def main():
gpu_id = 0
batchsize = 10
report_keys = ["loss_dis", "loss_gen"]
train_dataset = datasets.LabeledImageDataset(str(argv[1]))
train_iter = iterators.SerialIterator(train_dataset, batchsize)
models = []
generator = Generator()
discriminator = Discriminator()
opts = {}
opts["opt_gen"] = setup_adam_optimizer(generator)
opts["opt_dis"] = setup_adam_optimizer(discriminator)
models = [generator, discriminator]
chainer.cuda.get_device_from_id(gpu_id).use()
print("use gpu {}".format(gpu_id))
for m in models:
m.to_gpu()
updater_args = {
"iterator": {
'main': train_iter
},
"device": gpu_id,
"optimizer": opts,
"models": models
}
output = 'result'
display_interval = 20
evaluation_interval = 1000
max_iter = 10000
updater = Updater(**updater_args)
trainer = training.Trainer(updater, (max_iter, 'iteration'), out=output)
trainer.extend(
extensions.LogReport(keys=report_keys,
trigger=(display_interval, 'iteration')))
trainer.extend(extensions.PrintReport(report_keys),
trigger=(display_interval, 'iteration'))
trainer.extend(sample_generate(generator, output),
trigger=(evaluation_interval, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.run()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config_path',
type=str,
default='configs/base.yml',
help='path to config file')
parser.add_argument('--gpu',
type=int,
default=0,
help='index of gpu to be used')
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')
args = parser.parse_args()
config = yaml_utils.Config(yaml.load(open(args.config_path)))
chainer.cuda.get_device_from_id(args.gpu).use()
gen, dis = load_models(config)
gen.to_gpu(device=args.gpu)
dis.to_gpu(device=args.gpu)
seed_weights(gen)
seed_weights(dis)
print(np.sum(gen.block2.c2.b))
_ = input()
models = {"gen": gen, "dis": dis}
# Optimizer
opt_gen = make_optimizer(gen,
alpha=config.adam['alpha'],
beta1=config.adam['beta1'],
beta2=config.adam['beta2'])
opt_dis = make_optimizer(dis,
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
dataset = yaml_utils.load_dataset(config)
# 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,
})
updater = yaml_utils.load_updater_class(config)
updater = updater(**kwargs)
out = args.results_dir
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"]
# 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()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config_path',
type=str,
default='configs/sr.yml',
help='path to config file')
parser.add_argument('--gpu',
type=int,
default=0,
help='index of gpu to be used')
parser.add_argument('--results_dir',
type=str,
default='./results',
help='directory to save the results to')
parser.add_argument(
'--inception_model_path',
type=str,
default='./datasets/inception_model/inception_score.model',
help='path to the inception model')
parser.add_argument(
'--stat_file',
type=str,
default='./datasets/inception_model/fid_stats_cifar10_train.npz',
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')
args = parser.parse_args()
config = yaml_utils.Config(yaml.load(open(args.config_path)))
chainer.cuda.get_device_from_id(args.gpu).use()
# set up the model
devices = {'main': 0, 'second': 1, 'third': 2, 'fourth': 3}
gen, dis = load_models(config)
model = {"gen": gen, "dis": dis}
names = list(six.iterkeys(devices))
try:
names.remove('main')
except ValueError:
raise KeyError("devices must contain a 'main' key.")
