by Piotr Bialecki and Thomas Viehmann
This is a hacky addition of the Discriminator to our StyleGAN Generator notebook that is much nicer and annotated. This here is even less ready for consumption. We also don't have losses or training.
#import os
#os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import pickle
import numpy as np
%matplotlib inline
from matplotlib import pyplot
import IPython
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
device = 'cpu'
class MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2**(0.5), use_wscale=False, lrmul=1, bias=True):
super().__init__()
he_std = gain * input_size**(-0.5) # He init
# Equalized learning rate and custom learning rate multiplier.
if use_wscale:
init_std = 1.0 / lrmul
self.w_mul = he_std * lrmul
else:
init_std = he_std / lrmul
self.w_mul = lrmul
self.weight = torch.nn.Parameter(torch.randn(output_size, input_size) * init_std)
if bias:
self.bias = torch.nn.Parameter(torch.zeros(output_size))
self.b_mul = lrmul
else:
self.bias = None
def forward(self, x):
bias = self.bias
if bias is not None:
bias = bias * self.b_mul
return F.linear(x, self.weight * self.w_mul, bias)
class MyConv2d(nn.Module):
"""Conv layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_channels, output_channels, kernel_size, stride=1, gain=2**(0.5), use_wscale=False, lrmul=1, bias=True,
intermediate=None, upscale=False, downscale=False):
super().__init__()
if upscale:
self.upscale = Upscale2d()
else:
self.upscale = None
if downscale:
self.downscale = Downscale2d()
else:
self.downscale = None
he_std = gain * (input_channels * kernel_size ** 2) ** (-0.5) # He init
self.kernel_size = kernel_size
if use_wscale:
init_std = 1.0 / lrmul
self.w_mul = he_std * lrmul
else:
init_std = he_std / lrmul
self.w_mul = lrmul
self.weight = torch.nn.Parameter(torch.randn(output_channels, input_channels, kernel_size, kernel_size) * init_std)
if bias:
self.bias = torch.nn.Parameter(torch.zeros(output_channels))
self.b_mul = lrmul
else:
self.bias = None
self.intermediate = intermediate
def forward(self, x):
bias = self.bias
if bias is not None:
bias = bias * self.b_mul
have_convolution = False
if self.upscale is not None and min(x.shape[2:]) * 2 >= 128:
# this is the fused upscale + conv from StyleGAN, sadly this seems incompatible with the non-fused way
# this really needs to be cleaned up and go into the conv...
w = self.weight * self.w_mul
w = w.permute(1, 0, 2, 3)
# probably applying a conv on w would be more efficient. also this quadruples the weight (average)?!
w = F.pad(w, (1,1,1,1))
w = w[:, :, 1:, 1:]+ w[:, :, :-1, 1:] + w[:, :, 1:, :-1] + w[:, :, :-1, :-1]
x = F.conv_transpose2d(x, w, stride=2, padding=(w.size(-1)-1)//2)
have_convolution = True
elif self.upscale is not None:
x = self.upscale(x)
downscale = self.downscale
intermediate = self.intermediate
if downscale is not None and min(x.shape[2:]) >= 128:
w = self.weight * self.w_mul
w = F.pad(w, (1,1,1,1))
# in contrast to upscale, this is a mean...
w = (w[:, :, 1:, 1:]+ w[:, :, :-1, 1:] + w[:, :, 1:, :-1] + w[:, :, :-1, :-1])*0.25 # avg_pool?
x = F.conv2d(x, w, stride=2, padding=(w.size(-1)-1)//2)
have_convolution = True
downscale = None
elif downscale is not None:
assert intermediate is None
intermediate = downscale
if not have_convolution and intermediate is None:
return F.conv2d(x, self.weight * self.w_mul, bias, padding=self.kernel_size//2)
elif not have_convolution:
x = F.conv2d(x, self.weight * self.w_mul, None, padding=self.kernel_size//2)
if intermediate is not None:
x = intermediate(x)
if bias is not None:
x = x + bias.view(1, -1, 1, 1)
return x
class NoiseLayer(nn.Module):
"""adds noise. noise is per pixel (constant over channels) with per-channel weight"""
def __init__(self, channels):
super().__init__()
self.weight = nn.Parameter(torch.zeros(channels))
self.noise = None
def forward(self, x, noise=None):
if noise is None and self.noise is None:
noise = torch.randn(x.size(0), 1, x.size(2), x.size(3), device=x.device, dtype=x.dtype)
elif noise is None:
# here is a little trick: if you get all the noiselayers and set each
# modules .noise attribute, you can have pre-defined noise.
# Very useful for analysis
noise = self.noise
x = x + self.weight.view(1, -1, 1, 1) * noise
return x
class StyleMod(nn.Module):
def __init__(self, latent_size, channels, use_wscale):
super(StyleMod, self).__init__()
self.lin = MyLinear(latent_size,
channels * 2,
gain=1.0, use_wscale=use_wscale)
def forward(self, x, latent):
style = self.lin(latent) # style => [batch_size, n_channels*2]
shape = [-1, 2, x.size(1)] + (x.dim() - 2) * [1]
style = style.view(shape) # [batch_size, 2, n_channels, ...]
x = x * (style[:, 0] + 1.) + style[:, 1]
return x
class PixelNormLayer(nn.Module):
def __init__(self, epsilon=1e-8):
super().__init__()
self.epsilon = epsilon
def forward(self, x):
return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + self.epsilon)
class BlurLayer(nn.Module):
def __init__(self, kernel=[1, 2, 1], normalize=True, flip=False, stride=1):
super(BlurLayer, self).__init__()
kernel = torch.tensor(kernel, dtype=torch.float32)
kernel = kernel[:, None] * kernel[None, :]
kernel = kernel[None, None]
if normalize:
kernel = kernel / kernel.sum()
if flip:
kernel = kernel[:, :, ::-1, ::-1]
self.register_buffer('kernel', kernel)
self.stride = stride
def forward(self, x):
# expand kernel channels
kernel = self.kernel.expand(x.size(1), -1, -1, -1)
x = F.conv2d(
x,
kernel,
stride=self.stride,
padding=int((self.kernel.size(2)-1)/2),
groups=x.size(1)
)
return x
def upscale2d(x, factor=2, gain=1):
assert x.dim() == 4
if gain != 1:
x = x * gain
if factor != 1:
shape = x.shape
x = x.view(shape[0], shape[1], shape[2], 1, shape[3], 1).expand(-1, -1, -1, factor, -1, factor)
x = x.contiguous().view(shape[0], shape[1], factor * shape[2], factor * shape[3])
return x
class Upscale2d(nn.Module):
def __init__(self, factor=2, gain=1):
super().__init__()
assert isinstance(factor, int) and factor >= 1
self.gain = gain
self.factor = factor
def forward(self, x):
return upscale2d(x, factor=self.factor, gain=self.gain)
class G_mapping(nn.Sequential):
def __init__(self, nonlinearity='lrelu', use_wscale=True):
act, gain = {'relu': (torch.relu, np.sqrt(2)),
'lrelu': (nn.LeakyReLU(negative_slope=0.2), np.sqrt(2))}[nonlinearity]
layers = [
('pixel_norm', PixelNormLayer()),
('dense0', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
('dense0_act', act),
('dense1', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
('dense1_act', act),
('dense2', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
('dense2_act', act),
('dense3', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
('dense3_act', act),
('dense4', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
('dense4_act', act),
('dense5', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
('dense5_act', act),
('dense6', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
('dense6_act', act),
('dense7', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
('dense7_act', act)
]
super().__init__(OrderedDict(layers))
def forward(self, x):
x = super().forward(x)
# Broadcast
x = x.unsqueeze(1).expand(-1, 18, -1)
return x
class Truncation(nn.Module):
def __init__(self, avg_latent, max_layer=8, threshold=0.7):
super().__init__()
self.max_layer = max_layer
self.threshold = threshold
self.register_buffer('avg_latent', avg_latent)
def forward(self, x):
assert x.dim() == 3
interp = torch.lerp(self.avg_latent, x, self.threshold)
do_trunc = (torch.arange(x.size(1)) < self.max_layer).view(1, -1, 1)
return torch.where(do_trunc, interp, x)
class LayerEpilogue(nn.Module):
"""Things to do at the end of each layer."""
