-{}".format(global_step))
if acc > best_acc:
best_acc = acc
best_step = global_step
if torch.distributed.get_rank() == 0:
# we will only write the log file once
dev_log = "Epoch: {}, Global step: {}, Acc: {}".format(
e, global_step, acc)
yprint(dev_log)
os.makedirs(dev_results_dir, exist_ok=True)
with open(
os.path.join(dev_results_dir,
"dev_result.txt"), "w") as f:
f.write(dev_log + "\n")
with open(os.path.join(results_dir, "dev_log.txt"),
"a") as f:
f.write(dev_log + "\n")
torch.distributed.barrier()
args.save = dev_results_dir
save_checkpoint(global_step, model, optimizer,
lr_scheduler, args)
total_step += 1
with open(os.path.join(dev_results_dir, "dev_log.txt"), "a") as f:
f.write("Best acc: {} Best step: {}\n".format(best_acc, best_step))
if args.do_eval:
# evaluate on the test
test_dataloader, test_dataset = load_data(args, 'test', tokenizer, 1)
cand_ids = torch.tensor(test_dataset.cand_ids).to(device)
if args.do_train:
# if do training, then evaluate the one with the max acc on dev set.
eval_ckpt_path = os.path.join(results_dir,
"dev_step-{}".format(best_step))
args.load = eval_ckpt_path
else:
# if only do eval, then evaluate the one specified by the user.
args.load = args.eval_ckpt_path
load_checkpoint(model=model,
optimizer=None,
lr_scheduler=None,
args=args)
acc, _, _ = evaluate(args,
model,
test_dataloader,
cand_ids,
device,
mode="test")
if torch.distributed.get_rank() == 0:
eval_log = "Checkpoint from {}: Acc: {}".format(args.load, acc)
yprint(eval_log)
with open(os.path.join(results_dir, "eval_log"), "w") as f:
f.write(eval_log + "\n")
torch.distributed.barrier()
if opts.expert_trajectories:
save_state['T_sup'] = agent.T_sup
torch.save(save_state, os.path.join(opts.save_path, 'model_latest.net'))
print('Epoch %d : Train loss: %9.6f Val loss: %9.6f'%(epoch+1, train_err, val_err))
# Reduce supervision gradually
if opts.expert_trajectories and opts.hybrid_train:
if (epoch+1) % opts.hybrid_schedule == 0 and agent.T_sup > 0:
agent.T_sup -= 1
# Save the model after the first schedule is over
if epoch+1 == opts.hybrid_schedule:
torch.save(save_state, os.path.join(opts.save_path, 'model_after_one_schedule.net'))
# Decay expert reward gradually
if opts.expert_rewards and (epoch+1) % opts.expert_rewards_decay == 0:
agent.reward_scale_expert /= opts.expert_rewards_decay_factor
# Display three randomly selected batches of panoramas every 10 epochs
if (epoch+1) % 10 == 0 or epoch == 0:
for choice in rng_choices:
for pano_count in range(decoded_images[choice].size(0)):
x = vutils.make_grid(decoded_images[choice][pano_count], padding=5, normalize=True, scale_each=True, nrow=opts.T//2+1)
writer.add_image('Validation batch # : %d image # : %d'%(choice, pano_count), x, 0) # Converting this to 0 to save disk space, should be epoch ideally
if __name__ == '__main__':
opts = get_args()
assert not(opts.expert_rewards and opts.expert_trajectories), "Cannot use both sidekicks at once!"
train(opts)
import numpy as np
from autooed.problem import build_problem
from autooed.mobo import build_algorithm
from autooed.utils.seed import set_seed
from autooed.utils.initialization import generate_random_initial_samples
from autooed.utils.plot import plot_performance_space, plot_performance_metric
from arguments import get_args
if __name__ == '__main__':
# load arguments
args, module_cfg = get_args()
# set random seed
set_seed(args.seed)
# build problem
problem = build_problem(args.problem)
print(problem)
# build algorithm
algorithm = build_algorithm(args.algo, problem, module_cfg)
print(algorithm)
# generate initial random samples
X = generate_random_initial_samples(problem, args.n_init_sample)
Y = np.array([problem.evaluate_objective(x) for x in X])
def main():
import copy
import glob
import os
import time
import matplotlib.pyplot as plt
import gym
import numpy as np
import torch
torch.multiprocessing.set_start_method('spawn')
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from gym.spaces import Discrete
from arguments import get_args
from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
from baselines.common.vec_env.vec_normalize import VecNormalize
from envs import make_env
from img_env import ImgEnv, IMG_ENVS
from model import Policy
from storage import RolloutStorage
from utils import update_current_obs, eval_episode
from torchvision import transforms
from visdom import Visdom
import algo
viz = Visdom(port=8097)
print("#######")
print(
"WARNING: All rewards are clipped or normalized so you need to use a monitor (see envs.py) or visdom plot to get true rewards"
)
print("#######")
plot_rewards = []
plot_policy_loss = []
plot_value_loss = []
# x = np.array([0])
# y = np.array([0])
# counter = 0
# win = viz.line(
# X=x,
# Y=y,
# win="test1",
# name='Line1',
# opts=dict(
# title='Reward',
# )
# )
# win2 = viz.line(
# X=x,
# Y=y,
# win="test2",
# name='Line2',
# opts=dict(
# title='Policy Loss',
# )
# )
# win3 = viz.line(
# X=x,
# Y=y,
# win="test3",
# name='Line3',
# opts=dict(
# title='Value Loss',
# )
# )
args = get_args()
if args.no_cuda:
args.cuda = False
print(args)
assert args.algo in ['a2c', 'ppo', 'acktr']
if args.recurrent_policy:
assert args.algo in ['a2c', 'ppo'], \
'Recurrent policy is not implemented for ACKTR'
num_updates = int(args.num_frames) // args.num_steps // args.num_processes
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
toprint = ['seed', 'lr', 'nat', 'resnet']
if args.env_name in IMG_ENVS:
toprint += ['window', 'max_steps']
toprint.sort()
