def run(args):
if args.seed is not None:
from utils.common import init_random
init_random(args.seed)
# log.info(json.dumps(vars(args), indent=4))
datasets = {}
dsname = args.dataset_val[0]
root, cache_root = cfg.get_dataset_paths(dsname)
dataset_cls = cfg.get_dataset_class(dsname)
datasets[VAL] = dataset_cls(root=root,
cache_root=cache_root,
train=False,
test_split=args.test_split,
max_samples=args.val_count,
start=args.st,
use_cache=args.use_cache,
align_face_orientation=args.align,
crop_source=args.crop_source,
return_landmark_heatmaps=True,
landmark_sigma=args.sigma,
image_size=args.input_size)
print(datasets[VAL])
fntr = FabrecEval(datasets,
args,
args.sessionname,
workers=args.workers,
wait=args.wait)
import torch
torch.backends.cudnn.benchmark = True
fntr.eval_epoch()
def train(domain_name: str, log_to_file: bool, seed: int, train_epochs: int, train_dialogs: int,
eval_dialogs: int, max_turns: int, train_error_rate: float, test_error_rate: float,
lr: float, eps_start: float, grad_clipping: float, buffer_classname: str,
buffer_size: int, use_tensorboard: bool):
common.init_random(seed=seed)
file_log_lvl = LogLevel.DIALOGS if log_to_file else LogLevel.NONE
logger = DiasysLogger(console_log_lvl=LogLevel.RESULTS, file_log_lvl=file_log_lvl)
if buffer_classname == "prioritized":
buffer_cls = NaivePrioritizedBuffer
elif buffer_classname == "uniform":
buffer_cls = UniformBuffer
domain = JSONLookupDomain(name=domain_name)
bst = HandcraftedBST(domain=domain, logger=logger)
user = HandcraftedUserSimulator(domain, logger=logger)
noise = SimpleNoise(domain=domain, train_error_rate=train_error_rate,
test_error_rate=test_error_rate, logger=logger)
policy = DQNPolicy(domain=domain, lr=lr, eps_start=eps_start,
gradient_clipping=grad_clipping, buffer_cls=buffer_cls,
replay_buffer_size=buffer_size, train_dialogs=train_dialogs,
logger=logger)
evaluator = PolicyEvaluator(domain=domain, use_tensorboard=use_tensorboard,
experiment_name=domain_name, logger=logger)
ds = DialogSystem(policy,
user,
noise,
bst,
evaluator)
for epoch in range(train_epochs):
# train one epoch
ds.train()
evaluator.start_epoch()
for episode in range(train_dialogs):
ds.run_dialog(max_length=max_turns)
evaluator.end_epoch() # important for statistics!
ds.num_dialogs = 0 # IMPORTANT for epsilon scheduler in dqnpolicy
policy.save() # save model
# evaluate one epoch
ds.eval()
evaluator.start_epoch()
for episode in range(eval_dialogs):
ds.run_dialog(max_length=max_turns)
evaluator.end_epoch() # important for statistics!
