def run(self, network_pkl, run_dir=None, dataset_args=None, mirror_augment=None, num_gpus=1, tf_config=None, log_results=True):
self._network_pkl = network_pkl
self._dataset_args = dataset_args
self._mirror_augment = mirror_augment
self._results = []
if (dataset_args is None or mirror_augment is None) and run_dir is not None:
run_config = misc.parse_config_for_previous_run(run_dir)
self._dataset_args = dict(run_config['dataset'])
self._dataset_args['shuffle_mb'] = 0
self._mirror_augment = run_config['train'].get('mirror_augment', False)
time_begin = time.time()
with tf.Graph().as_default(), tflib.create_session(tf_config).as_default(): # pylint: disable=not-context-manager
_G, _D, Gs = misc.load_pkl(self._network_pkl)
self._evaluate(Gs, num_gpus=num_gpus)
self._eval_time = time.time() - time_begin
if log_results:
result_str = self.get_result_str()
if run_dir is not None:
log = os.path.join(run_dir, 'metric-%s.txt' % self.name)
with dnnlib.util.Logger(log, 'a'):
print(result_str)
else:
print(result_str)
def run(self,
network_pkl,
run_dir=None,
data_dir=None,
dataset_args=None,
mirror_augment=None,
num_gpus=1,
tf_config=None,
log_results=True,
Gs_kwargs=dict(is_validation=True)):
self._reset(network_pkl=network_pkl,
run_dir=run_dir,
data_dir=data_dir,
dataset_args=dataset_args,
mirror_augment=mirror_augment)
time_begin = time.time()
with tf.Graph().as_default(), tflib.create_session(
tf_config).as_default(): # pylint: disable=not-context-manager
self._report_progress(0, 1)
# _G, _D, Gs = misc.load_pkl(self._network_pkl)
_G, _D, _I, Gs = misc.load_pkl(self._network_pkl)
self._evaluate(Gs, Gs_kwargs=Gs_kwargs, num_gpus=num_gpus)
self._report_progress(1, 1)
self._eval_time = time.time() - time_begin # pylint: disable=attribute-defined-outside-init
if log_results:
if run_dir is not None:
log_file = os.path.join(run_dir, 'metric-%s.txt' % self.name)
with dnnlib.util.Logger(log_file, 'a'):
print(self.get_result_str().strip())
else:
print(self.get_result_str().strip())
def run(self, network_pkl, num_gpus=1, G_kwargs=dict(is_validation=True)):
self._results = []
self._network_name = os.path.splitext(os.path.basename(network_pkl))[0]
self._eval_time = 0
self._dataset = None
with tf.Graph().as_default(), tflib.create_session().as_default(): # pylint: disable=not-context-manager
self._report_progress(0, 1)
time_begin = time.time()
with dnnlib.util.open_url(network_pkl) as f:
G, D, Gs = pickle.load(f)
G_kwargs = dnnlib.EasyDict(G_kwargs)
G_kwargs.update(self.force_G_kwargs)
self._evaluate(G=G,
D=D,
Gs=Gs,
G_kwargs=G_kwargs,
num_gpus=num_gpus)
self._eval_time = time.time() - time_begin # pylint: disable=attribute-defined-outside-init
self._report_progress(1, 1)
if self._dataset is not None:
self._dataset.close()
self._dataset = None
result_str = self.get_result_str()
tqdm.write(result_str)
if self._run_dir is not None and os.path.isdir(self._run_dir):
with open(os.path.join(self._run_dir, f'metric-{self.name}.txt'),
'at') as f:
f.write(result_str + '\n')
def run(self,
network_pkl,
run_dir=None,
dataset_args=None,
mirror_augment=None,
num_gpus=1,
tf_config=None,
log_results=True,
model_type="rignet"):
create_dir(config.EVALUATION_DIR, exist_ok=True)
self._network_pkl = network_pkl
self._dataset_args = dataset_args
self._mirror_augment = mirror_augment
self._results = []
self.model_type = model_type
if (dataset_args is None
or mirror_augment is None) and run_dir is not None:
run_config = misc.parse_config_for_previous_run(run_dir)
self._dataset_args = dict(run_config['dataset'])
self._dataset_args['shuffle_mb'] = 0
self._mirror_augment = run_config['train'].get(
'mirror_augment', False)
time_begin = time.time()
with tf.Graph().as_default(), tflib.create_session(
tf_config).as_default(): # pylint: disable=not-context-manager
E, _G, _D, Gs = misc.load_pkl(self._network_pkl)
print("Loaded Encoder")
Inv, _, _, _ = misc.load_pkl(config.INVERSION_PICKLE_DIR)
print("Loaded Inv")
self._evaluate(Gs, E, Inv, num_gpus=num_gpus)
self._eval_time = time.time() - time_begin
if log_results:
result_str = self.get_result_str()
if run_dir is not None:
log = os.path.join(run_dir, 'metric-%s.txt' % self.name)
with dnnlib.util.Logger(log, 'a'):
print(result_str)
else:
print(result_str)
result_path = os.path.join(
config.EVALUATION_DIR, "result_" +