name = args.tag
args_param = vars(args)
os.makedirs(os.path.join(args.out_dir, args.env_name), exist_ok=True)
for arg in toprint:
if arg in args_param and (args_param[arg] or arg in ['gamma', 'seed']):
if args_param[arg] is True:
name += '{}_'.format(arg)
else:
name += '{}{}_'.format(arg, args_param[arg])
model_dir = os.path.join(args.out_dir, args.env_name, args.algo)
os.makedirs(model_dir, exist_ok=True)
results_dict = {'episodes': [], 'rewards': [], 'args': args}
torch.set_num_threads(1)
eval_env = make_env(args,
'cifar10',
args.seed,
1,
None,
args.add_timestep,
natural=args.nat,
train=False)
envs = make_env(args,
'cifar10',
args.seed,
1,
None,
args.add_timestep,
natural=args.nat,
train=True)
#print(envs)
# envs = envs[0]
# if args.num_processes > 1:
# envs = SubprocVecEnv(envs)
# else:
# envs = DummyVecEnv(envs)
# eval_env = DummyVecEnv(eval_env)
# if len(envs.observation_space.shape) == 1:
# envs = VecNormalize(envs, gamma=args.gamma)
obs_shape = envs.observation_space.shape
obs_shape = (obs_shape[0] * args.num_stack, *obs_shape[1:])
actor_critic = Policy(obs_shape,
envs.action_space,
args.recurrent_policy,
dataset=args.env_name,
resnet=args.resnet,
pretrained=args.pretrained)
if envs.action_space.__class__.__name__ == "Discrete":
action_shape = 1
else:
action_shape = envs.action_space.shape[0]
if args.cuda:
actor_critic.cuda()
if args.algo == 'a2c':
agent = algo.A2C_ACKTR(actor_critic,
args.value_loss_coef,
args.entropy_coef,
lr=args.lr,
eps=args.eps,
alpha=args.alpha,
max_grad_norm=args.max_grad_norm)
elif args.algo == 'ppo':
agent = algo.PPO(actor_critic,
args.clip_param,
args.ppo_epoch,
args.num_mini_batch,
args.value_loss_coef,
args.entropy_coef,
lr=args.lr,
eps=args.eps,
max_grad_norm=args.max_grad_norm)
elif args.algo == 'acktr':
agent = algo.A2C_ACKTR(actor_critic,
args.value_loss_coef,
args.entropy_coef,
acktr=True)
action_space = envs.action_space
if args.env_name in IMG_ENVS:
action_space = np.zeros(2)
# obs_shape = envs.observation_space.shape
rollouts = RolloutStorage(args.num_steps, args.num_processes, obs_shape,
action_space, actor_critic.state_size)
current_obs = torch.zeros(args.num_processes, *obs_shape)
obs = envs.reset()
update_current_obs(obs, current_obs, obs_shape, args.num_stack)
rollouts.observations[0].copy_(current_obs)
# These variables are used to compute average rewards for all processes.
episode_rewards = torch.zeros([args.num_processes, 1])
final_rewards = torch.zeros([args.num_processes, 1])
if args.cuda:
current_obs = current_obs.cuda()
rollouts.cuda()
start = time.time()
for j in range(num_updates):
# envs.display_original(j)
for step in range(args.num_steps):
# Sample actions
with torch.no_grad():
value, action, action_log_prob, states = actor_critic.act(
rollouts.observations[step], rollouts.states[step],
rollouts.masks[step])
cpu_actions = action.squeeze(1).cpu().numpy()
# Obser reward and next obs
obs, reward, done, info = envs.step(cpu_actions)
# envs.display_step(step, j)
# print("OBS", obs)
# print("REWARD", reward)
# print("DONE", done)
# print("INFO", info)
reward = torch.from_numpy(np.expand_dims(np.stack([reward]),
1)).float()
episode_rewards += reward
# If done then clean the history of observations.
masks = torch.FloatTensor([[0.0] if done_ else [1.0]
for done_ in [done]])
final_rewards *= masks
final_rewards += (1 - masks) * episode_rewards
episode_rewards *= masks
if args.cuda:
masks = masks.cuda()
if current_obs.dim() == 4:
current_obs *= masks.unsqueeze(2).unsqueeze(2)
else:
current_obs *= masks
update_current_obs(obs, current_obs, obs_shape, args.num_stack)
rollouts.insert(current_obs, states, action, action_log_prob,
value, reward, masks)
# print("envs.curr_img SHAPE: ", envs.curr_img.shape)
#display_state = envs.curr_img
# display_state[:, envs.pos[0]:envs.pos[0]+envs.window, envs.pos[1]:envs.pos[1]+envs.window] = 5
# display_state = custom_replace(display_state, 1, 0)
# display_state[:, envs.pos[0]:envs.pos[0]+envs.window, envs.pos[1]:envs.pos[1]+envs.window] = \
# envs.curr_img[:, envs.pos[0]:envs.pos[0]+envs.window, envs.pos[1]:envs.pos[1]+envs.window]
# img = transforms.ToPILImage()(display_state)
# img.save("state_cifar/"+"state"+str(j)+"_"+str(step)+".png")
with torch.no_grad():
next_value = actor_critic.get_value(rollouts.observations[-1],
rollouts.states[-1],
rollouts.masks[-1]).detach()
rollouts.compute_returns(next_value, args.use_gae, args.gamma,
args.tau)
value_loss, action_loss, dist_entropy = agent.update(rollouts)
rollouts.after_update()
if j % args.save_interval == 0:
torch.save((actor_critic.state_dict(), results_dict),
os.path.join(model_dir,
name + 'cifar_model_ppo_ex1_center.pt'))
if j % args.log_interval == 0:
end = time.time()
total_reward = eval_episode(eval_env, actor_critic, args)
results_dict['rewards'].append(total_reward)
total_num_steps = (j + 1) * args.num_processes * args.num_steps
print(
"Updates {}, num timesteps {}, FPS {}, reward {:.1f} entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}"
.format(j, total_num_steps,
int(total_num_steps / (end - start)),
np.mean(results_dict['rewards'][-10:]), dist_entropy,
value_loss, action_loss))
plot_rewards.append(np.mean(results_dict['rewards'][-10:]))
plot_policy_loss.append(action_loss)
plot_value_loss.append(value_loss)
plt.plot(range(len(plot_rewards)), plot_rewards)
plt.savefig("rewards_center.png")
plt.close()
plt.plot(range(len(plot_policy_loss)), plot_policy_loss)
plt.savefig("policyloss_center.png")
plt.close()