ds.num_dialogs = 0 # IMPORTANT for epsilon scheduler in dqnpolicy
def run(args):
from utils.common import init_random
if args.seed is not None:
init_random(args.seed)
# log.info(json.dumps(vars(args), indent=4))
datasets = {}
for phase, dsnames, num_samples in zip((TRAIN, VAL),
(args.dataset_train, args.dataset_val),
(args.train_count, args.val_count)):
train = phase == TRAIN
datasets[phase] = DATASETS[dsnames[0]](train=train,
max_samples=num_samples,
use_cache=args.use_cache,
start=args.st,
test_split=args.test_split,
align_face_orientation=args.align,
crop_source=args.crop_source,
return_landmark_heatmaps=lmcfg.PREDICT_HEATMAP and not args.train_coords,
# return_landmark_heatmaps=True,
return_modified_images=args.mod and train,
landmark_sigma=args.sigma,
# landmark_ids=lmcfg.LANDMARKS,
daug=args.daug)
print(datasets[phase])
fntr = AAELandmarkTraining(datasets, args, session_name=args.sessionname, batch_size=args.batchsize,
macro_batch_size=args.macro_batchsize,
snapshot_interval=args.save_freq, snapshot=args.resume, workers=args.workers,
wait=args.wait)
torch.backends.cudnn.benchmark = True
if args.eval:
fntr.eval_epoch()
else:
fntr.train(num_epochs=args.epochs)
log.console_level = log.LogLevel.RESULTS
log.file_level = log.LogLevel.DIALOGS
logger = log.DiasysLogger()
from modules.policy.rl.experience_buffer import UniformBuffer, NaivePrioritizedBuffer
from dialogsystem import DialogSystem
from modules.bst import HandcraftedBST
from modules.policy import DQNPolicy
from modules.simulator import HandcraftedUserSimulator
from utils.domain.jsonlookupdomain import JSONLookupDomain
from modules.policy.evaluation import PolicyEvaluator
from utils import common
if __name__ == "__main__":
common.init_random()
TRAIN_EPISODES = 1000
NUM_TEST_SEEDS = 10
EVAL_EPISODES = 500
MAX_TURNS = -1
TRAIN_EPOCHS = 10
# get #num_test_seeds random seeds
random_seeds = []
for i in range(NUM_TEST_SEEDS):
random_seeds.append(random.randint(0, 2**32-1))
results = {}
for seed in random_seeds:
common.init_once = False
def extract_main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--st', default=None, type=int)
parser.add_argument('--nd', default=None, type=int)
parser.add_argument('--split', default='train')
args = parser.parse_args()
extract_features(args.split, st=args.st, nd=args.nd)
if __name__ == '__main__':
# extract_main()
# exit()
from utils.nn import Batch
import utils.common as util
util.init_random()
ds = VggFace2(train=True, deterministic=True, use_cache=False, align_face_orientation=False,
return_modified_images=False, image_size=256)
micro_batch_loader = td.DataLoader(ds, batch_size=10, shuffle=True, num_workers=0)
f = 1.0
t = time.perf_counter()
for iter, data in enumerate(micro_batch_loader):
print('t load:', time.perf_counter() - t)
t = time.perf_counter()
batch = Batch(data, gpu=False)
print('t Batch:', time.perf_counter() - t)
images = ds_utils.denormalized(batch.images)
vis.vis_square(images, fx=f, fy=f, normalize=False, nCols=10, wait=0)
def train(domain_name: str, log_to_file: bool, seed: int, train_epochs: int, train_dialogs: int,
eval_dialogs: int, max_turns: int, train_error_rate: float, test_error_rate: float,
lr: float, eps_start: float, grad_clipping: float, buffer_classname: str,
buffer_size: int, use_tensorboard: bool):
"""
Training loop for the RL policy, for information on the parameters, look at the descriptions
of commandline arguments in the "if main" below
"""
seed = seed if seed != -1 else None
common.init_random(seed=seed)
file_log_lvl = LogLevel.DIALOGS if log_to_file else LogLevel.NONE
logger = DiasysLogger(console_log_lvl=LogLevel.RESULTS, file_log_lvl=file_log_lvl)
summary_writer = SummaryWriter(log_dir='logs') if use_tensorboard else None
if buffer_classname == "prioritized":
buffer_cls = NaivePrioritizedBuffer
elif buffer_classname == "uniform":
buffer_cls = UniformBuffer