convert_pickle_path_to_name(self._network_pkl) + ".txt")
write_to_file(result_str + "\n\n\n", result_path)
def run(self,
network_pkl,
num_imgs,
run_dir=None,
data_dir=None,
dataset_args=None,
mirror_augment=None,
ratio=1.0,
num_gpus=1,
tf_config=None,
log_results=True,
Gs_kwargs=dict(is_validation=True),
eval_mod=False,
**kwargs):
self._reset(network_pkl=network_pkl,
run_dir=run_dir,
data_dir=data_dir,
dataset_args=dataset_args,
mirror_augment=mirror_augment)
self.eval_mod = eval_mod
time_begin = time.time()
with tf.Graph().as_default(), tflib.create_session(
tf_config).as_default():
self._report_progress(0, 1)
_G = _D = Gs = None
if self._network_pkl is not None:
_G, _D, Gs = misc.load_pkl(self._network_pkl)[:3]
self._evaluate(Gs,
Gs_kwargs=Gs_kwargs,
num_gpus=num_gpus,
num_imgs=num_imgs,
ratio=ratio,
**kwargs)
self._report_progress(1, 1)
self._eval_time = time.time() - time_begin
if log_results:
if run_dir is not None:
log_file = os.path.join(run_dir, "metric-%s.txt" % self.name)
with dnnlib.util.Logger(log_file, "a", screen=False):
print(self.get_result_str().strip())
print(self.get_result_str(screen=True).strip())
return self._results[0].value
def run(self, network_pkl, run_dir=None, data_dir=None, dataset_args=None, mirror_augment=None, num_gpus=1, tf_config=None, log_results=True,
include_I=False, avg_mv_for_I=False, Gs_kwargs=dict(is_validation=True, return_atts=False), train_infernet=False, is_vae=False, use_D=False,
**kwargs):
self._reset(network_pkl=network_pkl, run_dir=run_dir, data_dir=data_dir, dataset_args=dataset_args, mirror_augment=mirror_augment)
time_begin = time.time()
with tf.Graph().as_default(), tflib.create_session(tf_config).as_default(): # pylint: disable=not-context-manager
self._report_progress(0, 1)
if include_I:
if avg_mv_for_I:
_G, _D, _I, Gs, I = misc.load_pkl(self._network_pkl)
else:
_G, _D, I, Gs = misc.load_pkl(self._network_pkl)
outs = self._evaluate(Gs=Gs, Gs_kwargs=Gs_kwargs, I_net=I, num_gpus=num_gpus, **kwargs)
elif train_infernet:
I, Gs = misc.load_pkl(self._network_pkl)
outs = self._evaluate(Gs=Gs, Gs_kwargs=Gs_kwargs, I_net=I, num_gpus=num_gpus, **kwargs)
elif is_vae:
if use_D:
I, Gs, D = misc.load_pkl(self._network_pkl)
else:
I, Gs = misc.load_pkl(self._network_pkl)
outs = self._evaluate(Gs=Gs, Gs_kwargs=Gs_kwargs, I_net=I, num_gpus=num_gpus, **kwargs)
else:
_G, _D, Gs = misc.load_pkl(self._network_pkl)
outs = self._evaluate(Gs=Gs, Gs_kwargs=Gs_kwargs, num_gpus=num_gpus, **kwargs)
self._report_progress(1, 1)
self._eval_time = time.time() - time_begin # pylint: disable=attribute-defined-outside-init
if log_results:
if run_dir is not None:
log_file = os.path.join(run_dir, 'metric-%s.txt' % self.name)
with dnnlib.util.Logger(log_file, 'a'):
print(self.get_result_str().strip())
else:
print(self.get_result_str().strip())
return outs
def setup_training_options(
# General options (not included in desc).
gpus=None, # Number of GPUs: <int>, default = 1 gpu
snap=None, # Snapshot interval: <int>, default = 50 ticks
# Training dataset.
data=None, # Training dataset (required): <path>
res=None, # Override dataset resolution: <int>, default = highest available
mirror=None, # Augment dataset with x-flips: <bool>, default = False
mirrory=None, # Augment dataset with y-flips: <bool>, default = False
use_raw=None,
# Metrics (not included in desc).
metrics=None, # List of metric names: [], ['fid50k_full'] (default), ...
metricdata=None, # Metric dataset (optional): <path>
# Base config.
cfg=None, # Base config: 'auto' (default), 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar', 'cifarbaseline'
gamma=None, # Override R1 gamma: <float>, default = depends on cfg
kimg=None, # Override training duration: <int>, default = depends on cfg
# Discriminator augmentation.
aug=None, # Augmentation mode: 'ada' (default), 'noaug', 'fixed', 'adarv'
p=None, # Specify p for 'fixed' (required): <float>
target=None, # Override ADA target for 'ada' and 'adarv': <float>, default = depends on aug
augpipe=None, # Augmentation pipeline: 'blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc' (default), ..., 'bgcfnc'
# Comparison methods.
cmethod=None, # Comparison method: 'nocmethod' (default), 'bcr', 'zcr', 'pagan', 'wgangp', 'auxrot', 'spectralnorm', 'shallowmap', 'adropout'