plt.plot(range(len(plot_value_loss)), plot_value_loss)
plt.savefig("valueloss_center.png")
plt.close()
def load_tnews_data(data_path, data_type, tokenizer, few_shot=False):
args = get_args()
filename = os.path.join(data_path, data_type+'.json')
objs = []
with open(filename) as fin:
for line in fin:
objs.append(json.loads(line.strip()))
pad_id = tokenizer.encoder['<pad>']
args.eod_token = tokenizer.encoder['<eod>']
labels = []
label_map = {}
label_reverse = {}
with open(os.path.join(data_path, 'labels.json')) as fin:
for i, line in enumerate(fin):
obj = json.loads(line.strip())
labels.append(obj['label_desc'])
label_map[obj['label_desc']] = i
label_reverse[obj['label']] = obj['label_desc']
all_tokens = []
all_masks = []
all_labels = []
for _, obj in enumerate(objs):
sentence = obj['sentence']
tokenized_sentence = tokenizer.encode(sentence)[:args.seq_length-20]
obj['label_desc'] = label_reverse[obj['label']]
if few_shot:
cur_labels = random.sample(labels, 3)
while obj['label_desc'] in cur_labels:
cur_labels = random.sample(labels, 3)
cur_labels.append(obj['label_desc'])
cur_label = cur_labels.index(obj['label_desc'])
assert cur_label != -1
else:
cur_labels = labels
cur_label = label_map[obj['label_desc']]
all_labels.append(cur_label)
for _, label in enumerate(cur_labels):
prompt = "这是关于{}的文章：".format(label)
prompt_tokens = tokenizer.encode(prompt)
prompt_len = len(prompt_tokens)
tokens = prompt_tokens + tokenized_sentence
second_mask = [0] * (args.seq_length-1)
for idx in range(prompt_len-1, len(tokens)-1):
second_mask[idx] = 1
all_masks.append(second_mask)
token_length = len(tokens)
assert token_length < args.seq_length
tokens.extend([pad_id] * (args.seq_length - token_length))
all_tokens.append(tokens)
all_tokens = torch.tensor(all_tokens, dtype=torch.long)
all_masks = torch.tensor(all_masks, dtype=torch.float)
dataset = TensorDataset(all_tokens, all_masks)
# Data parallel arguments.
world_size = mpu.get_data_parallel_world_size()
rank = mpu.get_data_parallel_rank()
global_batch_size = args.batch_size * world_size
num_workers = args.num_workers
sampler = torch.utils.data.SequentialSampler(dataset)
batch_sampler = DistributedBatchSampler(sampler=sampler,
batch_size=global_batch_size,
drop_last=True,
rank=rank,
world_size=world_size)
# Torch dataloader.
return torch.utils.data.DataLoader(dataset,
batch_sampler=batch_sampler,
num_workers=num_workers,
pin_memory=True), all_labels
def main():
args = get_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
log_dir = os.path.expanduser(args.log_dir)
eval_log_dir = log_dir + "_eval"
utils.cleanup_log_dir(log_dir)
utils.cleanup_log_dir(eval_log_dir)
if args.cuda and torch.cuda.is_available() and args.cuda_deterministic:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.set_num_threads(1)
device = torch.device("cuda:0" if args.cuda else "cpu")
base=SEVN
actor_critic, obs_rms = torch.load(save_dir, map_location=device)
actor_critic.to(device)
actor_critic.max_eval_success_rate = 0
print("Passed!")
num_processes = args.num_processes
eval_recurrent_hidden_states = torch.zeros(
args.num_processes, actor_critic.recurrent_hidden_state_size, device=device)
eval_masks = torch.zeros(num_processes, 1, device=device)
x = 0
while x < 10:
torch.manual_seed(args.seed + x)
torch.cuda.manual_seed_all(args.seed + x)
eval_envs = make_vec_envs(args.env_name, args.seed + x, args.num_processes,
args.gamma, args.log_dir, device, False, args.custom_gym)
eval_episode_rewards = []
eval_episode_length = []
eval_episode_success_rate = []
obs = eval_envs.reset()
while len(eval_episode_rewards) < num_processes*100:
with torch.no_grad():
_, action, _, eval_recurrent_hidden_states = actor_critic.act(
obs,
eval_recurrent_hidden_states,
eval_masks,
deterministic=True)
eval_envs.render()
obs, _, done, infos = eval_envs.step(action)
eval_masks = torch.tensor(
[[0.0] if done_ else [1.0] for done_ in done],
dtype=torch.float32,
device=device)
for info in infos:
if 'episode' in info.keys():
if info['was_successful_trajectory']:
if args.mod: #Modified Reward Function
reward[idx]=10
episode_rewards.append(10)
else:
eval_episode_rewards.append(info['episode']['r'])
eval_episode_length.append(info['episode']['l'])
eval_episode_success_rate.append(info['was_successful_trajectory'])
x+=1
print(" Evaluation using {} episodes: mean reward {:.5f}, mean_length {:.2f}, mean_success {:.2f} \n".format(
len(eval_episode_rewards), np.mean(eval_episode_rewards), np.mean(eval_episode_length), np.mean(eval_episode_success_rate)))
eval_envs.close()
print(eval_episode_rewards)
print(eval_episode_success_rate)
e = 0
while stats['total_samples'] < args.max_samples:
train(args, env, model, opt, opt_v, kf, stats, ep=e)
avg_eval = eval(args, env, model, stats)
log_writer.writerow([
stats['total_samples'], stats['max_reward'], stats['avg_reward'],
avg_eval
])
log_file.flush()
e += 1
print("total samples: ", stats['total_samples'],
stats['total_samples'] - last_iter_samples)
last_iter_samples = stats['total_samples']
if avg_eval > best_eval or last_save_step - stats[
'total_samples'] > 10000:
best_eval = avg_eval
last_save_step = stats['total_samples']
# save model if evaluation was better
torch.save(
model.state_dict(),
os.path.join(
args.log_dir, "model_ep" + str(e) + "_samples" +
str(stats['total_samples']) + "_eval" + str(avg_eval) +
".pth"))
log_file.close()
if __name__ == '__main__':
import arguments
optimize(arguments.get_args())
def main():
"""Main training program."""