def __init__(self, channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
super().__init__()
layers = []
if use_noise:
layers.append(('noise', NoiseLayer(channels)))
layers.append(('activation', activation_layer))
if use_pixel_norm:
layers.append(('pixel_norm', PixelNorm()))
if use_instance_norm:
layers.append(('instance_norm', nn.InstanceNorm2d(channels)))
self.top_epi = nn.Sequential(OrderedDict(layers))
if use_styles:
self.style_mod = StyleMod(dlatent_size, channels, use_wscale=use_wscale)
else:
self.style_mod = None
def forward(self, x, dlatents_in_slice=None):
x = self.top_epi(x)
if self.style_mod is not None:
x = self.style_mod(x, dlatents_in_slice)
else:
assert dlatents_in_slice is None
return x
class InputBlock(nn.Module):
def __init__(self, nf, dlatent_size, const_input_layer, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
super().__init__()
self.const_input_layer = const_input_layer
self.nf = nf
if self.const_input_layer:
# called 'const' in tf
self.const = nn.Parameter(torch.ones(1, nf, 4, 4))
self.bias = nn.Parameter(torch.ones(nf))
else:
self.dense = MyLinear(dlatent_size, nf*16, gain=gain/4, use_wscale=use_wscale) # tweak gain to match the official implementation of Progressing GAN
self.epi1 = LayerEpilogue(nf, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
self.conv = MyConv2d(nf, nf, 3, gain=gain, use_wscale=use_wscale)
self.epi2 = LayerEpilogue(nf, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
def forward(self, dlatents_in_range):
batch_size = dlatents_in_range.size(0)
if self.const_input_layer:
x = self.const.expand(batch_size, -1, -1, -1)
x = x + self.bias.view(1, -1, 1, 1)
else:
x = self.dense(dlatents_in_range[:, 0]).view(batch_size, self.nf, 4, 4)
x = self.epi1(x, dlatents_in_range[:, 0])
x = self.conv(x)
x = self.epi2(x, dlatents_in_range[:, 1])
return x
class GSynthesisBlock(nn.Module):
def __init__(self, in_channels, out_channels, blur_filter, dlatent_size, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
# 2**res x 2**res # res = 3..resolution_log2
super().__init__()
if blur_filter:
blur = BlurLayer(blur_filter)
else:
blur = None
self.conv0_up = MyConv2d(in_channels, out_channels, kernel_size=3, gain=gain, use_wscale=use_wscale,
intermediate=blur, upscale=True)
self.epi1 = LayerEpilogue(out_channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
self.conv1 = MyConv2d(out_channels, out_channels, kernel_size=3, gain=gain, use_wscale=use_wscale)
self.epi2 = LayerEpilogue(out_channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
def forward(self, x, dlatents_in_range):
x = self.conv0_up(x)
x = self.epi1(x, dlatents_in_range[:, 0])
x = self.conv1(x)
x = self.epi2(x, dlatents_in_range[:, 1])
return x
class G_synthesis(nn.Module):
def __init__(self,
dlatent_size = 512, # Disentangled latent (W) dimensionality.
num_channels = 3, # Number of output color channels.
resolution = 1024, # Output resolution.
fmap_base = 8192, # Overall multiplier for the number of feature maps.
fmap_decay = 1.0, # log2 feature map reduction when doubling the resolution.
fmap_max = 512, # Maximum number of feature maps in any layer.
use_styles = True, # Enable style inputs?
const_input_layer = True, # First layer is a learned constant?
use_noise = True, # Enable noise inputs?
randomize_noise = True, # True = randomize noise inputs every time (non-deterministic), False = read noise inputs from variables.
nonlinearity = 'lrelu', # Activation function: 'relu', 'lrelu'
use_wscale = True, # Enable equalized learning rate?
use_pixel_norm = False, # Enable pixelwise feature vector normalization?
use_instance_norm = True, # Enable instance normalization?
dtype = torch.float32, # Data type to use for activations and outputs.
fused_scale = 'auto', # True = fused convolution + scaling, False = separate ops, 'auto' = decide automatically.
blur_filter = [1,2,1], # Low-pass filter to apply when resampling activations. None = no filtering.
structure = 'auto', # 'fixed' = no progressive growing, 'linear' = human-readable, 'recursive' = efficient, 'auto' = select automatically.
is_template_graph = False, # True = template graph constructed by the Network class, False = actual evaluation.
force_clean_graph = False, # True = construct a clean graph that looks nice in TensorBoard, False = default behavior.
