def forward(self):
styles = self.affine(w) * self.weight_gain
x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv)
x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
return x
def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
assert noise_mode in ['random', 'const', 'none']
in_resolution = self.resolution // self.up
misc.assert_shape(
x, [None, self.weight.shape[1], in_resolution, in_resolution])
styles = self.affine(w)
noise = None
if self.use_noise and noise_mode == 'random':
noise = torch.randn(
[x.shape[0], 1, self.resolution, self.resolution],
device=x.device) * self.noise_strength
#noise += self.noise_const.expand_as(noise) * 0
if self.use_noise and noise_mode == 'const':
noise = self.noise_const * self.noise_strength
flip_weight = (self.up == 1) # slightly faster
x = modulated_conv2d(x=x,
weight=self.weight,
styles=styles,
noise=noise,
up=self.up,
padding=self.padding,
resample_filter=self.resample_filter,
flip_weight=flip_weight,
fused_modconv=fused_modconv)
act_gain = self.act_gain * gain
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
x = bias_act.bias_act(x,
self.bias.to(x.dtype),
act=self.activation,
gain=act_gain,
clamp=act_clamp)
return x
def forward(self, x, w, mask, fused_modconv=True):
w_n, w_m, _ = w.shape
styles = self.affine(w.view([w_n * w_m, -1])) * self.weight_gain
x = x.repeat_interleave(w_m, 0)
x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv)
x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
return x.view(w_n, w_m, *x.shape[1:]).mul(mask.unsqueeze(2)).sum(1)
def forward(self,
x,
latmask,
w,
noise_mode='random',
fused_modconv=True,
gain=1):
# def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
assert noise_mode in ['random', 'const', 'none']
in_resolution = self.resolution // self.up
# misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution])
styles = self.affine(w)
noise = None
if self.use_noise and noise_mode == 'random':
# !!! custom
sz = self.size if self.up == 2 and self.size is not None else x.shape[
2:]
noise = torch.randn([x.shape[0], 1, *sz],
device=x.device) * self.noise_strength
# noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength
if self.use_noise and noise_mode == 'const':
noise = self.noise_const * self.noise_strength
# !!! custom noise size
noise_size = self.size if self.up == 2 and self.size is not None and self.resolution > 4 else x.shape[
2:]
noise = fix_size(noise.unsqueeze(0).unsqueeze(0),
noise_size,
scale_type=self.scale_type)[0][0]
# print(x.shape, noise.shape, self.size, self.up)
flip_weight = (self.up == 1) # slightly faster
x = modulated_conv2d(
x=x,
weight=self.weight,
styles=styles,
noise=noise,
up=self.up,
latmask=latmask,
countHW=self.countHW,
splitfine=self.splitfine,
size=self.size,
scale_type=self.scale_type, # !!! custom
padding=self.padding,
resample_filter=self.resample_filter,
flip_weight=flip_weight,
fused_modconv=fused_modconv)
act_gain = self.act_gain * gain
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
x = bias_act.bias_act(x,
self.bias.to(x.dtype),
act=self.activation,
gain=act_gain,
clamp=act_clamp)
return x
def forward(self, x, gain=1):
w = self.weight * self.weight_gain
b = self.bias.to(x.dtype) if self.bias is not None else None
flip_weight = (self.up == 1) # slightly faster
x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight)
act_gain = self.act_gain * gain
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp)
return x
def forward(self, x):
w = self.weight.to(dtype=x.dtype) * self.weight_gain
b = self.bias
if b is not None:
b = b.to(dtype=x.dtype)
if self.bias_gain != 1:
b = b * self.bias_gain
if self.activation == 'linear' and b is not None:
x = torch.addmm(b.unsqueeze(0), x, w.t())
else:
x = x.matmul(w.t())
x = bias_act.bias_act(x, b, act=self.activation)
return x
def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
assert noise_mode in ['random', 'const', 'none']
in_resolution = self.resolution // self.up
misc.assert_shape(
x, [None, self.weight.shape[1], in_resolution, in_resolution])
styles = self.affine(w)
noise = None
if self.cfg.use_noise and noise_mode == 'random':
noise = torch.randn(
[x.shape[0], 1, self.resolution, self.resolution],
device=x.device) * self.noise_strength
if self.cfg.use_noise and noise_mode == 'const':
noise = self.noise_const * self.noise_strength
flip_weight = (self.up == 1) # slightly faster
if self.instance_norm:
x = x / (x.std(dim=[2, 3], keepdim=True) + 1e-8
) # [batch_size, c, h, w]
if self.cfg.fmm.enabled:
x = fmm_modulate_linear(x=x,
weight=self.weight,
styles=styles,
noise=noise,
activation=self.cfg.fmm.activation)
else:
x = modulated_conv2d(x=x,
weight=self.weight,
styles=styles,
noise=noise,
up=self.up,
padding=self.padding,
resample_filter=self.resample_filter,
flip_weight=flip_weight,
fused_modconv=fused_modconv)
act_gain = self.act_gain * gain
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
x = bias_act.bias_act(x,
self.bias.to(x.dtype),
act=self.activation,
gain=act_gain,
clamp=act_clamp)
return x
def forward(self, x, w, mask, noise_mode='random', fused_modconv=True, gain=1):
assert noise_mode in ['random', 'const', 'none']
in_resolution = self.resolution // self.up
misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution])
w_n, w_m, _ = w.shape
styles = self.affine(w.view([w_n * w_m, -1]))
noise = None
if self.use_noise and noise_mode == 'random':
noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength
if self.use_noise and noise_mode == 'const':
noise = self.noise_const * self.noise_strength
flip_weight = (self.up == 1) # slightly faster
act_gain = self.act_gain * gain
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
x = x.repeat_interleave(w_m, 0)
x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)
x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
return x.view(w_n, w_m, *x.shape[1:]).mul(mask.unsqueeze(2)).sum(1)
