from core.leras import nn
tf = nn.tf
class DeepFakeArchi(nn.ArchiBase):
"""
resolution
mod None - default
'quick'
opts ''
''
't'
"""
def __init__(self, resolution, use_fp16=False, mod=None, opts=None):
super().__init__()
if opts is None:
opts = ''
conv_dtype = tf.float16 if use_fp16 else tf.float32
if 'c' in opts:
def act(x, alpha=0.1):
return x*tf.cos(x)
else:
def act(x, alpha=0.1):
return tf.nn.leaky_relu(x, alpha)
if mod is None:
class Downscale(nn.ModelBase):
def __init__(self, in_ch, out_ch, kernel_size=5, *kwargs ):
self.in_ch = in_ch
self.out_ch = out_ch
self.kernel_size = kernel_size
super().__init__(*kwargs)
def on_build(self, *args, **kwargs ):
self.conv1 = nn.Conv2D( self.in_ch, self.out_ch, kernel_size=self.kernel_size, strides=2, padding='SAME', dtype=conv_dtype)
def forward(self, x):
x = self.conv1(x)
x = act(x, 0.1)
return x
def get_out_ch(self):
return self.out_ch
class DownscaleBlock(nn.ModelBase):
def on_build(self, in_ch, ch, n_downscales, kernel_size):
self.downs = []
last_ch = in_ch
for i in range(n_downscales):
cur_ch = ch*( min(2**i, 8) )
self.downs.append ( Downscale(last_ch, cur_ch, kernel_size=kernel_size))
last_ch = self.downs[-1].get_out_ch()
def forward(self, inp):
x = inp
for down in self.downs:
x = down(x)
return x
class Upscale(nn.ModelBase):
def on_build(self, in_ch, out_ch, kernel_size=3):
self.conv1 = nn.Conv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
def forward(self, x):
x = self.conv1(x)
x = act(x, 0.1)
x = nn.depth_to_space(x, 2)
return x
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, kernel_size=3):
self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
def forward(self, inp):
x = self.conv1(inp)
x = act(x, 0.2)
x = self.conv2(x)
x = act(inp + x, 0.2)
return x
class Encoder(nn.ModelBase):
def __init__(self, in_ch, e_ch, **kwargs ):
self.in_ch = in_ch
self.e_ch = e_ch
super().__init__(**kwargs)
def on_build(self):
if 't' in opts:
self.down1 = Downscale(self.in_ch, self.e_ch, kernel_size=5)
self.res1 = ResidualBlock(self.e_ch)
self.down2 = Downscale(self.e_ch, self.e_ch*2, kernel_size=5)
self.down3 = Downscale(self.e_ch*2, self.e_ch*4, kernel_size=5)
self.down4 = Downscale(self.e_ch*4, self.e_ch*8, kernel_size=5)
self.down5 = Downscale(self.e_ch*8, self.e_ch*8, kernel_size=5)
self.res5 = ResidualBlock(self.e_ch*8)
else:
self.down1 = DownscaleBlock(self.in_ch, self.e_ch, n_downscales=4 if 't' not in opts else 5, kernel_size=5)
def forward(self, x):
if use_fp16:
x = tf.cast(x, tf.float16)
if 't' in opts:
x = self.down1(x)
x = self.res1(x)
x = self.down2(x)
x = self.down3(x)
x = self.down4(x)
x = self.down5(x)
x = self.res5(x)
else:
x = self.down1(x)
x = nn.flatten(x)
if 'u' in opts:
x = nn.pixel_norm(x, axes=-1)
if use_fp16:
x = tf.cast(x, tf.float32)
return x
def get_out_res(self, res):
return res // ( (2**4) if 't' not in opts else (2**5) )
def get_out_ch(self):
return self.e_ch * 8
lowest_dense_res = resolution // (32 if 'd' in opts else 16)
class Inter(nn.ModelBase):
def __init__(self, in_ch, ae_ch, ae_out_ch, **kwargs):
self.in_ch, self.ae_ch, self.ae_out_ch = in_ch, ae_ch, ae_out_ch
super().__init__(**kwargs)
def on_build(self):
in_ch, ae_ch, ae_out_ch = self.in_ch, self.ae_ch, self.ae_out_ch
self.dense1 = nn.Dense( in_ch, ae_ch )
self.dense2 = nn.Dense( ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch )
if 't' not in opts:
self.upscale1 = Upscale(ae_out_ch, ae_out_ch)
def forward(self, inp):
x = inp
x = self.dense1(x)
x = self.dense2(x)
x = nn.reshape_4D (x, lowest_dense_res, lowest_dense_res, self.ae_out_ch)
