def load_from_dir(root_dir, model_index=None, G_weights=None, shift_in_w=True):
args = json.load(open(os.path.join(root_dir, 'args.json')))
models_dir = os.path.join(root_dir, 'models')
if model_index is None:
models = os.listdir(models_dir)
model_index = max([
int(name.split('.')[0].split('_')[-1]) for name in models
if name.startswith('deformator')
])
if G_weights is None:
G_weights = args['gan_weights']
if G_weights is None or not os.path.isfile(G_weights):
print('Using default local G weights')
G_weights = WEIGHTS[args['gan_type']]
if isinstance(G_weights, dict):
G_weights = G_weights[str(args['resolution'])]
if 'resolution' not in args.keys():
args['resolution'] = 128
G = load_generator(args, G_weights, shift_in_w)
deformator = LatentDeformator(
shift_dim=G.dim_shift,
input_dim=args['directions_count']
if 'directions_count' in args.keys() else None,
out_dim=args['max_latent_dim']
if 'max_latent_dim' in args.keys() else None,
type=DEFORMATOR_TYPE_DICT[args['deformator']])
if 'shift_predictor' not in args.keys(
) or args['shift_predictor'] == 'ResNet':
shift_predictor = LatentShiftPredictor(G.dim_shift)
elif args['shift_predictor'] == 'LeNet':
shift_predictor = LeNetShiftPredictor(
G.dim_shift, 1 if args['gan_type'] == 'SN_MNIST' else 3)
deformator_model_path = os.path.join(
models_dir, 'deformator_{}.pt'.format(model_index))
shift_model_path = os.path.join(
models_dir, 'shift_predictor_{}.pt'.format(model_index))
if os.path.isfile(deformator_model_path):
deformator.load_state_dict(
torch.load(deformator_model_path,
map_location=torch.device('cpu')))
if os.path.isfile(shift_model_path):
shift_predictor.load_state_dict(
torch.load(shift_model_path, map_location=torch.device('cpu')))
setattr(
deformator, 'annotation',
load_human_annotation(os.path.join(root_dir, HUMAN_ANNOTATION_FILE)))
return deformator.eval().cuda(), G.eval().cuda(), shift_predictor.eval(
).cuda()
def load_from_dir(root_dir, model_index=None, G_weights=None, verbose=False):
args = json.load(open(os.path.join(root_dir, 'args.json')))
models_dir = os.path.join(root_dir, 'models')
if model_index is None:
models = os.listdir(models_dir)
model_index = max([
int(name.split('.')[0].split('_')[-1]) for name in models
if name.startswith('deformator')
])
if verbose:
print('using max index {}'.format(model_index))
if G_weights is None:
G_weights = args['gan_weights']
if G_weights is None or not os.path.isfile(G_weights):
if verbose:
print('Using default local G weights')
G_weights = WEIGHTS[args['gan_type']]
if args['gan_type'] == 'BigGAN':
G = make_big_gan(G_weights, args['target_class']).eval()
elif args['gan_type'] in ['ProgGAN', 'PGGAN']:
G = make_proggan(G_weights)
else:
G = make_external(G_weights)
deformator = LatentDeformator(
G.dim_z, type=DEFORMATOR_TYPE_DICT[args['deformator']])
if 'shift_predictor' not in args.keys(
) or args['shift_predictor'] == 'ResNet':
shift_predictor = ResNetShiftPredictor(G.dim_z)
elif args['shift_predictor'] == 'LeNet':
shift_predictor = LeNetShiftPredictor(
G.dim_z, 1 if args['gan_type'] == 'SN_MNIST' else 3)
deformator_model_path = os.path.join(
models_dir, 'deformator_{}.pt'.format(model_index))
shift_model_path = os.path.join(
models_dir, 'shift_predictor_{}.pt'.format(model_index))
if os.path.isfile(deformator_model_path):
deformator.load_state_dict(torch.load(deformator_model_path))
if os.path.isfile(shift_model_path):
shift_predictor.load_state_dict(torch.load(shift_model_path))
# try to load dims annotation
directions_json = os.path.join(root_dir, 'directions.json')
if os.path.isfile(directions_json):
with open(directions_json, 'r') as f:
directions_dict = json.load(f, object_pairs_hook=OrderedDict)
setattr(deformator, 'directions_dict', directions_dict)
return deformator.eval().cuda(), G.eval().cuda(), shift_predictor.eval(
).cuda()
def main():
parser = argparse.ArgumentParser(
description='GAN-based unsupervised segmentation train')
parser.add_argument('--out', type=str, required=True)
parser.add_argument('--gan_weights', type=str, default=WEIGHTS['BigGAN'])
parser.add_argument('--deformator_weights', type=str, required=True)
parser.add_argument('--deformator_type',
type=str,
choices=DEFORMATOR_TYPE_DICT.keys(),
required=True)
parser.add_argument('--background_dim', type=int, required=True)
parser.add_argument('--classes',
type=int,
nargs='*',
default=list(range(1000)))
parser.add_argument('--device', type=int, default=0)
parser.add_argument('--seed', type=int, default=2)
parser.add_argument('--val_images_dir', type=str, default=None)
parser.add_argument('--val_masks_dir', type=str, default=None)
for key, val in SegmentationTrainParams().__dict__.items():
val_type = type(val) if key is not 'synthezing' else str
parser.add_argument('--{}'.format(key), type=val_type, default=None)
args = parser.parse_args()
torch.random.manual_seed(args.seed)
torch.cuda.set_device(args.device)
# save run p
save_command_run_params(args)
if len(args.classes) == 0:
print('using all ImageNet')
args.classes = list(range(1000))
G = make_big_gan(args.gan_weights, args.classes).eval().cuda()
deformator = LatentDeformator(
G.dim_z, type=DEFORMATOR_TYPE_DICT[args.deformator_type])
deformator.load_state_dict(
torch.load(args.deformator_weights, map_location=torch.device('cpu')))
deformator.eval().cuda()
model = UNet().train().cuda()
train_params = SegmentationTrainParams(**args.__dict__)
print(f'run train with p: {train_params.__dict__}')
train_segmentation(G,
deformator,
model,
train_params,
args.background_dim,
args.out,
val_dirs=[args.val_images_dir, args.val_masks_dir])
def objective(trial: optuna.trial.Trial):
print(f"[Optuna]: Trial #{trial.number}")
G = load_generator(args.__dict__, weights_path, args.w_shift)
params = Params(**args.__dict__)
params.batch_size = 128
params.directions_count = 200
params.deformator_lr = trial.suggest_float("deformator_lr", 1e-4, 1e-4, log=True)
params.shift_predictor_lr = params.deformator_lr
# params.torch_grad = True if trial.suggest_int("torch_grad", 0, 1) else False
deformator = LatentDeformator(shift_dim=G.dim_shift,
input_dim=params.directions_count,
out_dim=params.max_latent_dim,
type=DEFORMATOR_TYPE_DICT[args.deformator],
random_init=args.deformator_random_init
).cuda()
# deformator = MyDeformator(48*8*8, 48*8*8)
if args.shift_predictor == 'ResNet':
shift_predictor = LatentShiftPredictor(params.directions_count, args.shift_predictor_size).cuda()
elif args.shift_predictor == 'LeNet':
shift_predictor = LeNetShiftPredictor(
params.directions_count, 1 if args.gan_type == 'SN_MNIST' else 3).cuda()
# training
args.shift_distribution = SHIFT_DISTRIDUTION_DICT[args.shift_distribution_key]
# update dims with respect to the deformator if some of params are None
params.directions_count = int(deformator.input_dim)
params.max_latent_dim = int(deformator.out_dim)
trainer = Trainer(params, out_dir=args.out, verbose=True)
loss = trainer.train(G, deformator, shift_predictor, multi_gpu=args.multi_gpu, trial=trial)
# my_save_results_charts(G, deformator, params, trainer.log_dir)
compute_DVN(G, deformator, f'DVN_{args.gan_type}_{args.deformator}_{params.directions_count}.pt')
return loss
def main():
parser = argparse.ArgumentParser(description='Latent space rectification')
