def __init__(self,
layer,
dim=0,
power_iters=1,
eps=1e-12,
dtype='float32'):
super(Spectralnorm, self).__init__()
self.dim = dim
self.power_iters = power_iters
self.eps = eps
self.layer = layer
self.has_bias = False
self.is_fc = False
if 'bias' in layer._parameters:
bias = layer._parameters['bias']
self.bias_orig = self.create_parameter(bias.shape,
dtype=bias.dtype)
self.bias_orig.set_value(bias)
self.has_bias = True
del layer._parameters['bias']
weight = layer._parameters['weight']
self.weight_orig = self.create_parameter(weight.shape,
dtype=weight.dtype)
self.weight_orig.set_value(weight)
if isinstance(layer, dygraph.Linear):
self.is_fc = True
self.spectral_norm = dygraph.SpectralNorm(layer.weight.shape[::-1],
dim, power_iters, eps,
dtype)
else:
self.spectral_norm = dygraph.SpectralNorm(layer.weight.shape, dim,
power_iters, eps, dtype)
del layer._parameters['weight']
def __init__(self,
layer,
dim=0,
power_iters=1,
eps=1e-12,
dtype='float32'):
super(Spectralnorm, self).__init__()
self.spectral_norm = dygraph.SpectralNorm(layer.weight.shape, dim,
power_iters, eps, dtype)
self.dim = dim
self.power_iters = power_iters
self.eps = eps
self.layer = layer
weight = layer._parameters['weight']
del layer._parameters['weight']
self.weight_orig = self.create_parameter(weight.shape,
dtype=weight.dtype)
self.weight_orig.set_value(weight)
def __init__(self,
num_channels=3,
block_expansion=64,
num_blocks=4,
max_features=512,
sn=False,
use_kp=False,
num_kp=10,
kp_variance=0.01,
**kwargs):
super(Discriminator, self).__init__()
down_blocks = []
for i in range(num_blocks):
down_blocks.append(
DownBlock2d(num_channels + num_kp * use_kp if i == 0 else min(
max_features, block_expansion * (2**i)),
min(max_features, block_expansion * (2**(i + 1))),
norm=(i != 0),
kernel_size=4,
pool=(i != num_blocks - 1),
sn=sn))
self.down_blocks = dygraph.LayerList(down_blocks)
self.conv = dygraph.Conv2D(
self.down_blocks[len(self.down_blocks) -
1].conv.parameters()[0].shape[0],
1,
filter_size=1)
if sn:
self.sn = dygraph.SpectralNorm(self.conv.parameters()[0].shape,
dim=0)
else:
self.sn = None
self.use_kp = use_kp
self.kp_variance = kp_variance
def __init__(self,
in_features,
out_features,
norm=False,
kernel_size=4,
pool=False,
sn=False):
super(DownBlock2d, self).__init__()
self.conv = dygraph.Conv2D(in_features,
out_features,
filter_size=kernel_size)
if sn:
self.sn = dygraph.SpectralNorm(self.conv.weight.shape, dim=0)
else:
self.sn = None
if norm:
self.norm = dygraph.InstanceNorm(num_channels=out_features,
epsilon=1e-05,
dtype='float32')
else:
self.norm = None
self.pool = pool