global global_example_count, global_token_count, event_writer, logdir, train_step, train_loss, best_val_loss, eval_start_time, log_start_time, epoch
global_token_count = 0
# Arguments.
args = get_args()
# global global_example_count, global_token_count, event_writer, logdir
logdir = f'{args.logdir}'
os.system(f'mkdir -p {logdir}')
event_writer = SummaryWriter(logdir)
log_tb("first", time.time())
print('Pretrain BERT model')
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Timer.
timers = Timers()
# Pytorch distributed.
initialize_distributed(args)
# Random seeds for reproducability.
set_random_seed(args.seed)
# Data stuff.
data_config = configure_data()
data_config.set_defaults(data_set_type='BERT', transpose=False)
(train_data, val_data, test_data), tokenizer = data_config.apply(args)
args.data_size = tokenizer.num_tokens
# Model, optimizer, and learning rate.
model, optimizer, lr_scheduler, criterion = setup_model_and_optimizer(
args, tokenizer)
# At any point you can hit Ctrl + C to break out of training early.
try:
total_iters = 0
skipped_iters = 0
start_epoch = 1
best_val_loss = float('inf')
# Resume data loader if necessary.
if args.resume_dataloader:
start_epoch = args.epoch
total_iters = args.total_iters
train_data.batch_sampler.start_iter = total_iters % len(train_data)
# For all epochs.
for epoch in range(start_epoch, args.epochs + 1):
timers('epoch time').start()
iteration, skipped = train_epoch(epoch, model, optimizer,
train_data, lr_scheduler,
criterion, timers, args)
elapsed_time = timers('epoch time').elapsed()
total_iters += iteration
skipped_iters += skipped
lm_loss, nsp_loss = evaluate(val_data, model, criterion, args)
val_loss = lm_loss + nsp_loss
print('-' * 100)
print(
'| end of epoch {:3d} | time: {:5.2f}s | valid loss {:.4E} | '
'valid LM Loss {:.4E} | valid NSP Loss {:.4E}'.format(
epoch, elapsed_time, val_loss, lm_loss, nsp_loss))
print('-' * 100)
if val_loss < best_val_loss:
best_val_loss = val_loss
if args.save:
best_path = 'best/model.pt'
print('saving best model to:',
os.path.join(args.save, best_path))
save_checkpoint(best_path, epoch + 1, total_iters, model,
optimizer, lr_scheduler, args)
except KeyboardInterrupt:
print('-' * 100)
print('Exiting from training early')
if args.save:
cur_path = 'current/model.pt'
print('saving current model to:',
os.path.join(args.save, cur_path))
save_checkpoint(cur_path, epoch, total_iters, model, optimizer,
lr_scheduler, args)
exit()
if args.save:
final_path = 'final/model.pt'
print('saving final model to:', os.path.join(args.save, final_path))
save_checkpoint(final_path, args.epochs, total_iters, model, optimizer,
lr_scheduler, args)
if test_data is not None:
# Run on test data.
print('entering test')
lm_loss, nsp_loss = evaluate(test_data, model, criterion, args)
test_loss = lm_loss + nsp_loss
print('=' * 100)
print('| End of training | test loss {:5.4f} | valid LM Loss {:.4E} |'
' valid NSP Loss {:.4E}'.format(test_loss, lm_loss, nsp_loss))
print('=' * 100)
np.save('MRR' + str(opt.num_qbots) + str(opt.num_abots),
mean_rec_rank_final.cpu().numpy())
np.save('r1' + str(opt.num_qbots) + str(opt.num_abots),
r1_final.cpu().numpy())
np.save('r5' + str(opt.num_qbots) + str(opt.num_abots),
r5_final.cpu().numpy())
np.save('r10' + str(opt.num_qbots) + str(opt.num_abots),
r10_final.cpu().numpy())
return
##############################
# Main Code Execution Starts Here
##############################
opt = get_args()
opt.manualSeed = random.randint(1, 10000)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
np.random.seed(opt.manualSeed)
if opt.cuda:
torch.cuda.manual_seed_all(opt.manualSeed)
if torch.cuda.is_available() and not opt.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
t = datetime.datetime.now()
cur_time = '%s-%s-%s' % (t.day, t.month, t.hour)
save_path = os.path.join(opt.outf, cur_time)
def apt_select():
"""Run apt-select: Ubuntu archive mirror reporting tool"""
parser = get_args()
args = parser.parse_args()
top_number = args.top_number[0]
ping_only = args.ping_only
list_only = args.list_only
choose = args.choose
min_status = args.min_status[0].replace('-', ' ')
if not ping_only and (min_status != 'unknown'):
# Convert status argument to format used by Launchpad
min_status = min_status[0].upper() + min_status[1:]
if choose and (not top_number or top_number < 2):
parser.print_usage()
exit((
"error: -c/--choose option requires -t/--top-number NUMBER "
"where NUMBER is greater than 1."