dcap=None, # Multiplier for discriminator capacity: <float>, default = 1
# Transfer learning.
resume=None, # Load previous network: 'noresume' (default), 'ffhq256', 'ffhq512', 'ffhq1024', 'celebahq256', 'lsundog256', <file>, <url>
freezed=None, # Freeze-D: <int>, default = 0 discriminator layers
):
# Initialize dicts.
args = dnnlib.EasyDict()
args.G_args = dnnlib.EasyDict(func_name='training.networks.G_main')
args.D_args = dnnlib.EasyDict(func_name='training.networks.D_main')
args.G_opt_args = dnnlib.EasyDict(beta1=0.0, beta2=0.99)
args.D_opt_args = dnnlib.EasyDict(beta1=0.0, beta2=0.99)
args.loss_args = dnnlib.EasyDict(func_name='training.loss.stylegan2')
args.augment_args = dnnlib.EasyDict(
class_name='training.augment.AdaptiveAugment')
# ---------------------------
# General options: gpus, snap
# ---------------------------
if gpus is None:
gpus = 1
assert isinstance(gpus, int)
if not (gpus >= 1 and gpus & (gpus - 1) == 0):
raise UserError('--gpus must be a power of two')
args.num_gpus = gpus
if snap is None:
snap = 50
assert isinstance(snap, int)
if snap < 1:
raise UserError('--snap must be at least 1')
args.image_snapshot_ticks = snap
args.network_snapshot_ticks = snap
# ---------------------------------------------
# Training dataset: data, res, mirror, mirrory
# ---------------------------------------------
assert data is not None
assert isinstance(data, str)
data_name = os.path.basename(os.path.abspath(data))
if not os.path.isdir(data) or len(data_name) == 0:
raise UserError(
'--data must point to a directory containing *.tfrecords')
desc = data_name
with tf.Graph().as_default(), tflib.create_session().as_default(): # pylint: disable=not-context-manager
args.train_dataset_args = dnnlib.EasyDict(path=data,
max_label_size='full')
args.train_dataset_args.use_raw = use_raw
dataset_obj = dataset.load_dataset(
**args.train_dataset_args
) # try to load the data and see what comes out
args.train_dataset_args.resolution = dataset_obj.shape[
-1] # be explicit about resolution
args.train_dataset_args.max_label_size = dataset_obj.label_size # be explicit about label size
validation_set_available = dataset_obj.has_validation_set
dataset_obj.close()
dataset_obj = None
if res is None:
res = args.train_dataset_args.resolution
else:
assert isinstance(res, int)
if not (res >= 4 and res & (res - 1) == 0):
raise UserError('--res must be a power of two and at least 4')
if res > args.train_dataset_args.resolution:
raise UserError(
f'--res cannot exceed maximum available resolution in the dataset ({args.train_dataset_args.resolution})'
)
desc += f'-res{res:d}'
args.train_dataset_args.resolution = res
if mirror is None:
mirror = False
assert isinstance(mirror, bool)
if mirror:
desc += '-mirror'
args.train_dataset_args.mirror_augment = mirror
if mirrory is None:
mirrory = False
assert isinstance(mirrory, bool)
if mirrory:
desc += '-mirrory'
args.train_dataset_args.mirror_augment_v = mirrory
args.train_dataset_args.use_raw = use_raw
# ----------------------------
# Metrics: metrics, metricdata
# ----------------------------
if metrics is None:
metrics = ['fid50k_full']
assert isinstance(metrics, list)
assert all(isinstance(metric, str) for metric in metrics)
args.metric_arg_list = []
for metric in metrics:
if metric not in metric_defaults.metric_defaults:
raise UserError('\n'.join(
['--metrics can only contain the following values:', 'none'] +
list(metric_defaults.metric_defaults.keys())))
args.metric_arg_list.append(metric_defaults.metric_defaults[metric])
args.metric_dataset_args = dnnlib.EasyDict(args.train_dataset_args)
if metricdata is not None:
assert isinstance(metricdata, str)
if not os.path.isdir(metricdata):
raise UserError(
'--metricdata must point to a directory containing *.tfrecords'
)
args.metric_dataset_args.path = metricdata
# -----------------------------
# Base config: cfg, gamma, kimg
# -----------------------------
if cfg is None:
cfg = 'auto'
assert isinstance(cfg, str)
desc += f'-{cfg}'
cfg_specs = {
'auto':
dict(ref_gpus=-1,
kimg=25000,
mb=-1,
mbstd=-1,
fmaps=-1,
lrate=-1,
gamma=-1,
ema=-1,
ramp=0.05,
map=8), # populated dynamically based on 'gpus' and 'res'
'11gb-gpu':
dict(ref_gpus=1,
kimg=25000,
mb=4,
mbstd=4,
fmaps=1,
lrate=0.002,
gamma=10,
ema=10,
ramp=None,
map=8), # uses mixed-precision, 11GB GPU
'11gb-gpu-complex':
