55 lines
2.4 KiB
Python
55 lines
2.4 KiB
Python
# This is a wrapper around train.py which allows you to train a set of models using a variety of different training
|
|
# paradigms. This works by using the yielding mechanism built into train.py to iterate one step at a time and
|
|
# synchronize the underlying models.
|
|
#
|
|
# Note that this wrapper is **EXTREMELY** simple and doesn't attempt to do many things. Some issues you should plan for:
|
|
# 1) Each trainer will have its own optimizer for the underlying model - even when the model is shared.
|
|
# 2) Each trainer will run validation and save model states according to its own schedule. Likewise:
|
|
# 3) Each trainer will load state params for the models it controls independently, regardless of whether or not those
|
|
# models are shared. Your best bet is to have all models save state at the same time so that they all load ~ the same
|
|
# state when re-started.
|
|
import argparse
|
|
|
|
import yaml
|
|
|
|
import train
|
|
import utils.options as option
|
|
from utils.util import OrderedYaml
|
|
|
|
|
|
def main(master_opt, launcher):
|
|
trainers = []
|
|
all_networks = {}
|
|
shared_networks = []
|
|
for i, sub_opt in enumerate(master_opt['trainer_options']):
|
|
sub_opt_parsed = option.parse(sub_opt, is_train=True)
|
|
trainer = train.Trainer()
|
|
trainer.init(sub_opt_parsed, launcher, all_networks)
|
|
train_gen = trainer.create_training_generator(i)
|
|
model = next(train_gen)
|
|
for k, v in model.networks.items():
|
|
if k in all_networks.keys() and k not in shared_networks:
|
|
shared_networks.append(k)
|
|
all_networks[k] = v.module
|
|
trainers.append(train_gen)
|
|
print("Networks being shared by trainers: ", shared_networks)
|
|
|
|
# Now, simply "iterate" through the trainers to accomplish training.
|
|
while True:
|
|
for trainer in trainers:
|
|
next(trainer)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
#parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_chained_structured_trans_invariance.yml')
|
|
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
|
args = parser.parse_args()
|
|
|
|
Loader, Dumper = OrderedYaml()
|
|
with open(args.opt, mode='r') as f:
|
|
opt = yaml.load(f, Loader=Loader)
|
|
opt = {
|
|
'trainer_options': ['../options/teco.yml', '../options/exd.yml']
|
|
}
|
|
main(opt, args.launcher) |