))
try:
release = check_output(["lsb_release", "-ics"])
except OSError:
not_ubuntu()
else:
release = [s.strip() for s in release.decode('utf-8').split()]
if release[0] == 'Debian':
exit("Debian is not currently supported")
elif release[0] != 'Ubuntu':
not_ubuntu()
directory = '/etc/apt/'
apt_file = 'sources.list'
sources_path = directory + apt_file
if not path.isfile(sources_path):
exit("%s must exist as file" % sources_path)
mirrors_loc = "mirrors.ubuntu.com"
mirrors_url = "http://%s/mirrors.txt" % mirrors_loc
stderr.write("Getting list of mirrors...")
try:
mirrors_list = get_html(mirrors_url)
except HTMLGetError as err:
exit("Error getting list from %s:\n\t%s" % (mirrors_list, err))
stderr.write("done.\n")
mirrors_list = mirrors_list.splitlines()
codename = release[1][0].upper() + release[1][1:]
hardware = check_output(["uname", "-m"]).strip().decode('utf-8')
if hardware == 'x86_64':
hardware = 'amd64'
else:
hardware = 'i386'
archives = Mirrors(mirrors_list, ping_only, min_status)
archives.get_rtts()
if archives.got["ping"] < top_number:
top_number = archives.got["ping"]
if top_number == 0:
exit("Cannot connect to any mirrors in %s\n." % mirrors_list)
if not ping_only:
archives.get_launchpad_urls()
if not archives.abort_launch:
# Mirrors needs a limit to stop launching threads
archives.status_num = top_number
stderr.write("Looking up %d status(es)\n" % top_number)
archives.lookup_statuses(min_status, codename, hardware)
if top_number > 1:
stderr.write('\n')
repo_name = ""
found = False
skip_gen_msg = "Skipping file generation."
with open(sources_path, 'r') as sources_file:
lines = sources_file.readlines()
repos = []
required_repo = "main"
for line in lines:
fields = line.split()
if confirm_mirror(fields):
if (not found and
(release[1] in fields[2]) and
(fields[3] == required_repo)):
repos += [fields[1]]
found = True
continue
elif fields[2] == '%s-security' % (release[1]):
repos += [fields[1]]
break
if not repos:
stderr.write((
"Error finding current %s repository in %s\n%s\n" %
(required_repo, sources_path, skip_gen_msg)
))
else:
repo_name = repos[0]
rank = 0
current_key = -1
if ping_only:
archives.top_list = archives.ranked[:top_number+1]
for url in archives.top_list:
info = archives.urls[url]
host = info["Host"]
if url == repo_name:
host += " (current)"
current_key = rank
if not ping_only and not archives.abort_launch:
if "Status" in info:
assign_defaults(info, ("Org", "Speed"), "N/A")
print((
"%(rank)d. %(mirror)s\n%(tab)sLatency: %(ms)d ms\n"
"%(tab)sOrg: %(org)s\n%(tab)sStatus: %(status)s\n"
"%(tab)sSpeed: %(speed)s" % {
'tab': ' ',
'rank': rank + 1,
'mirror': host,
'ms': info["Latency"],
'org': info["Organisation"],
'status': info["Status"],
'speed': info["Speed"]
}
))
else:
print("%d. %s: %d ms" % (rank+1, info["Host"], info["Latency"]))
rank += 1
if rank == top_number:
break
key = 0
if choose:
key = ask((
"Choose a mirror (1 - %d)\n'q' to quit " %
len(archives.top_list)
))
while True:
try:
key = int(key)
except ValueError:
if key == 'q':
exit()
if (type(key) is not str) and (key >= 1) and (key <= rank):
break
key = ask("Invalid entry ")
key -= 1
if list_only:
exit()
# Avoid generating duplicate sources.list
if current_key == key:
exit((
"%s is the currently used mirror.\n%s" %
(archives.urls[repo_name]["Host"], skip_gen_msg)
))
mirror = archives.top_list[key]
lines = ''.join(lines)
for repo in repos:
lines = lines.replace(repo, mirror)
work_dir = getcwd()
if work_dir == directory[0:-1]:
query = (
"'%(dir)s' is the current directory.\n"
"Generating a new '%(apt)s' file will "
"overwrite the current file.\n"
"You should copy or backup '%(apt)s' before replacing it.\n"
"Continue?\n[yes|no] " % {
'dir': directory,
'apt': apt_file
}
)
yes_or_no(query)
write_file = work_dir.rstrip('/') + '/' + apt_file
try:
with open(write_file, 'w') as sources_file:
sources_file.write(lines)
except IOError as err:
exit("Unable to generate sources.list:\n\t%s\n" % err)
else:
print("New config file saved to %s" % write_file)
exit()
import torch
import torch.utils.data as td
import numpy as np
import scipy.io as sio
import data_handler
import networks
import trainer
import arguments
# import deepspeed
from sklearn.utils import shuffle
from sklearn.metrics import roc_auc_score
from tqdm import tqdm
args = arguments.get_args()
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
dataset = data_handler.DatasetFactory.get_dataset(args.dataset)
#loader = dataset.loader
seed = args.seed
m = args.memory_budget
# Fix the seed.
args.seed = seed
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
def main():
"""Main training program."""