dict(ref_gpus=1,
kimg=25000,
mb=4,
mbstd=4,
fmaps=1,
lrate=0.002,
gamma=10,
ema=10,
ramp=None,
map=8), # uses mixed-precision, 11GB GPU
'24gb-gpu':
dict(ref_gpus=1,
kimg=25000,
mb=8,
mbstd=8,
fmaps=1,
lrate=0.002,
gamma=10,
ema=10,
ramp=None,
map=8), # uses mixed-precision, 24GB GPU
'24gb-gpu-complex':
dict(ref_gpus=1,
kimg=25000,
mb=8,
mbstd=8,
fmaps=1,
lrate=0.002,
gamma=10,
ema=10,
ramp=None,
map=8), # uses mixed-precision, 24GB GPU
'48gb-gpu':
dict(ref_gpus=1,
kimg=25000,
mb=16,
mbstd=16,
fmaps=1,
lrate=0.002,
gamma=10,
ema=10,
ramp=None,
map=8), # uses mixed-precision, 48GB GPU
'stylegan2':
dict(ref_gpus=8,
kimg=25000,
mb=32,
mbstd=4,
fmaps=1,
lrate=0.002,
gamma=10,
ema=10,
ramp=None,
map=8), # uses mixed-precision, unlike original StyleGAN2
'paper256':
dict(ref_gpus=8,
kimg=25000,
mb=64,
mbstd=8,
fmaps=0.5,
lrate=0.0025,
gamma=1,
ema=20,
ramp=None,
map=8),
'paper512':
dict(ref_gpus=8,
kimg=25000,
mb=64,
mbstd=8,
fmaps=1,
lrate=0.0025,
gamma=0.5,
ema=20,
ramp=None,
map=8),
'paper1024':
dict(ref_gpus=8,
kimg=25000,
mb=32,
mbstd=4,
fmaps=1,
lrate=0.002,
gamma=2,
ema=10,
ramp=None,
map=8),
'cifar':
dict(ref_gpus=2,
kimg=100000,
mb=64,
mbstd=32,
fmaps=0.5,
lrate=0.0025,
gamma=0.01,
ema=500,
ramp=0.05,
map=2),
'cifarbaseline':
dict(ref_gpus=2,
kimg=100000,
mb=64,
mbstd=32,
fmaps=0.5,
lrate=0.0025,
gamma=0.01,
ema=500,
ramp=0.05,
map=8),
}
assert cfg in cfg_specs
spec = dnnlib.EasyDict(cfg_specs[cfg])
if cfg == 'auto':
desc += f'{gpus:d}'
spec.ref_gpus = gpus
spec.mb = max(min(gpus * min(4096 // res, 32), 64),
gpus) # keep gpu memory consumption at bay
spec.mbstd = min(
spec.mb // gpus, 4
) # other hyperparams behave more predictably if mbstd group size remains fixed
spec.fmaps = 1 if res >= 512 else 0.5
spec.lrate = 0.002 if res >= 1024 else 0.0025
spec.gamma = 0.0002 * (res**2) / spec.mb # heuristic formula
spec.ema = spec.mb * 10 / 32
args.total_kimg = spec.kimg
args.minibatch_size = spec.mb
args.minibatch_gpu = spec.mb // spec.ref_gpus
args.D_args.mbstd_group_size = spec.mbstd
args.G_args.fmap_base = args.D_args.fmap_base = int(spec.fmaps * 16384)
args.G_args.fmap_max = args.D_args.fmap_max = 512
args.G_opt_args.learning_rate = args.D_opt_args.learning_rate = spec.lrate
args.loss_args.r1_gamma = spec.gamma
args.G_smoothing_kimg = spec.ema
args.G_smoothing_rampup = spec.ramp
args.G_args.mapping_layers = spec.map
args.G_args.num_fp16_res = args.D_args.num_fp16_res = 4 # enable mixed-precision training
args.G_args.conv_clamp = args.D_args.conv_clamp = 256 # clamp activations to avoid float16 overflow
if cfg == 'cifar' or cfg.split('-')[-1] == 'complex':
args.loss_args.pl_weight = 0 # disable path length regularization
args.G_args.style_mixing_prob = None # disable style mixing
args.D_args.architecture = 'orig' # disable residual skip connections
if gamma is not None:
assert isinstance(gamma, float)
if not gamma >= 0:
raise UserError('--gamma must be non-negative')
desc += f'-gamma{gamma:g}'
args.loss_args.r1_gamma = gamma
if kimg is not None:
assert isinstance(kimg, int)
if not kimg >= 1:
raise UserError('--kimg must be at least 1')
desc += f'-kimg{kimg:d}'
args.total_kimg = kimg
# ---------------------------------------------------
# Discriminator augmentation: aug, p, target, augpipe
# ---------------------------------------------------
if aug is None:
aug = 'ada'
else:
assert isinstance(aug, str)
desc += f'-{aug}'
if aug == 'ada':
args.augment_args.tune_heuristic = 'rt'
args.augment_args.tune_target = 0.6
elif aug == 'noaug':
pass
elif aug == 'fixed':
if p is None:
raise UserError(f'--aug={aug} requires specifying --p')
elif aug == 'adarv':
if not validation_set_available:
raise UserError(
f'--aug={aug} requires separate validation set; please see "python dataset_tool.py pack -h"'
)
args.augment_args.tune_heuristic = 'rv'
args.augment_args.tune_target = 0.5
else:
raise UserError(f'--aug={aug} not supported')
if p is not None:
assert isinstance(p, float)
if aug != 'fixed':
raise UserError('--p can only be specified with --aug=fixed')
if not 0 <= p <= 1:
raise UserError('--p must be between 0 and 1')
desc += f'-p{p:g}'
args.augment_args.initial_strength = p
if target is not None:
assert isinstance(target, float)
if aug not in ['ada', 'adarv']:
raise UserError(
'--target can only be specified with --aug=ada or --aug=adarv')