for key, val in Params().__dict__.items():
target_type = type(val) if val is not None else int
parser.add_argument('--{}'.format(key), type=target_type, default=None)
parser.add_argument('--out', type=str, required=True, help='results directory')
parser.add_argument('--gan_type', type=str, choices=WEIGHTS.keys(), help='generator model type')
parser.add_argument('--gan_weights', type=str, default=None, help='path to generator weights')
parser.add_argument('--resolution', type=int, required=True)
parser.add_argument('--target_class', nargs='+', type=int, default=[239],
help='classes to use for conditional GANs')
parser.add_argument('--deformator', type=str, default='ortho',
choices=DEFORMATOR_TYPE_DICT.keys(), help='deformator type')
parser.add_argument('--deformator_random_init', type=bool, default=True)
parser.add_argument('--deformator_target', type=str, default='latent',
choices=DEFORMATOR_TARGET_DICT.keys())
parser.add_argument('--deformator_conv_layer_index', type=int, default=3)
parser.add_argument('--basis_vectors_path', type=str)
parser.add_argument('--shift_predictor_size', type=int, help='reconstructor resolution')
parser.add_argument('--shift_predictor', type=str,
choices=['ResNet', 'LeNet'], default='ResNet', help='reconstructor type')
parser.add_argument('--shift_distribution_key', type=str,
choices=SHIFT_DISTRIDUTION_DICT.keys())
parser.add_argument('--make_videos', type=bool, default=True)
parser.add_argument('--samples_for_videos', type=str, default=None)
parser.add_argument('--video_interpolate', type=int, default=None)
parser.add_argument('--seed', type=int, default=2)
parser.add_argument('--device', type=int, default=0)
parser.add_argument('--multi_gpu', type=bool, default=False,
help='Run generator in parallel. Be aware of old pytorch versions:\
https://github.com/pytorch/pytorch/issues/17345')
# model-specific
parser.add_argument('--w_shift', type=bool, default=True,
help='latent directions search in w-space for StyleGAN')
args = parser.parse_args()
torch.cuda.set_device(args.device)
random.seed(args.seed)
torch.random.manual_seed(args.seed)
save_command_run_params(args)
# init models
if args.gan_weights is not None:
weights_path = args.gan_weights
else:
weights_path = WEIGHTS[args.gan_type]
G = load_generator(args.__dict__, weights_path, args.w_shift)
if args.deformator_target == 'latent':
deformator = LatentDeformator(
shift_dim=G.dim_shift,
input_dim=args.directions_count,
out_dim=args.max_latent_dim,
type=DEFORMATOR_TYPE_DICT[args.deformator],
random_init=args.deformator_random_init
).cuda()
elif args.deformator_target == 'weight_svd':
deformator = WeightDeformatorSVDBasis(
generator=G,
conv_layer_ix=args.deformator_conv_layer_index,
directions_count=args.directions_count
).cuda()
G = G.cuda()
dim_shift = args.directions_count
elif args.deformator_target == 'weight_fixedbasis':
assert args.basis_vectors_path is not None
deformator = WeightDeformatorFixedBasis(
generator=G,
conv_layer_ix=args.deformator_conv_layer_index,
directions_count=args.directions_count,
basis_vectors_path=args.basis_vectors_path
).cuda()
G = G.cuda()
dim_shift = args.directions_count
else:
raise ValueError("Unknown deformator_target")
if args.shift_predictor == 'ResNet':
shift_predictor = LatentShiftPredictor(
dim_shift, args.shift_predictor_size).cuda()
elif args.shift_predictor == 'LeNet':
shift_predictor = LeNetShiftPredictor(
dim_shift, 1 if args.gan_type == 'SN_MNIST' else 3).cuda()
# training
args.shift_distribution = SHIFT_DISTRIDUTION_DICT[args.shift_distribution_key]
params = Params(**args.__dict__)
# update dims with respect to the deformator if some of params are None
if args.deformator_target == 'latent':
