mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-08-22 06:23:20 -07:00
Made separable optional
This commit is contained in:
TalosOfCrete
parent
7b5b13bbc7
commit
00a7a2357a
2 changed files with 187 additions and 100 deletions
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@ -14,17 +14,25 @@ class DeepFakeArchi(nn.ArchiBase):
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if mod is None:
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class Downscale(nn.ModelBase):
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def __init__(self, in_ch, out_ch, kernel_size=5, dilations=1, subpixel=True, use_activator=True, *kwargs ):
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def __init__(self, in_ch, out_ch, kernel_size=5, dilations=1, subpixel=True, use_activator=True, separable=False, *kwargs ):
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self.in_ch = in_ch
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self.out_ch = out_ch
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self.kernel_size = kernel_size
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self.dilations = dilations
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self.subpixel = subpixel
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self.use_activator = use_activator
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self.separable = separable
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super().__init__(*kwargs)
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def on_build(self, *args, **kwargs ):
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self.conv1 = nn.SeparableConv2D( self.in_ch,
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if self.separable:
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self.conv1 = nn.SeparableConv2D( self.in_ch,
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self.out_ch // (4 if self.subpixel else 1),
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kernel_size=self.kernel_size,
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strides=1 if self.subpixel else 2,
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padding='SAME', dilations=self.dilations)
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else:
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self.conv1 = nn.Conv2D( self.in_ch,
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self.out_ch // (4 if self.subpixel else 1),
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kernel_size=self.kernel_size,
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strides=1 if self.subpixel else 2,
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@ -42,13 +50,13 @@ class DeepFakeArchi(nn.ArchiBase):
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return (self.out_ch // 4) * 4 if self.subpixel else self.out_ch
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class DownscaleBlock(nn.ModelBase):
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def on_build(self, in_ch, ch, n_downscales, kernel_size, dilations=1, subpixel=True):
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def on_build(self, in_ch, ch, n_downscales, kernel_size, dilations=1, subpixel=True, separable=False):
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self.downs = []
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last_ch = in_ch
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for i in range(n_downscales):
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cur_ch = ch*( min(2**i, 8) )
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self.downs.append ( Downscale(last_ch, cur_ch, kernel_size=kernel_size, dilations=dilations, subpixel=subpixel) )
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self.downs.append ( Downscale(last_ch, cur_ch, kernel_size=kernel_size, dilations=dilations, subpixel=subpixel, separable=separable) )
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last_ch = self.downs[-1].get_out_ch()
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def forward(self, inp):
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@ -58,8 +66,11 @@ class DeepFakeArchi(nn.ArchiBase):
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return x
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class Upscale(nn.ModelBase):
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def on_build(self, in_ch, out_ch, kernel_size=3 ):
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self.conv1 = nn.SeparableConv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME')
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def on_build(self, in_ch, out_ch, kernel_size=3, separable=False ):
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if separable:
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self.conv1 = nn.SeparableConv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME')
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else:
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self.conv1 = nn.Conv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME')
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def forward(self, x):
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x = self.conv1(x)
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@ -68,9 +79,13 @@ class DeepFakeArchi(nn.ArchiBase):
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return x
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class ResidualBlock(nn.ModelBase):
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def on_build(self, ch, kernel_size=3 ):
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self.conv1 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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self.conv2 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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def on_build(self, ch, kernel_size=3, separable=False ):
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if separable:
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self.conv1 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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self.conv2 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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else:
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self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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def forward(self, inp):
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x = self.conv1(inp)
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@ -80,10 +95,10 @@ class DeepFakeArchi(nn.ArchiBase):
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return x
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class UpdownResidualBlock(nn.ModelBase):
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def on_build(self, ch, inner_ch, kernel_size=3 ):
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self.up = Upscale (ch, inner_ch, kernel_size=kernel_size)
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self.res = ResidualBlock (inner_ch, kernel_size=kernel_size)
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self.down = Downscale (inner_ch, ch, kernel_size=kernel_size, use_activator=False)
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def on_build(self, ch, inner_ch, kernel_size=3, separable=False ):
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self.up = Upscale (ch, inner_ch, kernel_size=kernel_size, separable=separable)