):
super().__init__()
def nf(stage):
return min(int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_max)
self.dlatent_size = dlatent_size
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
if is_template_graph: force_clean_graph = True
if force_clean_graph: randomize_noise = False
if structure == 'auto': structure = 'linear' if force_clean_graph else 'recursive'
act, gain = {'relu': (torch.relu, np.sqrt(2)),
'lrelu': (nn.LeakyReLU(negative_slope=0.2), np.sqrt(2))}[nonlinearity]
num_layers = resolution_log2 * 2 - 2
num_styles = num_layers if use_styles else 1
torgbs = []
blocks = []
for res in range(2, resolution_log2 + 1):
channels = nf(res-1)
name = '{s}x{s}'.format(s=2**res)
if res == 2:
blocks.append((name,
InputBlock(channels, dlatent_size, const_input_layer, gain, use_wscale,
use_noise, use_pixel_norm, use_instance_norm, use_styles, act)))
else:
blocks.append((name,
GSynthesisBlock(last_channels, channels, blur_filter, dlatent_size, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, act)))
last_channels = channels
self.torgb = MyConv2d(channels, num_channels, 1, gain=1, use_wscale=use_wscale)
self.blocks = nn.ModuleDict(OrderedDict(blocks))
def forward(self, dlatents_in):
# Input: Disentangled latents (W) [minibatch, num_layers, dlatent_size].
# lod_in = tf.cast(tf.get_variable('lod', initializer=np.float32(0), trainable=False), dtype)
batch_size = dlatents_in.size(0)
for i, m in enumerate(self.blocks.values()):
if i == 0:
x = m(dlatents_in[:, 2*i:2*i+2])
else:
x = m(x, dlatents_in[:, 2*i:2*i+2])
rgb = self.torgb(x)
return rgb
g_all = nn.Sequential(OrderedDict([
('g_mapping', G_mapping()),
#('truncation', Truncation(avg_latent)),
('g_synthesis', G_synthesis())
]))
Yes, we can! The following can be used to convert them from author's format. We have already done this for you, and you can get the weights from here.
Note that the weights are taken from the reference implementation distributed by NVidia Corporation as Licensed under the CC-BY-NC 4.0 license. As such, the same applies here.
For completeness, our conversion is below, but you can skip it if you download the PyTorch-ready weights.
if 0:
# this can be run to get the weights, but you need the reference implementation and weights
import dnnlib, dnnlib.tflib, pickle, torch, collections
dnnlib.tflib.init_tf()
weights = pickle.load(open('./karras2019stylegan-ffhq-1024x1024.pkl','rb'))
weights_pt = [collections.OrderedDict([(k, torch.from_numpy(v.value().eval())) for k,v in w.trainables.items()]) for w in weights]
torch.save(weights_pt, './karras2019stylegan-ffhq-1024x1024.pt')
if 1:
# then on the PyTorch side run
state_G, state_D, state_Gs = torch.load('../karras2019stylegan-ffhq-1024x1024.pt')
def key_translate(k):
k = k.lower().split('/')
if k[0] == 'g_synthesis':
if not k[1].startswith('torgb'):
k.insert(1, 'blocks')
k = '.'.join(k)
k = (k.replace('const.const','const').replace('const.bias','bias').replace('const.stylemod','epi1.style_mod.lin')
.replace('const.noise.weight','epi1.top_epi.noise.weight')
.replace('conv.noise.weight','epi2.top_epi.noise.weight')
.replace('conv.stylemod','epi2.style_mod.lin')
.replace('conv0_up.noise.weight', 'epi1.top_epi.noise.weight')
.replace('conv0_up.stylemod','epi1.style_mod.lin')
.replace('conv1.noise.weight', 'epi2.top_epi.noise.weight')
.replace('conv1.stylemod','epi2.style_mod.lin')
.replace('torgb_lod0','torgb')
.replace('fromrgb_lod0','fromrgb'))