params.directions_count = int(deformator.input_dim)
params.max_latent_dim = int(deformator.out_dim)
trainer = Trainer(params, out_dir=args.out)
trainer.train(G, deformator, shift_predictor, multi_gpu=args.multi_gpu)
if args.make_videos:
if 'weight_' not in args.deformator_target:
sys.stderr.write("Making video is available only for weight deformations.\n")
else:
generate_videos(args, G, deformator)
if args.deformator_target == 'latent':
save_results_charts(G, deformator, params, trainer.log_dir)
def main():
parser = argparse.ArgumentParser(
description='GAN-based unsupervised segmentation train')
parser.add_argument('--args',
type=str,
default=None,
help='json with all arguments')
parser.add_argument('--out', type=str, required=True)
parser.add_argument('--gan_weights', type=str, default=WEIGHTS['BigGAN'])
parser.add_argument('--deformator_weights', type=str, required=True)
parser.add_argument('--deformator_type',
type=str,
choices=DEFORMATOR_TYPE_DICT.keys(),
required=True)
parser.add_argument('--background_dim', type=int, required=True)
parser.add_argument('--classes',
type=int,
nargs='*',
default=list(range(1000)))
parser.add_argument('--device', type=int, default=0)
parser.add_argument('--seed', type=int, default=2)
parser.add_argument('--val_images_dir', type=str)
parser.add_argument('--val_masks_dir', type=str)
for key, val in SegmentationTrainParams().__dict__.items():
parser.add_argument('--{}'.format(key), type=type(val), default=None)
args = parser.parse_args()
torch.random.manual_seed(args.seed)
torch.cuda.set_device(args.device)
if args.args is not None:
with open(args.args) as args_json:
args_dict = json.load(args_json)
args.__dict__.update(**args_dict)
# save run params
if not os.path.isdir(args.out):
os.makedirs(args.out)
with open(os.path.join(args.out, 'args.json'), 'w') as args_file:
json.dump(args.__dict__, args_file)
with open(os.path.join(args.out, 'command.sh'), 'w') as command_file:
command_file.write(' '.join(sys.argv))
command_file.write('\n')
if len(args.classes) == 0:
print('using all ImageNet')
args.classes = list(range(1000))
G = make_big_gan(args.gan_weights, args.classes).eval().cuda()
deformator = LatentDeformator(
G.dim_z, type=DEFORMATOR_TYPE_DICT[args.deformator_type])
deformator.load_state_dict(
torch.load(args.deformator_weights, map_location=torch.device('cpu')))
deformator.eval().cuda()
model = UNet().train().cuda()
train_params = SegmentationTrainParams(**args.__dict__)
print('run train with params: {}'.format(train_params.__dict__))
train_segmentation(G, deformator, model, train_params, args.background_dim,
args.out)
if args.val_images_dir is not None:
evaluate(model, args.val_images_dir, args.val_masks_dir,
os.path.join(args.out, 'score.json'), 128)
def main():
tOption = TrainOptions()
for key, val in Params().__dict__.items():
tOption.parser.add_argument('--{}'.format(key),
type=type(val),
default=val)
tOption.parser.add_argument('--args',
type=str,
default=None,
help='json with all arguments')
tOption.parser.add_argument('--out', type=str, default='./output')
tOption.parser.add_argument('--gan_type',
type=str,
choices=WEIGHTS.keys(),
default='StyleGAN')
tOption.parser.add_argument('--gan_weights', type=str, default=None)
tOption.parser.add_argument('--target_class', type=int, default=239)
tOption.parser.add_argument('--json', type=str)
tOption.parser.add_argument('--deformator',
type=str,
default='proj',
choices=DEFORMATOR_TYPE_DICT.keys())
tOption.parser.add_argument('--deformator_random_init',
type=bool,
default=False)
tOption.parser.add_argument('--shift_predictor_size', type=int)
