def __init__(self,
total_batches,
batch_size,
num_layers=8,
image_width=512,
loss_coef=100,
theta_initial=None,
theta_hidden=None):
super().__init__()
# load clip
self.loss_coef = loss_coef
self.image_width = image_width
self.batch_size = batch_size
self.total_batches = total_batches
self.num_batches_processed = 0
w0 = default(theta_hidden, 30.)
w0_initial = default(theta_initial, 30.)
siren = SirenNet(dim_in=2,
dim_hidden=256,
num_layers=num_layers,
dim_out=3,
use_bias=True,
w0=w0,
w0_initial=w0_initial)
self.model = SirenWrapper(siren,
image_width=image_width,
image_height=image_width)
self.generate_size_schedule()
def __init__(
self,
total_batches,
batch_size,
num_layers=8,
image_width=512,
loss_coef=100,
):
super().__init__()
self.loss_coef = loss_coef
self.image_width = image_width
self.batch_size = batch_size
self.total_batches = total_batches
self.num_batches_processed = 0
siren = SirenNet(dim_in=2,
dim_hidden=256,
num_layers=num_layers,
dim_out=3,
use_bias=True)
self.model = SirenWrapper(siren,
image_width=image_width,
image_height=image_width)
self.generate_size_schedule()
def __init__(
self,
total_batches,
batch_size,
num_layers=8,
image_width=512,
loss_coef=100,
theta_initial=None,
theta_hidden=None,
lower_bound_cutout=0.1, # should be smaller than 0.8
upper_bound_cutout=1.0,
saturate_bound=False,
):
super().__init__()
# load clip
self.loss_coef = loss_coef
self.image_width = image_width
self.batch_size = batch_size
self.total_batches = total_batches
self.num_batches_processed = 0
w0 = default(theta_hidden, 30.)
w0_initial = default(theta_initial, 30.)
siren = SirenNet(
dim_in=2,
dim_hidden=256,
num_layers=num_layers,
dim_out=3,
use_bias=True,
w0=w0,
w0_initial=w0_initial
)
self.model = SirenWrapper(
siren,
image_width=image_width,
image_height=image_width
)
self.saturate_bound = saturate_bound
self.saturate_limit = 0.75 # cutouts above this value lead to destabilization
self.lower_bound_cutout = lower_bound_cutout
self.upper_bound_cutout = upper_bound_cutout
def __init__(
self,
clip_perceptor,
clip_norm,
input_res,
total_batches,
batch_size,
num_layers=8,
image_width=512,
loss_coef=100,
theta_initial=None,
theta_hidden=None,
lower_bound_cutout=0.1, # should be smaller than 0.8
upper_bound_cutout=1.0,
saturate_bound=False,
gauss_sampling=False,
gauss_mean=0.6,
gauss_std=0.2,
do_cutout=True,
center_bias=False,
center_focus=2,
hidden_size=256,
averaging_weight=0.3,
):
super().__init__()
# load clip
self.perceptor = clip_perceptor
self.input_resolution = input_res
self.normalize_image = clip_norm
self.loss_coef = loss_coef
self.image_width = image_width
self.batch_size = batch_size
self.total_batches = total_batches
self.num_batches_processed = 0
w0 = default(theta_hidden, 30.)
w0_initial = default(theta_initial, 30.)
siren = SirenNet(dim_in=2,
dim_hidden=hidden_size,
num_layers=num_layers,
dim_out=1,
use_bias=True,
w0=w0,
w0_initial=w0_initial)
self.model = SirenWrapper(siren,
image_width=image_width,
image_height=image_width)
self.saturate_bound = saturate_bound
self.saturate_limit = 0.75 # cutouts above this value lead to destabilization
self.lower_bound_cutout = lower_bound_cutout
self.upper_bound_cutout = upper_bound_cutout
self.gauss_sampling = gauss_sampling
self.gauss_mean = gauss_mean
self.gauss_std = gauss_std
self.do_cutout = do_cutout
self.center_bias = center_bias
self.center_focus = center_focus
self.averaging_weight = averaging_weight