help='batch size')
parser.add_argument('--load_from',
type=str,
default=None,
help='directory to load from')
parser.add_argument('--loss',
type=str,
default='ls',
help='loss function: bce or ls')
parser.add_argument('--mode',
type=str,
default='train',
help='mode train or evaluate')
args = parser.parse_args()
lr = args.lr
noise_dim = args.noise_dim
base_dim = args.base_dim
loss = args.loss
opt = VanillaGANOptimizer()
runner = TrainingRunner('dcgan',
create_dogs_dataset(),
dogs.Generator(base_dim=base_dim,
noise_dim=noise_dim),
dogs.Discriminator(base_dim=base_dim),
VanillaGANOptimizer(dsc_lr=lr,
gen_lr=lr,
loss=loss),
args=args)
runner.execute(args)
import argparse
from dogsgan.data.dogs import create_dogs_dataset
from dogsgan.training.optimizers import WGANOptimizer
from dogsgan.training.runner import TrainingRunner
import dogsgan.models.dogs as dogs
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='run dcgan training')
parser.add_argument('--lr', type=float, default=1e-4 ,help='learning rate')
parser.add_argument('--clip', type=float, default=0.02, help='weight clip threshold')
parser.add_argument('--noise_dim', type=int, default=1024, help='noise dimension')
parser.add_argument('--base_dim', type=int, default=128, help='base dimension')
parser.add_argument('--batch_size', type=int, default=64, help='batch size')
parser.add_argument('--load_from', type=str, default=None, help='directory to load from')
parser.add_argument('--mode', type=str, default='train', help='mode train or evaluate')
args = parser.parse_args()
lr = args.lr
noise_dim = args.noise_dim
base_dim = args.base_dim
clip = args.clip
runner = TrainingRunner('wgan', create_dogs_dataset(),
dogs.Generator(noise_dim=noise_dim, base_dim=base_dim, affine=False),
dogs.Discriminator(base_dim=base_dim, affine=False, clip_size=clip), WGANOptimizer(lr=lr), args=args)
runner.execute(args)
import argparse
from dogsgan.data.dogs import create_dogs_dataset
from dogsgan.training.optimizers import WGANGPOptimizer
from dogsgan.training.runner import TrainingRunner
import dogsgan.models.dogs as dogs
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='run dcgan training')
parser.add_argument('--lr', type=float, default=1e-4 ,help='learning rate')
parser.add_argument('--l', type=float, default=10.0, help='gradient penalty coefficent')
parser.add_argument('--noise_dim', type=int, default=1024, help='noise dimension')
parser.add_argument('--base_dim', type=int, default=128, help='base dimension')
parser.add_argument('--batch_size', type=int, default=64, help='batch size')
parser.add_argument('--load_from', type=str, default=None, help='directory to load from')
parser.add_argument('--mode', type=str, default='train', help='mode train or evaluate')
args = parser.parse_args()
lr = args.lr
l = args.l
noise_dim = args.noise_dim
base_dim = args.base_dim
runner = TrainingRunner('wgan_gp', create_dogs_dataset(),
dogs.Generator(noise_dim=noise_dim, base_dim=base_dim),
dogs.Discriminator(base_dim=base_dim, batch_norm=False),
WGANGPOptimizer(lr=lr, l=l), args=args)
runner.execute(args)
"""
Copyright 2018 JetBrains, s.r.o
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from dogsgan.data.dogs import create_dogs_dataset
from dogsgan.training.optimizers import WGANGPOptimizer
from dogsgan.training.runner import TrainingRunner
import dogsgan.models.dogs as dogs
if __name__ == '__main__':
for l in [1.0, 2.0, 4.0, 8.0, 10.0, 16.0]:
runner = TrainingRunner(
f'wgan_gp-{l}', create_dogs_dataset(),
dogs.Generator(base_dim=224),
dogs.Discriminator(batch_norm=False, base_dim=224),
WGANGPOptimizer(l=l))
runner.train(epochs=100, batch_size=64)