tOption.parser.add_argument('--shift_predictor',
type=str,
choices=['ResNet', 'LeNet'],
default='ResNet')
tOption.parser.add_argument('--shift_distribution_key',
type=str,
choices=SHIFT_DISTRIDUTION_DICT.keys())
tOption.parser.add_argument('--seed', type=int, default=2)
tOption.parser.add_argument('--device', type=int, default=0)
tOption.parser.add_argument('--continue_train', type=bool, default=False)
tOption.parser.add_argument('--deformator_path',
type=str,
default='output/models/deformator_90000.pt')
tOption.parser.add_argument(
'--shift_predictor_path',
type=str,
default='output/models/shift_predictor_190000.pt')
args = tOption.parse()
torch.cuda.set_device(args.device)
random.seed(args.seed)
torch.random.manual_seed(args.seed)
if args.args is not None:
with open(args.args) as args_json:
args_dict = json.load(args_json)
args.__dict__.update(**args_dict)
# save run params
#if not os.path.isdir(args.out):
# os.makedirs(args.out)
#with open(os.path.join(args.out, 'args.json'), 'w') as args_file:
# json.dump(args.__dict__, args_file)
#with open(os.path.join(args.out, 'command.sh'), 'w') as command_file:
# command_file.write(' '.join(sys.argv))
# command_file.write('\n')
# init models
if args.gan_weights is not None:
weights_path = args.gan_weights
else:
weights_path = WEIGHTS[args.gan_type]
if args.gan_type == 'BigGAN':
G = make_big_gan(weights_path, args.target_class).eval()
elif args.gan_type == 'StyleGAN':
G = make_stylegan(
weights_path,
net_info[args.stylegan.dataset]['resolution']).eval()
elif args.gan_type == 'ProgGAN':
G = make_proggan(weights_path).eval()
else:
G = make_external(weights_path).eval()
#判断是对z还是w做latent code
if args.model == 'stylegan':
assert (args.stylegan.latent in ['z', 'w']), 'unknown latent space'
if args.stylegan.latent == 'z':
target_dim = G.dim_z
else:
target_dim = G.dim_w
if args.shift_predictor == 'ResNet':
shift_predictor = ResNetShiftPredictor(
args.direction_size, args.shift_predictor_size).cuda()
elif args.shift_predictor == 'LeNet':
shift_predictor = LeNetShiftPredictor(
args.direction_size,
1 if args.gan_type == 'SN_MNIST' else 3).cuda()
if args.continue_train:
deformator = LatentDeformator(
direction_size=args.direction_size,
out_dim=target_dim,
type=DEFORMATOR_TYPE_DICT[args.deformator]).cuda()
deformator.load_state_dict(
torch.load(args.deformator_path, map_location=torch.device('cpu')))
shift_predictor.load_state_dict(
torch.load(args.shift_predictor_path,
map_location=torch.device('cpu')))
else:
deformator = LatentDeformator(
direction_size=args.direction_size,
out_dim=target_dim,
type=DEFORMATOR_TYPE_DICT[args.deformator],
random_init=args.deformator_random_init).cuda()
# transform
graph_kwargs = util.set_graph_kwargs(args)
transform_type = ['zoom', 'shiftx', 'color', 'shifty']
transform_model = EasyDict()
for a_type in transform_type:
model = graphs.find_model_using_name(args.model, a_type)
g = model(**graph_kwargs)
transform_model[a_type] = EasyDict(model=g)
# training
args.shift_distribution = SHIFT_DISTRIDUTION_DICT[
args.shift_distribution_key]
trainer = Trainer(params=Params(**args.__dict__),
out_dir=args.out,
out_json=args.json,
continue_train=args.continue_train)
trainer.train(G, deformator, shift_predictor, transform_model)
def main():
parser = argparse.ArgumentParser(description='Latent space rectification')
parser.add_argument('--out', type=str, default='./output')
parser.add_argument('--gan_type',
type=str,
choices=WEIGHTS.keys(),
default='StyleGAN')
parser.add_argument('--gan_weights', type=str, default=None)
parser.add_argument('--json', type=str)