if not 0 <= target <= 1:
raise UserError('--target must be between 0 and 1')
desc += f'-target{target:g}'
args.augment_args.tune_target = target
assert augpipe is None or isinstance(augpipe, str)
if augpipe is None:
augpipe = 'bgc'
else:
if aug == 'noaug':
raise UserError('--augpipe cannot be specified with --aug=noaug')
desc += f'-{augpipe}'
augpipe_specs = {
'blit':
dict(xflip=1, rotate90=1, xint=1),
'geom':
dict(scale=1, rotate=1, aniso=1, xfrac=1),
'color':
dict(brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1),
'filter':
dict(imgfilter=1),
'noise':
dict(noise=1),
'cutout':
dict(cutout=1),
'bg':
dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1),
'bgc':
dict(xflip=1,
rotate90=1,
xint=1,
scale=1,
rotate=1,
aniso=1,
xfrac=1,
brightness=1,
contrast=1,
lumaflip=1,
hue=1,
saturation=1),
'bgcf':
dict(xflip=1,
rotate90=1,
xint=1,
scale=1,
rotate=1,
aniso=1,
xfrac=1,
brightness=1,
contrast=1,
lumaflip=1,
hue=1,
saturation=1,
imgfilter=1),
'bgcfn':
dict(xflip=1,
rotate90=1,
xint=1,
scale=1,
rotate=1,
aniso=1,
xfrac=1,
brightness=1,
contrast=1,
lumaflip=1,
hue=1,
saturation=1,
imgfilter=1,
noise=1),
'bgcfnc':
dict(xflip=1,
rotate90=1,
xint=1,
scale=1,
rotate=1,
aniso=1,
xfrac=1,
brightness=1,
contrast=1,
lumaflip=1,
hue=1,
saturation=1,
imgfilter=1,
noise=1,
cutout=1),
}
assert augpipe in augpipe_specs
if aug != 'noaug':
args.augment_args.apply_func = 'training.augment.augment_pipeline'
args.augment_args.apply_args = augpipe_specs[augpipe]
# ---------------------------------
# Comparison methods: cmethod, dcap
# ---------------------------------
assert cmethod is None or isinstance(cmethod, str)
if cmethod is None:
cmethod = 'nocmethod'
else:
desc += f'-{cmethod}'
if cmethod == 'nocmethod':
pass
elif cmethod == 'bcr':
args.loss_args.func_name = 'training.loss.cmethods'
args.loss_args.bcr_real_weight = 10
args.loss_args.bcr_fake_weight = 10
args.loss_args.bcr_augment = dnnlib.EasyDict(
func_name='training.augment.augment_pipeline',
xint=1,
xint_max=1 / 32)
elif cmethod == 'zcr':
args.loss_args.func_name = 'training.loss.cmethods'
args.loss_args.zcr_gen_weight = 0.02
args.loss_args.zcr_dis_weight = 0.2
args.G_args.num_fp16_res = args.D_args.num_fp16_res = 0 # disable mixed-precision training
args.G_args.conv_clamp = args.D_args.conv_clamp = None
elif cmethod == 'pagan':
if aug != 'noaug':
raise UserError(
f'--cmethod={cmethod} is not compatible with discriminator augmentation; please specify --aug=noaug'
)
args.D_args.use_pagan = True
args.augment_args.tune_heuristic = 'rt' # enable ada heuristic
args.augment_args.pop('apply_func',
None) # disable discriminator augmentation
args.augment_args.pop('apply_args', None)
args.augment_args.tune_target = 0.95
elif cmethod == 'wgangp':
if aug != 'noaug':
raise UserError(
f'--cmethod={cmethod} is not compatible with discriminator augmentation; please specify --aug=noaug'
)
if gamma is not None:
raise UserError(
f'--cmethod={cmethod} is not compatible with --gamma')
args.loss_args = dnnlib.EasyDict(func_name='training.loss.wgangp')
args.G_opt_args.learning_rate = args.D_opt_args.learning_rate = 0.001
args.G_args.num_fp16_res = args.D_args.num_fp16_res = 0 # disable mixed-precision training
args.G_args.conv_clamp = args.D_args.conv_clamp = None
args.lazy_regularization = False
elif cmethod == 'auxrot':
if args.train_dataset_args.max_label_size > 0:
raise UserError(
f'--cmethod={cmethod} is not compatible with label conditioning; please specify a dataset without labels'
)
args.loss_args.func_name = 'training.loss.cmethods'
args.loss_args.auxrot_alpha = 10
args.loss_args.auxrot_beta = 5
args.D_args.score_max = 5 # prepare D to output 5 scalars per image instead of just 1
elif cmethod == 'spectralnorm':
args.D_args.use_spectral_norm = True
elif cmethod == 'shallowmap':
if args.G_args.mapping_layers == 2:
raise UserError(f'--cmethod={cmethod} is a no-op for --cfg={cfg}')
args.G_args.mapping_layers = 2
elif cmethod == 'adropout':
if aug != 'noaug':
raise UserError(
f'--cmethod={cmethod} is not compatible with discriminator augmentation; please specify --aug=noaug'
)
args.D_args.adaptive_dropout = 1
args.augment_args.tune_heuristic = 'rt' # enable ada heuristic
args.augment_args.pop('apply_func',
None) # disable discriminator augmentation
args.augment_args.pop('apply_args', None)