domain = JSONLookupDomain(name=domain_name)
bst = HandcraftedBST(domain=domain, logger=logger)
user = HandcraftedUserSimulator(domain, logger=logger)
# noise = SimpleNoise(domain=domain, train_error_rate=train_error_rate,
# test_error_rate=test_error_rate, logger=logger)
policy = DQNPolicy(domain=domain, lr=lr, eps_start=eps_start,
gradient_clipping=grad_clipping, buffer_cls=buffer_cls,
replay_buffer_size=buffer_size, train_dialogs=train_dialogs,
logger=logger, summary_writer=summary_writer)
evaluator = PolicyEvaluator(domain=domain, use_tensorboard=use_tensorboard,
experiment_name=domain_name, logger=logger,
summary_writer=summary_writer)
ds = DialogSystem(services=[user, bst, policy, evaluator], protocol='tcp')
# ds.draw_system_graph()
error_free = ds.is_error_free_messaging_pipeline()
if not error_free:
ds.print_inconsistencies()
for j in range(train_epochs):
# START TRAIN EPOCH
evaluator.train()
policy.train()
evaluator.start_epoch()
for episode in range(train_dialogs):
if episode % 100 == 0:
print("DIALOG", episode)
logger.dialog_turn("\n\n!!!!!!!!!!!!!!!! NEW DIALOG !!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n")
ds.run_dialog(start_signals={f'user_acts/{domain.get_domain_name()}': []})
evaluator.end_epoch()
policy.save()
# START EVAL EPOCH
evaluator.eval()
policy.eval()
evaluator.start_epoch()
for episode in range(eval_dialogs):
logger.dialog_turn("\n\n!!!!!!!!!!!!!!!! NEW DIALOG !!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n")
ds.run_dialog(start_signals={f'user_acts/{domain.get_domain_name()}': []})
evaluator.end_epoch()
ds.shutdown()
default=25,
type=int,
help=
"maximum turns per dialog (dialogs with more turns will be terminated and counting as failed"
)
parser.add_argument("-eer",
"--evalerror",
type=float,
default=0.0,
help="simulation error rate while evaluating")
args = parser.parse_args()
# init random generator and logger
common.init_random(args.randomseed)
file_log_lvl = LogLevel.DIALOGS if args.logtofile else LogLevel.NONE
logger = DiasysLogger(console_log_lvl=LogLevel.RESULTS,
file_log_lvl=file_log_lvl)
# choose 'real' domain name from shorthand
if args.domain == 'courses':
domain_name = 'ImsCourses'
elif args.domain == 'lecturers':
domain_name = 'ImsLecturers'
test_hdc_usersim(domain_name=domain_name,
logger=logger,
eval_epochs=args.epochs,
eval_dialogs=args.evaldialogs,
max_turns=args.maxturns,
evaluator = PolicyEvaluator(domain=super_domain,
use_tensorboard=True,
experiment_name="tutorial",
logger=dialogue_logger,
summary_writer=summary_writer)
# SET CONSTANTS
TRAIN_EPOCHS = 1
TRAIN_EPISODES = 1000
EVAL_EPISODES = 1000
MAX_TURNS = 25
bst = HandcraftedBST(domain=super_domain)
# Choose how many repeated trials
for i in range(1):
common.init_random(
) # add seed here as a parameter to the init_random if wanted
ds = DialogSystem(services=[user_sim, bst, policy, evaluator],
protocol='tcp')
# Start train/eval loop
for j in range(TRAIN_EPOCHS):
# START TRAIN EPOCH
evaluator.train()
policy.train()
evaluator.start_epoch()
for episode in range(TRAIN_EPISODES):
if episode % 100 == 0:
print("DIALOG", episode)
logger.dialog_turn(
"\n\n!!!!!!!!!!!!!!!! NEW DIALOG !!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n"
from utils import common
if __name__ == "__main__":
logger = DiasysLogger(console_log_lvl=LogLevel.RESULTS,
file_log_lvl=LogLevel.DIALOGS)
turns = {}
success = {}
for domain in [
JSONLookupDomain(domain_str) for domain_str in ['ImsCourses']
]:
turns[domain.name] = []
success[domain.name] = []
for i in range(5):
common.init_random() # add seed here if wanted
TRAIN_EPOCHS = 10
TRAIN_EPISODES = 1000
EVAL_EPISODES = 1000
MAX_TURNS = 25
bst = HandcraftedBST(domain=domain, logger=logger)
user = HandcraftedUserSimulator(domain, logger=logger)
noise = SimpleNoise(domain=domain,
train_error_rate=0.30,
test_error_rate=0.30,
logger=logger)
policy = DQNPolicy(domain=domain,
lr=0.0001,
eps_start=0.3,