models = {'main': model}
for name in names:
g = copy.deepcopy(model['gen'])
d = copy.deepcopy(model['dis'])
if devices[name] >= 0:
g.to_gpu(device=devices[name])
d.to_gpu(device=devices[name])
models[name] = {"gen": g, "dis": d}
if devices['main'] >= 0:
models['main']['gen'].to_gpu(device=devices['main'])
models['main']['dis'].to_gpu(device=devices['main'])
links = [[name, link] for name, link in sorted(dis.namedlinks())]
for name, link in links:
print(name)
links = [[name, link] for name, link in sorted(gen.namedlinks())]
for name, link in links:
print(name)
# Optimizer
opt_gen = make_optimizer(models['main']['gen'],
alpha=config.adam['alpha'],
beta1=config.adam['beta1'],
beta2=config.adam['beta2'])
opt_dis = make_optimizer(models['main']['dis'],
alpha=config.adam['alpha'],
beta1=config.adam['beta1'],
beta2=config.adam['beta2'])
opts = {"opt_gen": opt_gen, "opt_dis": opt_dis}
# Dataset
dataset = yaml_utils.load_dataset(config)
# Iterator
iterator = chainer.iterators.MultiprocessIterator(
dataset, config.batchsize, n_processes=args.loaderjob)
kwargs = config.updater['args'] if 'args' in config.updater else {}
kwargs.update({
'devices': devices,
'models': models,
'iterator': iterator,
'optimizer': opts
})
updater = yaml_utils.load_updater_class(config)
updater = updater(**kwargs)
out = args.results_dir
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", "FID"]
# Set up logging
trainer.extend(extensions.snapshot(),
trigger=(config.snapshot_interval, 'iteration'))
for m in models['main'].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(models['main']['gen'],
out,
n_classes=gen.n_classes),
trigger=(config.evaluation_interval, 'iteration'),
priority=extension.PRIORITY_WRITER)
else:
trainer.extend(sample_generate(models['main']['gen'], out),
trigger=(config.evaluation_interval, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.extend(sample_generate_light(models['main']['gen'],
out,
rows=10,
cols=10),
trigger=(config.evaluation_interval // 10, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.extend(calc_inception(models['main']['gen'],
n_ims=5000,
splits=1,
dst=args.results_dir,
path=args.inception_model_path),
trigger=(config.evaluation_interval, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.extend(calc_FID(models['main']['gen'],
n_ims=5000,
dst=args.results_dir,
path=args.inception_model_path,
stat_file=args.stat_file),
trigger=(config.evaluation_interval, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.extend(monitor_largest_singular_values(models['main']['dis'],
dst=args.results_dir),
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()
def main():
parser = argparse.ArgumentParser(description='Train script')
parser.add_argument('--data_path',
type=str,
default="data/datasets/cifar10/train",
help='dataset directory path')
parser.add_argument('--class_name',
'-class',
type=str,
default='all_class',
help='class name (default: all_class(str))')
parser.add_argument('--num_workers',
type=int,
default=4,
help='number of parallel data loading processes')
parser.add_argument('--batchsize', '-b', type=int, default=16)
parser.add_argument('--max_iter', '-m', type=int, default=40000)
parser.add_argument('--gpu',
'-g',
type=int,
default=0,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out',
'-o',
default='result',
help='Directory to output the result')
parser.add_argument('--snapshot_interval',
type=int,
default=2500,
help='Interval of snapshot')
parser.add_argument('--evaluation_interval',
type=int,
default=5000,
help='Interval of evaluation')
parser.add_argument('--out_image_interval',
type=int,
default=1250,
help='Interval of evaluation')
parser.add_argument('--stage_interval',
type=int,
default=40000,
help='Interval of stage progress')
parser.add_argument('--display_interval',
type=int,
default=100,
help='Interval of displaying log to console')
parser.add_argument(
'--n_dis',
type=int,
default=1,
help='number of discriminator update per generator update')
parser.add_argument('--lam',
type=float,
default=10,
help='gradient penalty')
parser.add_argument('--gamma',
type=float,
default=750,
help='gradient penalty')
parser.add_argument('--pooling_comp',
type=float,
default=1.0,
help='compensation')
parser.add_argument('--pretrained_generator', type=str, default="")
parser.add_argument('--pretrained_discriminator', type=str, default="")
parser.add_argument('--initial_stage', type=float, default=0.0)
parser.add_argument('--generator_smoothing', type=float, default=0.999)
args = parser.parse_args()
record_setting(args.out)