if use_fp16:
x = tf.cast(x, tf.float16)
if 't' not in opts:
x = self.upscale1(x)
return x
def get_out_res(self):
return lowest_dense_res * 2 if 't' not in opts else lowest_dense_res
def get_out_ch(self):
return self.ae_out_ch
class Decoder(nn.ModelBase):
def on_build(self, in_ch, d_ch, d_mask_ch):
if 't' not in opts:
self.upscale0 = Upscale(in_ch, d_ch*8, kernel_size=3)
self.upscale1 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
self.upscale2 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
self.res0 = ResidualBlock(d_ch*8, kernel_size=3)
self.res1 = ResidualBlock(d_ch*4, kernel_size=3)
self.res2 = ResidualBlock(d_ch*2, kernel_size=3)
self.upscalem0 = Upscale(in_ch, d_mask_ch*8, kernel_size=3)
self.upscalem1 = Upscale(d_mask_ch*8, d_mask_ch*4, kernel_size=3)
self.upscalem2 = Upscale(d_mask_ch*4, d_mask_ch*2, kernel_size=3)
self.out_conv = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME', dtype=conv_dtype)
if 'd' in opts:
self.out_conv1 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
self.out_conv2 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
self.out_conv3 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
self.upscalem3 = Upscale(d_mask_ch*2, d_mask_ch*1, kernel_size=3)
self.out_convm = nn.Conv2D( d_mask_ch*1, 1, kernel_size=1, padding='SAME', dtype=conv_dtype)
else:
self.out_convm = nn.Conv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME', dtype=conv_dtype)
else:
self.upscale0 = Upscale(in_ch, d_ch*8, kernel_size=3)
self.upscale1 = Upscale(d_ch*8, d_ch*8, kernel_size=3)
self.upscale2 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
self.upscale3 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
self.res0 = ResidualBlock(d_ch*8, kernel_size=3)
self.res1 = ResidualBlock(d_ch*8, kernel_size=3)
self.res2 = ResidualBlock(d_ch*4, kernel_size=3)
self.res3 = ResidualBlock(d_ch*2, kernel_size=3)
self.upscalem0 = Upscale(in_ch, d_mask_ch*8, kernel_size=3)
self.upscalem1 = Upscale(d_mask_ch*8, d_mask_ch*8, kernel_size=3)
self.upscalem2 = Upscale(d_mask_ch*8, d_mask_ch*4, kernel_size=3)
self.upscalem3 = Upscale(d_mask_ch*4, d_mask_ch*2, kernel_size=3)
self.out_conv = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME', dtype=conv_dtype)
if 'd' in opts:
self.out_conv1 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
self.out_conv2 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
self.out_conv3 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
self.upscalem4 = Upscale(d_mask_ch*2, d_mask_ch*1, kernel_size=3)
self.out_convm = nn.Conv2D( d_mask_ch*1, 1, kernel_size=1, padding='SAME', dtype=conv_dtype)
else:
self.out_convm = nn.Conv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME', dtype=conv_dtype)
def forward(self, z):
x = self.upscale0(z)
x = self.res0(x)
x = self.upscale1(x)
x = self.res1(x)
x = self.upscale2(x)
x = self.res2(x)
if 't' in opts:
x = self.upscale3(x)
x = self.res3(x)
if 'd' in opts:
x = tf.nn.sigmoid( nn.depth_to_space(tf.concat( (self.out_conv(x),
self.out_conv1(x),
self.out_conv2(x),
self.out_conv3(x)), nn.conv2d_ch_axis), 2) )
else:
x = tf.nn.sigmoid(self.out_conv(x))
m = self.upscalem0(z)
m = self.upscalem1(m)
m = self.upscalem2(m)
if 't' in opts:
m = self.upscalem3(m)
if 'd' in opts:
m = self.upscalem4(m)
else:
if 'd' in opts:
m = self.upscalem3(m)
m = tf.nn.sigmoid(self.out_convm(m))
if use_fp16:
x = tf.cast(x, tf.float32)
m = tf.cast(m, tf.float32)
return x, m
self.Encoder = Encoder
self.Inter = Inter
self.Decoder = Decoder
nn.DeepFakeArchi = DeepFakeArchi
