/
train_mn.py
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/
train_mn.py
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import os, sys, time
import shutil
import yaml
import argparse
import chainer
from chainer import training
from chainer.training import extension
from chainer.training import extensions
import chainermn
import multiprocessing
sys.path.append(os.path.dirname(__file__))
from evaluation import sample_generate, sample_generate_conditional, sample_generate_light, calc_inception
import source.yaml_utils as yaml_utils
def create_result_dir(result_dir, config_path, config):
if not os.path.exists(result_dir):
os.makedirs(result_dir)
def copy_to_result_dir(fn, result_dir):
bfn = os.path.basename(fn)
shutil.copy(fn, '{}/{}'.format(result_dir, bfn))
copy_to_result_dir(config_path, result_dir)
copy_to_result_dir(
config.models['generator']['fn'], result_dir)
copy_to_result_dir(
config.models['discriminator']['fn'], result_dir)
copy_to_result_dir(
config.dataset['dataset_fn'], result_dir)
copy_to_result_dir(
config.updater['fn'], result_dir)
def load_models(config):
gen_conf = config.models['generator']
gen = yaml_utils.load_model(gen_conf['fn'], gen_conf['name'], gen_conf['args'])
dis_conf = config.models['discriminator']
dis = yaml_utils.load_model(dis_conf['fn'], dis_conf['name'], dis_conf['args'])
return gen, dis
def make_optimizer(model, comm, alpha=0.0002, beta1=0., beta2=0.9):
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(alpha=alpha, beta1=beta1, beta2=beta2), comm)
optimizer.setup(model)
return optimizer
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config_path', type=str, default='configs/base.yml', help='path to config file')
parser.add_argument('--data_dir', type=str, default='./data/imagenet')
parser.add_argument('--results_dir', type=str, default='./results/gans',
help='directory to save the results to')
parser.add_argument('--inception_model_path', type=str, default='./datasets/inception_model',
help='path to the inception model')
parser.add_argument('--snapshot', type=str, default='',
help='path to the snapshot')
parser.add_argument('--loaderjob', type=int,
help='number of parallel data loading processes')
parser.add_argument('--communicator', type=str,
default='hierarchical', help='Type of communicator')
args = parser.parse_args()
config = yaml_utils.Config(yaml.load(open(args.config_path)))
comm = chainermn.create_communicator(args.communicator)
device = comm.intra_rank
chainer.cuda.get_device_from_id(device).use()
print("init")
multiprocessing.set_start_method('forkserver')
if comm.rank == 0:
print('==========================================')
print('Using {} communicator'.format(args.communicator))
print('==========================================')
# Model
gen, dis = load_models(config)
gen.to_gpu()
dis.to_gpu()
models = {"gen": gen, "dis": dis}
# Optimizer
opt_gen = make_optimizer(gen, comm,
alpha=config.adam['alpha'], beta1=config.adam['beta1'], beta2=config.adam['beta2'])
opt_dis = make_optimizer(dis, comm,
alpha=config.adam['alpha'], beta1=config.adam['beta1'], beta2=config.adam['beta2'])
opts = {"opt_gen": opt_gen, "opt_dis": opt_dis}
# Dataset
if config['dataset'][
'dataset_name'] != 'CIFAR10Dataset': # Cifar10 dataset handler does not take "root" as argument.
config['dataset']['args']['root'] = args.data_dir
if comm.rank == 0:
dataset = yaml_utils.load_dataset(config)
else:
_ = yaml_utils.load_dataset(config) # Dummy, for adding path to the dataset module
dataset = None
dataset = chainermn.scatter_dataset(dataset, comm)
# Iterator
iterator = chainer.iterators.MultiprocessIterator(dataset, config.batchsize,
n_processes=args.loaderjob)
kwargs = config.updater['args'] if 'args' in config.updater else {}
kwargs.update({
'models': models,
'iterator': iterator,
'optimizer': opts,
'device': device,
})
updater = yaml_utils.load_updater_class(config)
updater = updater(**kwargs)
out = args.results_dir
if comm.rank == 0:
create_result_dir(out, args.config_path, config)
trainer = training.Trainer(updater, (config.iteration, 'iteration'), out=out)
report_keys = ["loss_dis", "loss_gen", "inception_mean", "inception_std"]
if comm.rank == 0:
# Set up logging
trainer.extend(extensions.snapshot(), trigger=(config.snapshot_interval, 'iteration'))
for m in models.values():
trainer.extend(extensions.snapshot_object(
m, m.__class__.__name__ + '_{.updater.iteration}.npz'), trigger=(config.snapshot_interval, 'iteration'))
trainer.extend(extensions.LogReport(keys=report_keys,
trigger=(config.display_interval, 'iteration')))
trainer.extend(extensions.PrintReport(report_keys), trigger=(config.display_interval, 'iteration'))
if gen.n_classes > 0:
trainer.extend(sample_generate_conditional(gen, out, n_classes=gen.n_classes),
trigger=(config.evaluation_interval, 'iteration'),
priority=extension.PRIORITY_WRITER)
else:
trainer.extend(sample_generate(gen, out),
trigger=(config.evaluation_interval, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.extend(sample_generate_light(gen, out, rows=10, cols=10),
trigger=(config.evaluation_interval // 10, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.extend(calc_inception(gen, n_ims=5000, splits=1, path=args.inception_model_path),
trigger=(config.evaluation_interval, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.extend(extensions.ProgressBar(update_interval=config.progressbar_interval))
ext_opt_gen = extensions.LinearShift('alpha', (config.adam['alpha'], 0.),
(config.iteration_decay_start, config.iteration), opt_gen)
ext_opt_dis = extensions.LinearShift('alpha', (config.adam['alpha'], 0.),
(config.iteration_decay_start, config.iteration), opt_dis)
trainer.extend(ext_opt_gen)
trainer.extend(ext_opt_dis)
if args.snapshot:
print("Resume training with snapshot:{}".format(args.snapshot))
chainer.serializers.load_npz(args.snapshot, trainer)
# Run the training
print("start training")
trainer.run()
if __name__ == '__main__':
main()