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Timer.
timers = Timers()
# Arguments.
args = get_args()
# Pytorch distributed.
initialize_distributed(args)
if torch.distributed.get_rank() == 0:
print('Pretrain GPT2 model')
print_args(args)
# Random seeds for reproducability.
set_random_seed(args.seed)
# prepare log file
os.makedirs(args.save, exist_ok=True)
with open(args.log_file, "w") as f:
f.write("Logging:\n")
# Model, optimizer, and learning rate.
with open(args.student_config_path, "r") as f:
student_config = json.load(f)
student_model, optimizer, lr_scheduler, student_iteration = setup_model_and_optimizer(
args,
student_config,
need_optim=True,
ckpt_path=args.student_load,
do_fp16=args.fp16)
args.iteration = student_iteration
teacher_model = None
if args.teacher_config_path is not None:
with open(args.teacher_config_path, "r") as f:
teacher_config = json.load(f)
teacher_model, _, _, _ = setup_model_and_optimizer(
args,
teacher_config,
need_optim=True,
ckpt_path=args.teacher_load,
do_fp16=(args.fp16 or args.teacher_fp16))
if torch.distributed.get_rank() == 0:
print(student_iteration)
train_data_iterator, val_data_iterator, test_data_iterator = \
build_train_valid_test_data_iterators(
train_valid_test_dataset_provider, args)
iteration = 0
if args.do_train:
iteration, skipped = train(student_model, teacher_model, optimizer,
lr_scheduler, train_data_iterator,
val_data_iterator, timers, args)
prefix = 'the end of training for val data'
evaluate_and_print_results(prefix, val_data_iterator, student_model,
teacher_model, args, timers, False)
if args.save and iteration != 0:
save_checkpoint(iteration, student_model, optimizer, lr_scheduler,
args)
if args.do_test:
# Run on test data.
prefix = 'the end of training for test data'
evaluate_and_print_results(prefix, test_data_iterator, student_model,
teacher_model, args, timers, True)
if is_value_list:
for rk, rv in sorted(results_dict.items()):
if keys_to_print is not None and rk not in keys_to_print:
continue
cprint_and_append("{}: {}".format(rk, rv))
return line_list
if __name__ == '__main__':
num_total_runs = 7
main_args = get_args(
model_name="GAT", # GAT, GCN
dataset_class=
"Planetoid", # Planetoid, FullPlanetoid, RandomPartitionGraph
dataset_name="Cora", # Cora, CiteSeer, PubMed, rpg-10-500-0.1-0.025
custom_key=
"EV13NSO8", # NEO8, NEDPO8, EV13NSO8, EV9NSO8, EV1O8, EV2O8, -500, -Link, -ES, -ATT
)
pprint_args(main_args)
if len(main_args.gpu_deny_list) == main_args.num_gpus_total:
alloc_gpu = [None]
cprint("Use CPU", "yellow")
else:
alloc_gpu = blind_other_gpus(num_gpus_total=main_args.num_gpus_total,
num_gpus_to_use=main_args.num_gpus_to_use,
gpu_deny_list=main_args.gpu_deny_list)
if not alloc_gpu:
alloc_gpu = [
int(
def main():
config = None
args = get_args()
config, checkpoint = get_config_and_checkpoint(args)
set_random_seeds(args, config)
eval_log_dir = args.save_dir + "_eval"
try:
os.makedirs(args.save_dir)
os.makedirs(eval_log_dir)
except OSError:
pass
now = datetime.datetime.now()
experiment_name = args.experiment_name + '_' + now.strftime("%Y-%m-%d_%H-%M-%S")
# Create checkpoint file
save_dir_model = os.path.join(args.save_dir, 'model', experiment_name)
save_dir_config = os.path.join(args.save_dir, 'config', experiment_name)
try:
os.makedirs(save_dir_model)
os.makedirs(save_dir_config)
except OSError as e:
logger.error(e)
exit()
if args.config:
shutil.copy2(args.config, save_dir_config)
curriculum = args.follow_curriculum
if args.follow_curriculum:
print('Using preset curriculum')
# Tensorboard Logging
writer = SummaryWriter(os.path.join(args.save_dir, 'tensorboard', experiment_name))
# Logger that writes to STDOUT and a file in the save_dir
logger = setup_carla_logger(args.save_dir, experiment_name)
device = torch.device("cuda:0" if args.cuda else "cpu")
norm_reward = not config.no_reward_norm
norm_obs = not config.no_obs_norm
assert not (config.num_virtual_goals > 0) or (config.reward_class == 'SparseReward'), 'Cant use HER with dense reward'
obs_converter = CarlaObservationConverter(h=84, w=84, rel_coord_system=config.rel_coord_system)
action_converter = CarlaActionsConverter(config.action_type)
envs = make_vec_envs(obs_converter, action_converter, args.starting_port, config.seed, config.num_processes,
config.gamma, device, config.reward_class, num_frame_stack=1, subset=config.experiments_subset,
norm_reward=norm_reward, norm_obs=norm_obs, apply_her=config.num_virtual_goals > 0,
video_every=args.video_interval,
video_dir=os.path.join(args.save_dir, 'video',
experiment_name),
curriculum=curriculum)
if config.agent == 'forward':
agent = agents.ForwardCarla()
if config.agent == 'vpg':
agent = agents.VPGCarla(obs_converter,
action_converter,
config.value_loss_coef,
config.entropy_coef,
lr=config.lr,
eps=config.eps, alpha=config.alpha,
gamma=config.gamma,
max_grad_norm=config.max_grad_norm)
if config.agent == 'a2c':
agent = agents.A2CCarla(obs_converter,
action_converter,
config.value_loss_coef,
config.entropy_coef,
lr=config.lr,
eps=config.eps, alpha=config.alpha,
max_grad_norm=config.max_grad_norm)
elif config.agent == 'acktr':
agent = agents.A2CCarla(obs_converter,
action_converter,
config.value_loss_coef,
config.entropy_coef,
lr=config.lr,
eps=config.eps, alpha=config.alpha,
max_grad_norm=config.max_grad_norm,
acktr=True)