parser.add_argument('--deformator',
type=str,
default='ortho',
choices=DEFORMATOR_TYPE_DICT.keys())
parser.add_argument('--deformator_path',
type=str,
default='output/models/deformator_490000.pt')
parser.add_argument('--images_dir', type=str, default='output/images/')
parser.add_argument('--shift_predictor_size', type=int)
parser.add_argument('--shift_predictor',
type=str,
choices=['ResNet', 'LeNet'],
default='ResNet')
parser.add_argument('--shift_distribution_key',
type=str,
choices=SHIFT_DISTRIDUTION_DICT.keys())
parser.add_argument('--seed', type=int, default=5)
parser.add_argument('--device', type=int, default=0)
args = parser.parse_args()
torch.cuda.set_device(args.device)
# save run params
if not os.path.isdir(args.out):
os.makedirs(args.out)
# init models
if args.gan_weights is not None:
weights_path = args.gan_weights
else:
weights_path = WEIGHTS[args.gan_type]
if args.gan_type == 'BigGAN':
G = make_big_gan(weights_path, args.target_class).eval()
elif args.gan_type == 'StyleGAN':
G = make_stylegan(weights_path)
elif args.gan_type == 'ProgGAN':
G = make_proggan(weights_path).eval()
else:
G = make_external(weights_path).eval()
deformator = LatentDeformator(
G.dim_z, type=DEFORMATOR_TYPE_DICT[args.deformator]).cuda()
deformator.load_state_dict(
torch.load(args.deformator_path, map_location=torch.device('cpu')))
random.seed(args.seed)
torch.random.manual_seed(args.seed)
z = make_noise(batch=5, dim=G.dim_z).cuda()
dims = [2, 9]
fig = make_interpolation_chart(G,
deformator=deformator,
z=z,
shifts_r=10,
shifts_count=3,
dims=None,
dims_count=10,
texts=None,
dpi=1024,
direction_size=args.direction_size)
fig_to_image(fig).convert("RGB").save(
os.path.join(args.images_dir, 'test_{}.jpg'.format(args.seed)))
def main():
parser = argparse.ArgumentParser(description='Latent space rectification')
for key, val in Params().__dict__.items():
target_type = type(val) if val is not None else int
parser.add_argument('--{}'.format(key), type=target_type, default=None)
parser.add_argument('--out',
type=str,
required=True,
help='results directory')
parser.add_argument('--gan_type',
type=str,
choices=WEIGHTS.keys(),
help='generator model type')
parser.add_argument('--gan_weights',
type=str,
default=None,
help='path to generator weights')
parser.add_argument('--target_class',
nargs='+',
type=int,
default=[239],
help='classes to use for conditional GANs')
parser.add_argument('--deformator',
type=str,
default='ortho',
choices=DEFORMATOR_TYPE_DICT.keys(),
help='deformator type')
parser.add_argument('--deformator_random_init', type=bool, default=True)
parser.add_argument('--shift_predictor_size',
type=int,
help='reconstructor resolution')
parser.add_argument('--shift_predictor',
type=str,
choices=['ResNet', 'LeNet'],
default='ResNet',
help='reconstructor type')
parser.add_argument('--shift_distribution_key',
type=str,
choices=SHIFT_DISTRIDUTION_DICT.keys())
parser.add_argument('--seed', type=int, default=2)
parser.add_argument('--device', type=int, default=0)
parser.add_argument(
'--multi_gpu',
type=bool,
default=False,
help='Run generator in parallel. Be aware of old pytorch versions:\
https://github.com/pytorch/pytorch/issues/17345')
# model-specific
parser.add_argument(
'--w_shift',
type=bool,
default=True,
help='latent directions search in w-space for StyleGAN')
args = parser.parse_args()
torch.cuda.set_device(args.device)
random.seed(args.seed)
torch.random.manual_seed(args.seed)
save_command_run_params(args)
# init models
if args.gan_weights is not None:
weights_path = args.gan_weights
else:
weights_path = WEIGHTS[args.gan_type]
G = load_generator(args.__dict__, weights_path, args.w_shift)