if 'torgb_lod' in k or 'fromrgb_lod' in k: # we don't want the lower layers to/from RGB
k = None
return k
def weight_translate(k, w):
k = key_translate(k)
if k.endswith('.weight'):
if w.dim() == 2:
w = w.t()
elif w.dim() == 1:
pass
else:
assert w.dim() == 4
w = w.permute(3, 2, 0, 1)
return w
if 0:
param_dict = {key_translate(k) : weight_translate(k, v) for k,v in state_Gs.items() if key_translate(k) is not None}
if 1:
sd_shapes = {k : v.shape for k,v in g_all.state_dict().items()}
param_shapes = {k : v.shape for k,v in param_dict.items() }
for k in list(sd_shapes)+list(param_shapes):
pds = param_shapes.get(k)
sds = sd_shapes.get(k)
if pds is None:
print ("sd only", k, sds)
elif sds is None:
print ("pd only", k, pds)
elif sds != pds:
print ("mismatch!", k, pds, sds)
g_all.load_state_dict(param_dict, strict=False) # needed for the blur kernels
torch.save(g_all.state_dict(), './karras2019stylegan-ffhq-1024x1024.for_g_all.pt')
if 0:
param_dict = {key_translate(k) : weight_translate(k, v) for k,v in state_D.items() if key_translate(k) is not None}
if 1:
sd_shapes = {k : v.shape for k,v in d_basic.state_dict().items()}
param_shapes = {k : v.shape for k,v in param_dict.items() }
for k in list(sd_shapes)+list(param_shapes):
pds = param_shapes.get(k)
sds = sd_shapes.get(k)
if pds is None:
print ("sd only", k, sds)
elif sds is None:
print ("pd only", k, pds)
elif sds != pds:
print ("mismatch!", k, pds, sds)
d_basic.load_state_dict(param_dict, strict=False) # needed for the blur kernels
torch.save(d_basic.state_dict(), '../karras2019stylegan-ffhq-1024x1024.for_d_basic.pt')
Let's load the weights.
g_all.load_state_dict(torch.load('../karras2019stylegan-ffhq-1024x1024.for_g_all.pt', map_location=device))
g_all.eval()
g_all.to(device);
Now we're all set! Let's generate faces!
if 0:
%matplotlib inline
from matplotlib import pyplot
import torchvision
g_all.eval()
g_all.to(device)
torch.manual_seed(500)
nb_rows = 2
nb_cols = 8
nb_samples = nb_rows * nb_cols
latents = torch.randn(nb_samples, 512, device=device)
with torch.no_grad():
imgs = g_all(latents)
imgs = (imgs.clamp(-1, 1) + 1) / 2.0 # normalization to 0..1 range
imgs = imgs.cpu()
imgs = torchvision.utils.make_grid(imgs, nrow=nb_cols)
pyplot.figure(figsize=(15, 6))
pyplot.imshow(imgs.permute(1, 2, 0).detach().numpy())
class StddevLayer(nn.Module):
def __init__(self, group_size=4, num_new_features=1):
super().__init__()
self.group_size = 4
self.num_new_features = 1
def forward(self, x):
b, c, h, w = x.shape
group_size = min(self.group_size, b)
y = x.reshape([group_size, -1, self.num_new_features,
c // self.num_new_features, h, w])
y = y - y.mean(0, keepdim=True)
y = (y**2).mean(0, keepdim=True)
y = (y + 1e-8)**0.5
y = y.mean([3, 4, 5], keepdim=True).squeeze(3) # don't keep the meaned-out channels
y = y.expand(group_size, -1, -1, h, w).clone().reshape(b, self.num_new_features, h, w)
z = torch.cat([x, y], dim=1)
return z
class Downscale2d(nn.Module):
def __init__(self, factor=2, gain=1):
super().__init__()
assert isinstance(factor, int) and factor >= 1
self.factor = factor
self.gain = gain
if factor == 2:
f = [np.sqrt(gain) / factor] * factor
self.blur = BlurLayer(kernel=f, normalize=False, stride=factor)
else:
self.blur = None
def forward(self, x):
assert x.dim()==4
# 2x2, float32 => downscale using _blur2d().
if self.blur is not None and x.dtype == torch.float32:
return self.blur(x)
# Apply gain.
if self.gain != 1:
x = x * self.gain
# No-op => early exit.
if factor == 1:
return x
# Large factor => downscale using tf.nn.avg_pool().
# NOTE: Requires tf_config['graph_options.place_pruned_graph']=True to work.
return F.avg_pool2d(x, self.factor)
class DiscriminatorBlock(nn.Sequential):
def __init__(self, in_channels, out_channels, gain, use_wscale, activation_layer):
super().__init__(OrderedDict([
('conv0', MyConv2d(in_channels, in_channels, 3, gain=gain, use_wscale=use_wscale)), # out channels nf(res-1)
('act0', activation_layer),
('blur', BlurLayer()),
('conv1_down', MyConv2d(in_channels, out_channels, 3, gain=gain, use_wscale=use_wscale, downscale=True)),
('act1', activation_layer)]))
class View(nn.Module):
def __init__(self, *shape):
super().__init__()
self.shape = shape
def forward(self, x):
return x.view(x.size(0), *self.shape)
class DiscriminatorTop(nn.Sequential):
def __init__(self, mbstd_group_size, mbstd_num_features, in_channels, intermediate_channels, gain, use_wscale, activation_layer, resolution=4, in_channels2=None, output_features=1, last_gain=1):
layers = []
if mbstd_group_size > 1:
layers.append(('stddev_layer', StddevLayer(mbstd_group_size, mbstd_num_features)))
if in_channels2 is None:
in_channels2 = in_channels
layers.append(('conv', MyConv2d(in_channels + mbstd_num_features, in_channels2, 3, gain=gain, use_wscale=use_wscale)))
layers.append(('act0', activation_layer))
layers.append(('view', View(-1)))
layers.append(('dense0', MyLinear(in_channels2*resolution*resolution, intermediate_channels, gain=gain, use_wscale=use_wscale)))
layers.append(('act1', activation_layer))
layers.append(('dense1', MyLinear(intermediate_channels, output_features, gain=last_gain, use_wscale=use_wscale)))
super().__init__(OrderedDict(layers))
class D_basic(nn.Sequential):
def __init__(self,
#images_in, # First input: Images [minibatch, channel, height, width].
#labels_in, # Second input: Labels [minibatch, label_size].
num_channels = 3, # Number of input color channels. Overridden based on dataset.
resolution = 1024, # Input resolution. Overridden based on dataset.
fmap_base = 8192, # Overall multiplier for the number of feature maps.
fmap_decay = 1.0, # log2 feature map reduction when doubling the resolution.
fmap_max = 512, # Maximum number of feature maps in any layer.
nonlinearity = 'lrelu', # Activation function: 'relu', 'lrelu',
use_wscale = True, # Enable equalized learning rate?
mbstd_group_size = 4, # Group size for the minibatch standard deviation layer, 0 = disable.
mbstd_num_features = 1, # Number of features for the minibatch standard deviation layer.
#blur_filter = [1,2,1], # Low-pass filter to apply when resampling activations. None = no filtering.
):
self.mbstd_group_size = 4
self.mbstd_num_features = 1
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage):
return min(int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_max)
act, gain = {'relu': (torch.relu, np.sqrt(2)),
'lrelu': (nn.LeakyReLU(negative_slope=0.2), np.sqrt(2))}[nonlinearity]
self.gain = gain
self.use_wscale = use_wscale
super().__init__(OrderedDict([
('fromrgb', MyConv2d(num_channels, nf(resolution_log2-1), 1, gain=gain, use_wscale=use_wscale)),
('act', act)]
+[('{s}x{s}'.format(s=2**res), DiscriminatorBlock(nf(res-1), nf(res-2), gain=gain, use_wscale=use_wscale, activation_layer=act)) for res in range(resolution_log2, 2, -1)]
+[('4x4', DiscriminatorTop(mbstd_group_size, mbstd_num_features, nf(2), nf(2), gain=gain, use_wscale=use_wscale, activation_layer=act))]))
if 1:
d_basic = D_basic()
d_basic.load_state_dict(torch.load('../karras2019stylegan-ffhq-1024x1024.for_d_basic.pt', map_location=device))
d_basic.to(device)