args.augment_args.tune_target = 0.6
else:
raise UserError(f'--cmethod={cmethod} not supported')
if dcap is not None:
assert isinstance(dcap, float)
if not dcap > 0:
raise UserError('--dcap must be positive')
desc += f'-dcap{dcap:g}'
args.D_args.fmap_base = max(int(args.D_args.fmap_base * dcap), 1)
args.D_args.fmap_max = max(int(args.D_args.fmap_max * dcap), 1)
# ----------------------------------
# Transfer learning: resume, freezed
# ----------------------------------
resume_specs = {
'ffhq256':
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl',
'ffhq512':
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/ffhq-res512-mirror-stylegan2-noaug.pkl',
'ffhq1024':
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/ffhq-res1024-mirror-stylegan2-noaug.pkl',
'celebahq256':
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/celebahq-res256-mirror-paper256-kimg100000-ada-target0.5.pkl',
'lsundog256':
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/lsundog-res256-paper256-kimg100000-noaug.pkl',
'afhqcat512':
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/afhqcat.pkl',
'afhqdog512':
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/afhqdog.pkl',
'afhqwild512':
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/afhqwild.pkl',
'brecahad512':
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/brecahad.pkl',
'cifar10':
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/cifar10.pkl',
'metfaces':
'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/metfaces.pkl',
}
assert resume is None or isinstance(resume, str)
if resume is None:
resume = 'noresume'
elif resume == 'noresume':
desc += '-noresume'
elif resume in resume_specs:
desc += f'-resume{resume}'
args.resume_pkl = resume_specs[resume] # predefined url
else:
desc += '-resumecustom'
args.resume_pkl = resume # custom path or url
if resume != 'noresume':
args.augment_args.tune_kimg = 100 # make ADA react faster at the beginning
args.G_smoothing_rampup = None # disable EMA rampup
if freezed is not None:
assert isinstance(freezed, int)
if not freezed >= 0:
raise UserError('--freezed must be non-negative')
desc += f'-freezed{freezed:d}'
args.D_args.freeze_layers = freezed
return desc, args
def load_network(self, network_path: str) -> None:
self.session = tflib.create_session(None, force_as_default=True)
# Load Networks
self.image_resolution = '256'
replace_res = re.findall('256|512', network_path)
if replace_res:
self.image_resolution = int(replace_res[0])
self.current_network_checkpoint = network_path
_, _, self.Gs = misc.load_pkl('../../results/' + network_path)
self._vgg16_model = misc.load_pkl(
'https://nvlabs-fi-cdn.nvidia.com/stylegan/networks/metrics/vgg16_zhang_perceptual.pkl'
)
# Define Placeholders
self.latent_placeholder = tf.placeholder(tf.float32,
shape=self.Gs.input_shapes[0])
self.label_placeholder = tf.placeholder(tf.float32,
shape=self.Gs.input_shapes[1])
self.dlatent_placeholder = tf.placeholder(
tf.float32, shape=self.Gs.components.synthesis.input_shape)
self.dlatent_left_placeholder = tf.placeholder(
tf.float32, self.Gs.components.synthesis.input_shape)
self.dlatent_right_placeholder = tf.placeholder(
tf.float32, self.Gs.components.synthesis.input_shape)
self.magnitude_placeholder = tf.placeholder(tf.float32, shape=())
self.delta_magnitude_placeholder = tf.placeholder(tf.float32, shape=())
if self.Gs.input_shapes[1][1] == 121:
self.label_version = 'v7'
if self.Gs.input_shapes[1][1] == 127:
self.label_version = 'v5'
read_label_version = re.findall('v\d+', network_path)
if read_label_version:
self.label_version = read_label_version[0]
# Generate Image
self._output_image = self.Gs.get_output_for(self.latent_placeholder,
self.label_placeholder,
randomize_noise=False,
truncation_psi=None,
style_mixing_prob=None)
self._synthesis = self.Gs.components.synthesis.get_output_for(
self.dlatent_placeholder, randomize_noise=False)
if 'baseline_without_labels' in self.current_network_checkpoint:
zero_label = tf.zeros(shape=[1, 0], dtype=tf.float32)
self._mapping_without_labels = self.Gs.components.mapping.get_output_for(
self.latent_placeholder, zero_label)
# Separate Mapping
if 'mapping' in self.Gs.components:
self.separate_mapping = False
self._mapping = self.Gs.components.mapping.get_output_for(
self.latent_placeholder, self.label_placeholder)