elif config.agent == 'ppo':
agent = agents.PPOCarla(obs_converter,
action_converter,
config.clip_param,
config.ppo_epoch,
config.num_mini_batch,
config.value_loss_coef,
config.entropy_coef,
lr=config.lr,
eps=config.eps,
max_grad_norm=config.max_grad_norm)
if checkpoint is not None:
load_modules(agent.optimizer, agent.model, checkpoint)
rollouts = RolloutStorage(config.num_steps, config.num_processes,
envs.observation_space, envs.action_space, 20,
config.num_virtual_goals, config.rel_coord_system, obs_converter)
obs = envs.reset()
# Save the first observation
obs = obs_to_dict(obs)
rollouts.obs = obs_to_dict(rollouts.obs)
for k in rollouts.obs:
rollouts.obs[k][rollouts.step + 1].copy_(obs[k])
rollouts.obs = dict_to_obs(rollouts.obs)
rollouts.to(device)
start = time.time()
total_steps = 0
total_episodes = 0
total_reward = 0
episode_reward = torch.zeros(config.num_processes)
for j in range(config.num_updates):
for step in range(config.num_steps):
# Sample actions
with torch.no_grad():
value, action, action_log_prob, recurrent_hidden_states = agent.act(
rollouts.get_obs(step),
rollouts.recurrent_hidden_states[step],
rollouts.masks[step])
# Observe reward and next obs
obs, reward, done, info = envs.step(action)
# For logging purposes
carla_rewards = torch.tensor([i['carla-reward'] for i in info], dtype=torch.float)
episode_reward += carla_rewards
total_reward += carla_rewards.sum().item()
total_steps += config.num_processes * config.num_steps
if done.any():
total_episodes += done.sum()
torch_done = torch.tensor(done.astype(int)).byte()
mean_episode_reward = episode_reward[torch_done].mean().item()
logger.info('{} episode(s) finished with reward {}'.format(done.sum(), mean_episode_reward))
writer.add_scalar('train/mean_ep_reward_vs_steps', mean_episode_reward, total_steps)
writer.add_scalar('train/mean_ep_reward_vs_episodes', mean_episode_reward, total_episodes)
episode_reward[torch_done] = 0
# If done then clean the history of observations.
masks = torch.FloatTensor(1-done)
rollouts.insert(obs, recurrent_hidden_states, action, action_log_prob, value, reward, masks.unsqueeze(-1))
if config.num_virtual_goals > 0:
rollouts.apply_her(config.num_virtual_goals, device, beta=config.beta)
with torch.no_grad():
next_value = agent.get_value(rollouts.get_obs(-1), # Get last observation
rollouts.recurrent_hidden_states[-1],
rollouts.masks[-1]).detach()
rollouts.compute_returns(next_value, config.use_gae, config.gamma, config.tau)
value_loss, action_loss, dist_entropy = agent.update(rollouts)
rollouts.after_update()
if j % args.save_interval == 0 and args.save_dir != "" and config.agent !='forward':
save_path = os.path.join(save_dir_model, str(j) + '.pth.tar')
save_modules(agent.optimizer, agent.model, args, config, save_path)
total_num_steps = (j + 1) * config.num_processes * config.num_steps
if j % args.log_interval == 0:
# Logging to the stdout/our logs
end = time.time()
logger.info('------------------------------------')
logger.info('Episodes {}, Updates {}, num timesteps {}, FPS {}'\
.format(total_episodes, j + 1, total_num_steps, total_num_steps / (end - start)))
logger.info('------------------------------------')
# Logging to tensorboard
writer.add_scalar('train/cum_reward_vs_steps', total_reward, total_steps)
writer.add_scalar('train/cum_reward_vs_updates', total_reward, j+1)
if config.agent in ['a2c', 'acktr', 'ppo']:
writer.add_scalar('debug/value_loss_vs_steps', value_loss, total_steps)
writer.add_scalar('debug/value_loss_vs_updates', value_loss, j+1)
writer.add_scalar('debug/action_loss_vs_steps', action_loss, total_steps)
writer.add_scalar('debug/action_loss_vs_updates', action_loss, j+1)
writer.add_scalar('debug/dist_entropy_vs_steps', dist_entropy, total_steps)
writer.add_scalar('debug/dist_entropy_vs_updates', dist_entropy, j+1)
# Sample the last reward
writer.add_scalar('debug/sampled_normalized_reward_vs_steps', reward.mean(), total_steps)
writer.add_scalar('debug/sampled_normalized_reward_vs_updates', reward.mean(), j+1)
writer.add_scalar('debug/sampled_carla_reward_vs_steps', carla_rewards.mean(), total_steps)
writer.add_scalar('debug/sampled_carla_reward_vs_updates', carla_rewards.mean(), j+1)
if (args.eval_interval is not None and j % args.eval_interval == 0):
eval_envs = make_vec_envs(
args.env_name, args.starting_port, obs_converter, args.x + config.num_processes, config.num_processes,
config.gamma, eval_log_dir, config.add_timestep, device, True,
curriculum)
vec_norm = get_vec_normalize(eval_envs)
if vec_norm is not None:
vec_norm.ob_rms = get_vec_normalize(envs).ob_rms
eval_episode_rewards = []
obs = eval_envs.reset()
eval_recurrent_hidden_states = torch.zeros(config.num_processes,
20, device=device)
eval_masks = torch.zeros(config.num_processes, 1, device=device)
while len(eval_episode_rewards) < 10:
with torch.no_grad():
_, action, _, eval_recurrent_hidden_states = agent.act(
obs, eval_recurrent_hidden_states, eval_masks, deterministic=True)
# Obser reward and next obs
carla_obs, reward, done, infos = eval_envs.step(action)
eval_masks = torch.FloatTensor([[0.0] if done_ else [1.0]
for done_ in done])
for info in infos:
if 'episode' in info.keys():
eval_episode_rewards.append(info['episode']['r'])
eval_envs.close()
logger.info(" Evaluation using {} episodes: mean reward {:.5f}\n".
