def main():
'''
main function, start point
'''
# 引数関連
parser = argparse.ArgumentParser()
parser.add_argument('--batchsize',
'-b',
type=int,
default=128,
help='Number of images in each mini-batch')
parser.add_argument('--learnrate',
'-l',
type=float,
default=0.001,
help='Learning rate for SGD')
parser.add_argument('--epoch',
'-e',
type=int,
default=100,
help='Number of sweeps over the dataset to train')
parser.add_argument('--gpu',
'-g',
type=int,
default=0,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--resume',
'-r',
default='',
help='Resume the training from snapshot')
parser.add_argument('--iter_parallel',
'-p',
action='store_true',
default=False,
help='loading dataset from disk')
parser.add_argument('--opt',
'-o',
type=str,
choices=('adam', 'sgd'),
default='adam')
parser.add_argument('--fsize', '-f', type=int, default=5)
parser.add_argument('--ch', '-c', type=int, default=4)
args = parser.parse_args()
# parameter出力
print("-=Learning Parameter=-")
print("# Max Epochs: {}".format(args.epoch))
print("# Batch Size: {}".format(args.batchsize))
print("# Learning Rate: {}".format(args.learnrate))
print("# Optimizer Method: {}".format(args.opt))
print("# Filter Size: {}".format(args.fsize))
print("# Channel Scale: {}".format(args.ch))
print('# Train Dataet: General 100')
if args.iter_parallel:
print("# Data Iters that loads in Parallel")
print("\n")
# 保存ディレクトリ
# save didrectory
model_dir_name = 'AEFINet_BN_opt_{}_ch_{}_fsize_{}'.format(
args.opt, args.ch, args.fsize)
outdir = path.join(ROOT_PATH, 'results', 'FI', 'AEFINet', model_dir_name)
if not path.exists(outdir):
os.makedirs(outdir)
with open(path.join(outdir, 'arg_param.txt'), 'w') as f:
for k, v in args.__dict__.items():
f.write('{}:{}\n'.format(k, v))
#loading dataset
print('# loading dataet(General100_train, General100_test) ...')
if args.iter_parallel:
train = ds.SequenceDataset(dataset='train')
test = ds.SequenceDataset(dataset='test')
else:
train = ds.SequenceDatasetOnMem(dataset='train')
test = ds.SequenceDatasetOnMem(dataset='test')
# prepare model
model = N.GenEvaluator(N.AEFINetBN(f_size=args.fsize, ch=args.ch))
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
# setup optimizer
if args.opt == 'adam':
optimizer = chainer.optimizers.Adam(alpha=args.learnrate)
elif args.opt == 'sgd':
optimizer = chainer.optimizers.MomentumSGD(lr=args.learnrate,
momentum=0.9)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001))
# setup iter
if args.iter_parallel:
train_iter = chainer.iterators.MultiprocessIterator(train,
args.batchsize,
n_processes=8)
test_iter = chainer.iterators.MultiprocessIterator(test,
args.batchsize,
repeat=False,
shuffle=False,
n_processes=8)
else:
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test,
args.batchsize,
repeat=False,
shuffle=False)
# setup trainer
updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=outdir)
# # eval test data
trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu))
# dump loss graph
trainer.extend(extensions.dump_graph('main/loss'))
# lr shift
if args.opt == 'sgd':
trainer.extend(extensions.ExponentialShift("lr", 0.1),
trigger=(100, 'epoch'))
elif args.opt == 'adam':
trainer.extend(extensions.ExponentialShift("alpha", 0.1),
trigger=(50, 'epoch'))
# save snapshot
trainer.extend(extensions.snapshot(), trigger=(10, 'epoch'))
trainer.extend(extensions.snapshot_object(
model, 'model_snapshot_{.updater.epoch}'),
trigger=(10, 'epoch'))
# log report
trainer.extend(extensions.LogReport())
trainer.extend(extensions.observe_lr(), trigger=(1, 'epoch'))
# plot loss graph
trainer.extend(
extensions.PlotReport(['main/loss', 'validation/main/loss'],
'epoch',
file_name='loss.png'))
# plot acc graph
trainer.extend(
extensions.PlotReport(['main/PSNR', 'validation/main/PSNR'],
'epoch',
file_name='PSNR.png'))
# print info
trainer.extend(
extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'main/PSNR',
'validation/main/PSNR', 'lr', 'elapsed_time'
]))
# print progbar
trainer.extend(extensions.ProgressBar())
# [ChainerUI] enable to send commands from ChainerUI
trainer.extend(CommandsExtension())
# [ChainerUI] save 'args' to show experimental conditions
save_args(args, outdir)
if args.resume:
# Resume from a snapshot
chainer.serializers.load_npz(args.resume, trainer)
trainer.run()
# save final model
model_outdir = path.join(ROOT_PATH, 'models', model_dir_name)
if not path.exists(model_outdir):
os.makedirs(model_outdir)
model_name = 'AEFINet_opt_{}_ch_{}_fsize_{}.npz'.format(
args.opt, args.ch, args.fsize)
chainer.serializers.save_npz(path.join(model_outdir, model_name), model)
model_parameter = {
'name': 'AEFINet',
'parameter': {
'f_size': args.fsize,
'ch': args.ch
}
}