else:
self.separate_mapping = True
self.concat = self.Gs.components.synthesis.input_shape[2] > 512
self._latent_separate_mapping = self.Gs.components.mapping_latent.get_output_for(
self.latent_placeholder)
self._label_separate_mapping = self.Gs.components.mapping_label.get_output_for(
self.label_placeholder)
# Interpolation Gradient Graph
dlatent_int_delta, dlatent_int = utils.delta_lerp(
a=self.dlatent_left_placeholder,
b=self.dlatent_right_placeholder,
mag=self.magnitude_placeholder,
delta_mag=self.delta_magnitude_placeholder,
)
image_int_delta = self.Gs.components.synthesis.get_output_for(
dlatent_int_delta, randomize_noise=False)
self._gradients = tf.gradients(image_int_delta, [dlatent_int])[0]
# VGG16 Graph
image_int = self.Gs.components.synthesis.get_output_for(
dlatent_int, randomize_noise=False)
self._vgg16_distance = self._vgg16_model.get_output_for(
image_int, image_int_delta)
print('Successfully loaded:', self.current_network_checkpoint)
def run(data, train_dir, config, d_aug, diffaug_policy, cond, ops, mirror, mirror_v, \
kimg, batch_size, lrate, resume, resume_kimg, num_gpus, ema_kimg, gamma, freezeD):
# training functions
if d_aug: # https://github.com/mit-han-lab/data-efficient-gans
train = EasyDict(
run_func_name='training.training_loop_diffaug.training_loop'
) # Options for training loop (Diff Augment method)
loss_args = EasyDict(
func_name='training.loss_diffaug.ns_DiffAugment_r1',
policy=diffaug_policy) # Options for loss (Diff Augment method)
else: # original nvidia
train = EasyDict(run_func_name='training.training_loop.training_loop'
) # Options for training loop (original from NVidia)
G_loss = EasyDict(func_name='training.loss.G_logistic_ns_pathreg'
) # Options for generator loss.
D_loss = EasyDict(func_name='training.loss.D_logistic_r1'
) # Options for discriminator loss.
# network functions
G = EasyDict(func_name='training.networks_stylegan2.G_main'
) # Options for generator network.
D = EasyDict(func_name='training.networks_stylegan2.D_stylegan2'
) # Options for discriminator network.
G_opt = EasyDict(beta1=0.0, beta2=0.99,
epsilon=1e-8) # Options for generator optimizer.
D_opt = EasyDict(beta1=0.0, beta2=0.99,
epsilon=1e-8) # Options for discriminator optimizer.
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(
size='1080p',
layout='random') # Options for setup_snapshot_image_grid().
sc = dnnlib.SubmitConfig() # Options for dnnlib.submit_run().
tf_config = {'rnd.np_random_seed': 1000} # Options for tflib.init_tf().
G.impl = D.impl = ops
# dataset (tfrecords) - get or create
tfr_files = file_list(os.path.dirname(data), 'tfr')
tfr_files = [
f for f in tfr_files if basename(data) == basename(f).split('-')[0]
]
if len(tfr_files) == 0 or os.stat(tfr_files[0]).st_size == 0:
tfr_file, total_samples = create_from_image_folders(
data) if cond is True else create_from_images(data)
else:
tfr_file = tfr_files[0]
dataset_args = EasyDict(tfrecord=tfr_file)
# resolutions
with tf.Graph().as_default(), tflib.create_session().as_default(): # pylint: disable=not-context-manager
dataset_obj = dataset.load_dataset(
**dataset_args) # loading the data to see what comes out
resolution = dataset_obj.resolution
init_res = dataset_obj.init_res
res_log2 = dataset_obj.res_log2
dataset_obj.close()
dataset_obj = None
if list(init_res) == [4, 4]:
desc = '%s-%d' % (basename(data), resolution)
else:
print(' custom init resolution', init_res)
desc = basename(tfr_file)
G.init_res = D.init_res = list(init_res)
train.savenames = [desc.replace(basename(data), 'snapshot'), desc]
desc += '-%s' % config
# training schedule
train.total_kimg = kimg
train.image_snapshot_ticks = 1 * num_gpus if kimg <= 1000 else 4 * num_gpus
train.network_snapshot_ticks = 5
train.mirror_augment = mirror
train.mirror_augment_v = mirror_v
sched.tick_kimg_base = 2 if train.total_kimg < 2000 else 4
# learning rate
if config == 'e':
sched.G_lrate_base = 0.001
sched.G_lrate_dict = {0: 0.001, 1: 0.0007, 2: 0.0005, 3: 0.0003}
sched.lrate_step = 1500 # period for stepping to next lrate, in kimg
if config == 'f':
sched.G_lrate_base = lrate # 0.001 for big datasets, 0.0003 for few-shot
sched.D_lrate_base = sched.G_lrate_base # *2 - not used anyway
# batch size (for 16gb memory GPU)
sched.minibatch_gpu_base = 4096 // resolution if batch_size is None else batch_size