deformator = LatentDeformator(
shift_dim=G.dim_shift,
input_dim=args.directions_count,
out_dim=args.max_latent_dim,
type=DEFORMATOR_TYPE_DICT[args.deformator],
random_init=args.deformator_random_init).cuda()
if args.shift_predictor == 'ResNet':
shift_predictor = LatentShiftPredictor(
G.dim_shift, args.shift_predictor_size).cuda()
elif args.shift_predictor == 'LeNet':
shift_predictor = LeNetShiftPredictor(
G.dim_shift, 1 if args.gan_type == 'SN_MNIST' else 3).cuda()
# training
args.shift_distribution = SHIFT_DISTRIDUTION_DICT[
args.shift_distribution_key]
params = Params(**args.__dict__)
# update dims with respect to the deformator if some of params are None
params.directions_count = int(deformator.input_dim)
params.max_latent_dim = int(deformator.out_dim)
trainer = Trainer(params, out_dir=args.out)
trainer.train(G, deformator, shift_predictor, multi_gpu=args.multi_gpu)
save_results_charts(G, deformator, params, trainer.log_dir)
def main():
parser = argparse.ArgumentParser(description='Latent space rectification')
for key, val in Params().__dict__.items():
parser.add_argument('--{}'.format(key), type=type(val), default=None)
parser.add_argument('--args',
type=str,
default=None,
help='json with all arguments')
parser.add_argument('--out', type=str, required=True)
parser.add_argument('--gan_type', type=str, choices=WEIGHTS.keys())
parser.add_argument('--gan_weights', type=str, default=None)
parser.add_argument('--target_class', type=int, default=239)
parser.add_argument('--json', type=str)
parser.add_argument('--deformator',
type=str,
default='ortho',
choices=DEFORMATOR_TYPE_DICT.keys())
parser.add_argument('--deformator_random_init', type=bool, default=False)
parser.add_argument('--shift_predictor_size', type=int)
parser.add_argument('--shift_predictor',
type=str,
choices=['ResNet', 'LeNet'],
default='ResNet')
parser.add_argument('--shift_distribution_key',
type=str,
choices=SHIFT_DISTRIDUTION_DICT.keys())
parser.add_argument('--seed', type=int, default=2)
parser.add_argument('--device', type=int, default=0)
args = parser.parse_args()
torch.cuda.set_device(args.device)
random.seed(args.seed)
torch.random.manual_seed(args.seed)
if args.args is not None:
with open(args.args) as args_json:
args_dict = json.load(args_json)
args.__dict__.update(**args_dict)
# save run params
if not os.path.isdir(args.out):
os.makedirs(args.out)
with open(os.path.join(args.out, 'args.json'), 'w') as args_file:
json.dump(args.__dict__, args_file)
with open(os.path.join(args.out, 'command.sh'), 'w') as command_file:
command_file.write(' '.join(sys.argv))
command_file.write('\n')
# init models
if args.gan_weights is not None:
weights_path = args.gan_weights
else:
weights_path = WEIGHTS[args.gan_type]
if args.gan_type == 'BigGAN':
G = make_big_gan(weights_path, args.target_class).eval()
elif args.gan_type == 'ProgGAN':
G = make_proggan(weights_path).eval()
else:
G = make_external(weights_path).eval()
deformator = LatentDeformator(
G.dim_z,
type=DEFORMATOR_TYPE_DICT[args.deformator],
random_init=args.deformator_random_init).cuda()
if args.shift_predictor == 'ResNet':
shift_predictor = ResNetShiftPredictor(
G.dim_z, args.shift_predictor_size).cuda()
elif args.shift_predictor == 'LeNet':
shift_predictor = LeNetShiftPredictor(
G.dim_z, 1 if args.gan_type == 'SN_MNIST' else 3).cuda()
# training
args.shift_distribution = SHIFT_DISTRIDUTION_DICT[
args.shift_distribution_key]
args.deformation_loss = DEFORMATOR_LOSS_DICT[args.deformation_loss]
trainer = Trainer(params=Params(**args.__dict__),
out_dir=args.out,
out_json=args.json)
trainer.train(G, deformator, shift_predictor)