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self.res = ResidualBlock (inner_ch, kernel_size=kernel_size, separable=separable)
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self.down = Downscale (inner_ch, ch, kernel_size=kernel_size, use_activator=False, separable=separable)
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def forward(self, inp):
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x = self.up(inp)
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@ -94,15 +109,16 @@ class DeepFakeArchi(nn.ArchiBase):
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return x, upx
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class Encoder(nn.ModelBase):
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def on_build(self, in_ch, e_ch, is_hd):
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def on_build(self, in_ch, e_ch, is_hd, separable=False):
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self.is_hd=is_hd
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self.separable=separable
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if self.is_hd:
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self.down1 = DownscaleBlock(in_ch, e_ch*2, n_downscales=4, kernel_size=3, dilations=1)
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self.down2 = DownscaleBlock(in_ch, e_ch*2, n_downscales=4, kernel_size=5, dilations=1)
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self.down3 = DownscaleBlock(in_ch, e_ch//2, n_downscales=4, kernel_size=5, dilations=2)
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self.down4 = DownscaleBlock(in_ch, e_ch//2, n_downscales=4, kernel_size=7, dilations=2)
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self.down1 = DownscaleBlock(in_ch, e_ch*2, n_downscales=4, kernel_size=3, dilations=1, separable=self.separable)
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self.down2 = DownscaleBlock(in_ch, e_ch*2, n_downscales=4, kernel_size=5, dilations=1, separable=self.separable)
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self.down3 = DownscaleBlock(in_ch, e_ch//2, n_downscales=4, kernel_size=5, dilations=2, separable=self.separable)
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self.down4 = DownscaleBlock(in_ch, e_ch//2, n_downscales=4, kernel_size=7, dilations=2, separable=self.separable)
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else:
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self.down1 = DownscaleBlock(in_ch, e_ch, n_downscales=4, kernel_size=5, dilations=1, subpixel=False)
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self.down1 = DownscaleBlock(in_ch, e_ch, n_downscales=4, kernel_size=5, dilations=1, subpixel=False, separable=self.separable)
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def forward(self, inp):
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if self.is_hd:
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@ -117,16 +133,16 @@ class DeepFakeArchi(nn.ArchiBase):
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lowest_dense_res = resolution // 16
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class Inter(nn.ModelBase):
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def __init__(self, in_ch, ae_ch, ae_out_ch, is_hd=False, **kwargs):
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self.in_ch, self.ae_ch, self.ae_out_ch = in_ch, ae_ch, ae_out_ch
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def __init__(self, in_ch, ae_ch, ae_out_ch, is_hd=False, separable=False **kwargs):
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self.in_ch, self.ae_ch, self.ae_out_ch, self.separable = in_ch, ae_ch, ae_out_ch, separable
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super().__init__(**kwargs)
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def on_build(self):
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in_ch, ae_ch, ae_out_ch = self.in_ch, self.ae_ch, self.ae_out_ch
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in_ch, ae_ch, ae_out_ch, separable = self.in_ch, self.ae_ch, self.ae_out_ch, self.separable
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self.dense1 = nn.Dense( in_ch, ae_ch )
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self.dense2 = nn.Dense( ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch )
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self.upscale1 = Upscale(ae_out_ch, ae_out_ch)
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self.upscale1 = Upscale(ae_out_ch, ae_out_ch, separable)
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def forward(self, inp):
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x = self.dense1(inp)
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@ -146,26 +162,34 @@ class DeepFakeArchi(nn.ArchiBase):
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def on_build(self, in_ch, d_ch, d_mask_ch, is_hd ):
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self.is_hd = is_hd
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self.upscale0 = Upscale(in_ch, d_ch*8, kernel_size=3)
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self.upscale1 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
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self.upscale2 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
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self.upscale0 = Upscale(in_ch, d_ch*8, kernel_size=3, separable=separable)
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self.upscale1 = Upscale(d_ch*8, d_ch*4, kernel_size=3, separable=separable)
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self.upscale2 = Upscale(d_ch*4, d_ch*2, kernel_size=3, separable=separable)
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if is_hd:
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self.res0 = UpdownResidualBlock(in_ch, d_ch*8, kernel_size=3)
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self.res1 = UpdownResidualBlock(d_ch*8, d_ch*4, kernel_size=3)
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self.res2 = UpdownResidualBlock(d_ch*4, d_ch*2, kernel_size=3)
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self.res3 = UpdownResidualBlock(d_ch*2, d_ch, kernel_size=3)
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self.res0 = UpdownResidualBlock(in_ch, d_ch*8, kernel_size=3, separable=separable)
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self.res1 = UpdownResidualBlock(d_ch*8, d_ch*4, kernel_size=3, separable=separable)
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self.res2 = UpdownResidualBlock(d_ch*4, d_ch*2, kernel_size=3, separable=separable)
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self.res3 = UpdownResidualBlock(d_ch*2, d_ch, kernel_size=3, separable=separable)
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else:
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self.res0 = ResidualBlock(d_ch*8, kernel_size=3)
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self.res1 = ResidualBlock(d_ch*4, kernel_size=3)
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self.res2 = ResidualBlock(d_ch*2, kernel_size=3)
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self.res0 = ResidualBlock(d_ch*8, kernel_size=3, separable=separable)
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self.res1 = ResidualBlock(d_ch*4, kernel_size=3, separable=separable)
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self.res2 = ResidualBlock(d_ch*2, kernel_size=3, separable=separable)
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self.out_conv = nn.SeparableConv2D( d_ch*2, 3, kernel_size=1, padding='SAME')