report_keys = [
"stage", "loss_dis", "loss_gp", "loss_gen", "g", "inception_mean",
"inception_std", "FID"
]
max_iter = args.max_iter
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
generator = Generator()
generator_smooth = Generator()
discriminator = Discriminator(pooling_comp=args.pooling_comp)
# select GPU
if args.gpu >= 0:
generator.to_gpu()
generator_smooth.to_gpu()
discriminator.to_gpu()
print("use gpu {}".format(args.gpu))
if args.pretrained_generator != "":
chainer.serializers.load_npz(args.pretrained_generator, generator)
if args.pretrained_discriminator != "":
chainer.serializers.load_npz(args.pretrained_discriminator,
discriminator)
copy_param(generator_smooth, generator)
# Setup an optimizer
def make_optimizer(model, alpha=0.001, beta1=0.0, beta2=0.99):
optimizer = chainer.optimizers.Adam(alpha=alpha,
beta1=beta1,
beta2=beta2)
optimizer.setup(model)
# optimizer.add_hook(chainer.optimizer.WeightDecay(0.00001), 'hook_dec')
return optimizer
opt_gen = make_optimizer(generator)
opt_dis = make_optimizer(discriminator)
if args.class_name == 'all_class':
data_path = args.data_path
one_class_flag = False
else:
data_path = os.path.join(args.data_path, args.class_name)
one_class_flag = True
train_dataset = ImageDataset(data_path, one_class_flag=one_class_flag)
train_iter = chainer.iterators.MultiprocessIterator(
train_dataset, args.batchsize, n_processes=args.num_workers)
# Set up a trainer
updater = Updater(models=(generator, discriminator, generator_smooth),
iterator={'main': train_iter},
optimizer={
'opt_gen': opt_gen,
'opt_dis': opt_dis
},
device=args.gpu,
n_dis=args.n_dis,
lam=args.lam,
gamma=args.gamma,
smoothing=args.generator_smoothing,
initial_stage=args.initial_stage,
stage_interval=args.stage_interval)
trainer = training.Trainer(updater, (max_iter, 'iteration'), out=args.out)
trainer.extend(extensions.snapshot_object(
generator, 'generator_{.updater.iteration}.npz'),
trigger=(args.snapshot_interval, 'iteration'))
trainer.extend(extensions.snapshot_object(
generator_smooth, 'generator_smooth_{.updater.iteration}.npz'),
trigger=(args.snapshot_interval, 'iteration'))
trainer.extend(extensions.snapshot_object(
discriminator, 'discriminator_{.updater.iteration}.npz'),
trigger=(args.snapshot_interval, 'iteration'))
trainer.extend(
extensions.LogReport(keys=report_keys,
trigger=(args.display_interval, 'iteration')))
trainer.extend(extensions.PrintReport(report_keys),
trigger=(args.display_interval, 'iteration'))
trainer.extend(sample_generate(generator_smooth, args.out),
trigger=(args.out_image_interval, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.extend(sample_generate_light(generator_smooth, args.out),
trigger=(args.evaluation_interval // 10, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.extend(calc_inception(generator_smooth),
trigger=(args.evaluation_interval, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.extend(calc_FID(generator_smooth),
trigger=(args.evaluation_interval, 'iteration'),
priority=extension.PRIORITY_WRITER)
trainer.extend(extensions.ProgressBar(update_interval=10))
# Run the training
trainer.run()
def main():
config = yaml_utils.Config(yaml.load(open(args.config_path)))
chainer.cuda.get_device_from_id(args.gpu).use()
from model_loader import gen, dis
models = {"gen": gen, "dis": dis}
# Optimizer
opt_gen = make_optimizer(
gen, alpha=config.adam['alpha'], beta1=config.adam['beta1'], beta2=config.adam['beta2'])
opt_dis = make_optimizer(
dis, alpha=config.adam['alpha'], beta1=config.adam['beta1'], beta2=config.adam['beta2'])
opts = {"opt_gen": opt_gen, "opt_dis": opt_dis}
# Dataset
# Cifar10 and STL10 dataset handler does not take "root" as argument.
if config['dataset']['dataset_name'] != 'CIFAR10Dataset' and \
config['dataset']['dataset_name'] != 'STL10Dataset':
config['dataset']['args']['root'] = args.data_dir
dataset = yaml_utils.load_dataset(config)
# 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,
})
updater = yaml_utils.load_updater_class(config)
updater = updater(**kwargs)
out = args.results_dir
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", "FID_mean", "FID_std"]
# 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=50000, dst=out, splits=10, path=args.inception_model_path),
# trigger=(config.evaluation_interval, 'iteration'),
# priority=extension.PRIORITY_WRITER)
trainer.extend(calc_inception_and_FID(gen, n_ims=50000, dst=out, path=args.inception_model_path, splits=10, stat_file=args.FID_stat_file),
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()