print(' Batch size', sched.minibatch_gpu_base)
sched.minibatch_size_base = num_gpus * sched.minibatch_gpu_base
sc.num_gpus = num_gpus
if config == 'e':
G.fmap_base = D.fmap_base = 8 << 10
if d_aug: loss_args.gamma = 100 if gamma is None else gamma
else: D_loss.gamma = 100 if gamma is None else gamma
elif config == 'f':
G.fmap_base = D.fmap_base = 16 << 10
else:
print(' Only configs E and F are implemented')
exit()
if cond:
desc += '-cond'
dataset_args.max_label_size = 'full' # conditioned on full label
if freezeD:
D.freezeD = True
train.resume_with_new_nets = True
if d_aug:
desc += '-daug'
sc.submit_target = dnnlib.SubmitTarget.LOCAL
sc.local.do_not_copy_source_files = True
kwargs = EasyDict(train)
kwargs.update(G_args=G, D_args=D, G_opt_args=G_opt, D_opt_args=D_opt)
kwargs.update(dataset_args=dataset_args,
sched_args=sched,
grid_args=grid,
tf_config=tf_config)
kwargs.update(resume_pkl=resume,
resume_kimg=resume_kimg,
resume_with_new_nets=True)
if ema_kimg is not None:
kwargs.update(G_ema_kimg=ema_kimg)
if d_aug:
kwargs.update(loss_args=loss_args)
else:
kwargs.update(G_loss_args=G_loss, D_loss_args=D_loss)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.submit_config.run_dir_root = train_dir
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)
def run(self,
network_pkl,
run_dir=None,
data_dir=None,
dataset_args=None,
mirror_augment=None,
num_gpus=1,
tf_config=None,
log_results=True,
num_repeats=1,
Gs_kwargs=dict(is_validation=True),
resume_with_new_nets=False,
truncations=[None]):
self._reset(network_pkl=network_pkl,
run_dir=run_dir,
data_dir=data_dir,
dataset_args=dataset_args,
mirror_augment=mirror_augment)
with tf.Graph().as_default(), tflib.create_session(
tf_config).as_default(): # pylint: disable=not-context-manager
self._report_progress(0, 1)
_G, _D, Gs = misc.load_pkl(self._network_pkl)
if resume_with_new_nets:
dataset = self._get_dataset_obj()
G = dnnlib.tflib.Network(
'G',
num_channels=dataset.shape[0],
resolution=dataset.shape[1],
label_size=dataset.label_size,
func_name='training.co_mod_gan.G_main',
pix2pix=dataset.pix2pix)
Gs_new = G.clone('Gs')
Gs_new.copy_vars_from(Gs)
Gs = Gs_new
for t in truncations:
print('truncation={}'.format(t))
self._results = []
time_begin = time.time()
Gs_kwargs.update(truncation_psi_val=t)
self._evaluate(Gs, Gs_kwargs=Gs_kwargs, num_gpus=num_gpus)
self._report_progress(1, 1)
if num_repeats > 1:
records = [
dnnlib.EasyDict(value=[res.value],
suffix=res.suffix,
fmt=res.fmt) for res in self._results
]
for i in range(1, num_repeats):
print(self.get_result_str().strip())
self._results = []
self._report_progress(0, 1)
self._evaluate(Gs,
Gs_kwargs=Gs_kwargs,
num_gpus=num_gpus)
self._report_progress(1, 1)
for rec, res in zip(records, self._results):
rec.value.append(res.value)
self._results = []
for rec in records:
self._report_result(np.mean(rec.value), rec.suffix,
rec.fmt)
self._report_result(np.std(rec.value),
rec.suffix + '-std', rec.fmt)
self._eval_time = time.time() - time_begin # pylint: disable=attribute-defined-outside-init
if log_results:
if run_dir is not None:
log_file = os.path.join(run_dir,
'metric-%s.txt' % self.name)
with dnnlib.util.Logger(log_file, 'a'):
print(self.get_result_str().strip())
else:
print(self.get_result_str().strip())
from training import dataset
import numpy as np
import tensorflow as tf
import training.misc as misc
import matplotlib.pyplot as plt
tfrecord_dir = '../../datasets/cars_v5_512'
tflib.init_tf({'gpu_options.allow_growth': True})
training_set = dataset.TFRecordDataset(tfrecord_dir,
max_label_size='full',
repeat=False,
shuffle_mb=0)
tflib.init_uninitialized_vars()
session = tflib.create_session(None, force_as_default=True)
latent_placeholder = tf.placeholder(tf.float32, shape=(None, 512))
dlatent_placeholder = tf.placeholder(tf.float32, shape=(None, 16, 512))
label_placeholder = tf.placeholder(tf.float32, shape=(None, 127))
G, D, Gs = misc.load_pkl(
'../../results/00155-stylegan2-cars_v5_512-2gpu-config-f/network-snapshot-010467.pkl'
)
num_steps = 10
label_left = training_set.get_random_labels_np(1)
rotation_offset = 108
rotations = label_left[:, rotation_offset:rotation_offset + 8]
rotation_index = np.argmax(rotations, axis=1)
new_rotation_index = ((rotation_index + np.random.choice([-1, 1])) % 8)
new_rotation = np.zeros([8], dtype=np.uint32)