format(len(eval_episode_rewards),
np.mean(eval_episode_rewards)))
import os
import random
import sys
from pathlib import Path
from shutil import copyfile
from arguments import get_args
random.seed(1)
args = get_args('args for datasplit (citiscapes)', mode='data')
current_path = os.path.abspath('')
train_path = "/home/himanshu/cityscape/leftImg8bit/train"
train_path_lb = "/home/himanshu/cityscape/gtFine/train"
perc = args.percentage
if perc < 0 or perc > 100:
print('Illegal usage of -p, only between 0 and 100')
sys.exit(0)
destination = Path("/home/akshay/cityscape/frac" + str(perc) +
"/leftImg8bit/train")
os.makedirs(str(destination), exist_ok=True)
destination_lb = Path("/home/akshay/cityscape/frac" + str(perc) +
"/gtFine/train")
os.makedirs(str(destination_lb), exist_ok=True)
for folder in os.listdir(train_path):
des1 = Path(destination / folder)
feature = model(images.to(args.device))
preds = classifier(feature)
loss = F.cross_entropy(preds, labels.to(args.device))
loss.backward()
optimizer.step()
loss_meter.update(loss.item())
lr = lr_scheduler.step()
local_progress.set_postfix({
'lr': lr,
"loss": loss_meter.val,
'loss_avg': loss_meter.avg
})
classifier.eval()
correct, total = 0, 0
acc_meter.reset()
for idx, (images, labels) in enumerate(test_loader):
with torch.no_grad():
feature = model(images.to(args.device))
preds = classifier(feature).argmax(dim=1)
correct = (preds == labels.to(args.device)).sum().item()
acc_meter.update(correct / preds.shape[0])
print(f'Accuracy = {acc_meter.avg * 100:.2f}')
if __name__ == "__main__":
main(args=get_args())
def main():
doers = _get_doers(shell)
doers.update(_get_doers(utils))
possible_actions = doers.keys() + ['start', 'stop', 'status']
args = arguments.get_args(possible_actions)
if args.action is None:
print ('No action')
return 65 # os.EX_DATAERR
apiclient = None
verify = True
if args.insecure:
verify = False
if args.no_api is False:
apiclient = client.Client(opts=args, verify=verify)
if args.client_id:
apiclient.client_id = args.client_id
else:
if winutils.is_windows():
print("--no-api mode is not available on windows")
return 69 # os.EX_UNAVAILABLE
if args.action in doers:
try:
return doers[args.action](apiclient, args)
except Exception as e:
print ('ERROR {0}'.format(e))
return 70 # os.EX_SOFTWARE
freezer_scheduler = FreezerScheduler(apiclient=apiclient,
interval=int(args.interval),
job_path=args.jobs_dir)
if args.no_daemon:
print ('Freezer Scheduler running in no-daemon mode')
daemon = NoDaemon(daemonizable=freezer_scheduler)
else:
if winutils.is_windows():
daemon = Daemon(daemonizable=freezer_scheduler,
interval=int(args.interval),
job_path=args.jobs_dir,
insecure=args.insecure)
else:
daemon = Daemon(daemonizable=freezer_scheduler)
if args.action == 'start':
daemon.start(log_file=args.log_file)
elif args.action == 'stop':
daemon.stop()
elif args.action == 'reload':
daemon.reload()
elif args.action == 'status':
daemon.status()
# os.RETURN_CODES are only available to posix like systems, on windows
# we need to translate the code to an actual number which is the equivalent
return 0 # os.EX_OK
def main():
args = get_args()
model_types = ['conv_net', 'conv_net_attn', 'transformer']
assert args.model_type in model_types
raw_data = data_utils.read_smiles_ring_data('%s/raw.csv' % args.data)
atom_predictor, optimizer = init_model(args, args.n_classes)
data_utils.load_shortest_paths(args) # Shortest paths includes all splits
agg_stats = ['loss', 'nei_score', 'acc', 'auc', 'gnorm', 'gnorm_clip']
selection_stat = 'acc'
select_higher = True
if args.test_mode:
dataset_loaders = load_datasets(raw_data, 0, args)
test_model(
dataset_loaders=dataset_loaders,
model=atom_predictor,
stat_names=agg_stats,
train_func=run_epoch,
args=args,
)
exit()
all_stats = {}
for name in agg_stats:
all_stats[name] = []
output_dir = args.output_dir
all_model_paths = []
for round_idx in range(args.n_rounds):
dataset_loaders = load_datasets(raw_data, round_idx, args, n_workers=0)
atom_predictor, optimizer = init_model(args, args.n_classes)
cur_output_dir = '%s/run_%d' % (output_dir, round_idx)
args.output_dir = cur_output_dir
create_dirs(args, cur_output_dir)
test_stats, best_model_path = train_model(
dataset_loaders=dataset_loaders,
model=atom_predictor,
optimizer=optimizer,
stat_names=agg_stats,
selection_stat=selection_stat,
train_func=run_epoch,
args=args,
select_higher=select_higher,
)
# Aggregate stats of interest
for name in agg_stats:
all_stats[name].append(test_stats[name])
all_model_paths.append(best_model_path)
# Write summary file
summary_file = open('%s/summary.txt' % output_dir, 'w+')
for name, stats_arr in all_stats.items():
stats = np.array(stats_arr)
mean, std = np.mean(stats), np.std(stats)
stats_string = '%s: %s, mean: %.3f, std: %.3f' % (name, str(stats_arr),
mean, std)
print(stats_string)
summary_file.write('%s\n' % stats_string)
for model_path in all_model_paths:
summary_file.write('%s\n' % model_path)
summary_file.close()