with open(path.join(model_outdir, 'model_parameter.json'), 'w') as f:
json.dump(model_parameter, f)
def main():
'''
main function, start point
'''
# 引数関連
parser = argparse.ArgumentParser()
parser.add_argument('--batchsize',
'-b',
type=int,
default=128,
help='Number of images in each mini-batch')
parser.add_argument('--learnrate',
'-l',
type=float,
default=0.001,
help='Learning rate for SGD')
parser.add_argument('--epoch',
'-e',
type=int,
default=100,
help='Number of sweeps over the dataset to train')
parser.add_argument('--gpu0',
'-g',
type=int,
default=0,
help='GPU1 ID (negative value indicates CPU)')
parser.add_argument('--gpu1',
'-G',
type=int,
default=2,
help='GPU2 ID (negative value indicates CPU)')
parser.add_argument('--resume',
'-r',
default='',
help='Resume the training from snapshot')
parser.add_argument('--iter_parallel',
'-p',
action='store_true',
default=False,
help='loading dataset from disk')
parser.add_argument('--opt',
'-o',
type=str,
choices=('adam', 'sgd'),
default='adam')
parser.add_argument('--fsize', '-f', type=int, default=5)
parser.add_argument('--ch', '-c', type=int, default=4)
args = parser.parse_args()
# parameter出力
print("-=Learning Parameter=-")
print("# Max Epochs: {}".format(args.epoch))
print("# Batch Size: {}".format(args.batchsize))
print("# Learning Rate: {}".format(args.learnrate))
print("# Optimizer Method: {}".format(args.opt))
print("# Filter Size: {}".format(args.fsize))
print("# Channel Scale: {}".format(args.ch))
print('# Train Dataet: General 100')
if args.iter_parallel:
print("# Data Iters that loads in Parallel")
print("\n")
# 保存ディレクトリ
# make result dir
network_name = 'AEFINetConcat'
model_name = 'AEFINet_Test_opt_{}_ch_{}_fsize_{}'.format(
args.opt, args.ch, args.fsize)
outdir = path.join(ROOT_PATH, 'results', 'FI', 'AEFINet', model_name)
util.make_result_dir(args, outdir)
#loading dataset
if args.iter_parallel:
train = datasets.SequenceDataset(
dataset='UCF101_train_size64_frame3_group10_max100_p')
test = datasets.SequenceDataset(
dataset='UCF101_test_size64_frame3_group25_max5_p')
else:
train = datasets.SequenceDatasetOnMem(
dataset='UCF101_train_size64_frame3_group10_max100_p')
test = datasets.SequenceDatasetOnMem(
dataset='UCF101_test_size64_frame3_group25_max5_p')
# prepare model
chainer.cuda.get_device_from_id(args.gpu0).use()
model = N.GenEvaluator(N.AEFINetConcat(f_size=args.fsize, ch=args.ch))
# setup optimizer
if args.opt == 'adam':
optimizer = chainer.optimizers.Adam(alpha=args.learnrate)
elif args.opt == 'sgd':
optimizer = chainer.optimizers.MomentumSGD(lr=args.learnrate,
momentum=0.9)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001))
# setup iter
if args.iter_parallel:
train_iter = chainer.iterators.MultiprocessIterator(train,
args.batchsize,
n_processes=8)
test_iter = chainer.iterators.MultiprocessIterator(test,
args.batchsize,
repeat=False,
shuffle=False,
n_processes=8)
else:
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test,
args.batchsize,
repeat=False,
shuffle=False)
# setup trainer
updater = training.ParallelUpdater(
train_iter,
optimizer,
devices={
'main': args.gpu0,
'second': args.gpu1
},
)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=outdir)
# # eval test data
trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu0))
# dump loss graph
trainer.extend(extensions.dump_graph('main/loss'))
# lr shift
if args.opt == 'sgd':
trainer.extend(extensions.ExponentialShift("lr", 0.1),
trigger=(100, 'epoch'))
elif args.opt == 'adam':
trainer.extend(extensions.ExponentialShift("alpha", 0.1),
trigger=(100, 'epoch'))
# save snapshot
trainer.extend(extensions.snapshot(), trigger=(10, 'epoch'))
trainer.extend(extensions.snapshot_object(
model, 'model_snapshot_{.updater.epoch}'),
trigger=(10, 'epoch'))
# log report
trainer.extend(extensions.LogReport())
trainer.extend(extensions.observe_lr(), trigger=(1, 'epoch'))
# plot loss graph
trainer.extend(
extensions.PlotReport(['main/loss', 'validation/main/loss'],
'epoch',
file_name='loss.png'))
# plot acc graph
trainer.extend(
extensions.PlotReport(['main/PSNR', 'validation/main/PSNR'],
'epoch',
file_name='PSNR.png'))
# print info
trainer.extend(
extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'main/PSNR',
'validation/main/PSNR', 'lr', 'elapsed_time'
]))
# print progbar
trainer.extend(extensions.ProgressBar())
# [ChainerUI] enable to send commands from ChainerUI
trainer.extend(CommandsExtension())
# [ChainerUI] save 'args' to show experimental conditions
save_args(args, outdir)
if args.resume:
# Resume from a snapshot
chainer.serializers.load_npz(args.resume, trainer)
# start train
trainer.run()
# save final model
util.save_trained_model(model_name,
model,
network_name,
f_size=args.fsize,
ch=args.ch)