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if separable:
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self.out_conv = nn.SeparableConv2D( d_ch*2, 3, kernel_size=1, padding='SAME')
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else:
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self.out_conv = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME')
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self.upscalem0 = Upscale(in_ch, d_mask_ch*8, kernel_size=3)
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self.upscalem1 = Upscale(d_mask_ch*8, d_mask_ch*4, kernel_size=3)
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self.upscalem2 = Upscale(d_mask_ch*4, d_mask_ch*2, kernel_size=3)
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self.out_convm = nn.SeparableConv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME')
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self.upscalem0 = Upscale(in_ch, d_mask_ch*8, kernel_size=3, separable=separable)
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self.upscalem1 = Upscale(d_mask_ch*8, d_mask_ch*4, kernel_size=3, separable=separable)
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self.upscalem2 = Upscale(d_mask_ch*4, d_mask_ch*2, kernel_size=3, separable=separable)
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if separable:
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self.out_convm = nn.SeparableConv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME')
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else:
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self.out_convm = nn.Conv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME')
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def forward(self, inp):
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z = inp
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@ -200,17 +224,25 @@ class DeepFakeArchi(nn.ArchiBase):
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elif mod == 'quick':
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class Downscale(nn.ModelBase):
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def __init__(self, in_ch, out_ch, kernel_size=5, dilations=1, subpixel=True, use_activator=True, *kwargs ):
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def __init__(self, in_ch, out_ch, kernel_size=5, dilations=1, subpixel=True, use_activator=True, separable=False, *kwargs ):
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self.in_ch = in_ch
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self.out_ch = out_ch
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self.kernel_size = kernel_size
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self.dilations = dilations
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self.subpixel = subpixel
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self.use_activator = use_activator
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self.separable = separable
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super().__init__(*kwargs)
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def on_build(self, *args, **kwargs ):
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self.conv1 = nn.SeparableConv2D( self.in_ch,
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if self.separable:
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self.conv1 = nn.SeparableConv2D( self.in_ch,
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self.out_ch // (4 if self.subpixel else 1),
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kernel_size=self.kernel_size,
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strides=1 if self.subpixel else 2,
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padding='SAME', dilations=self.dilations )
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else:
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self.conv1 = nn.Conv2D( self.in_ch,
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self.out_ch // (4 if self.subpixel else 1),
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kernel_size=self.kernel_size,
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strides=1 if self.subpixel else 2,
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@ -230,13 +262,13 @@ class DeepFakeArchi(nn.ArchiBase):
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return (self.out_ch // 4) * 4 if self.subpixel else self.out_ch
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class DownscaleBlock(nn.ModelBase):
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def on_build(self, in_ch, ch, n_downscales, kernel_size, dilations=1, subpixel=True):
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def on_build(self, in_ch, ch, n_downscales, kernel_size, dilations=1, subpixel=True, separable=False):
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self.downs = []
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last_ch = in_ch
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for i in range(n_downscales):
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cur_ch = ch*( min(2**i, 8) )
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self.downs.append ( Downscale(last_ch, cur_ch, kernel_size=kernel_size, dilations=dilations, subpixel=subpixel) )
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self.downs.append ( Downscale(last_ch, cur_ch, kernel_size=kernel_size, dilations=dilations, subpixel=subpixel, separable=separable) )
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last_ch = self.downs[-1].get_out_ch()
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def forward(self, inp):
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@ -246,8 +278,11 @@ class DeepFakeArchi(nn.ArchiBase):
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return x
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class Upscale(nn.ModelBase):
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def on_build(self, in_ch, out_ch, kernel_size=3 ):
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self.conv1 = nn.SeparableConv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME')
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def on_build(self, in_ch, out_ch, kernel_size=3, separable=False ):
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if separable:
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self.conv1 = nn.SeparableConv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME')
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else:
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self.conv1 = nn.Conv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME')
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def forward(self, x):
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x = self.conv1(x)
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@ -256,9 +291,13 @@ class DeepFakeArchi(nn.ArchiBase):
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return x
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class ResidualBlock(nn.ModelBase):
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def on_build(self, ch, kernel_size=3 ):
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self.conv1 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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self.conv2 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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def on_build(self, ch, kernel_size=3, separable=False ):
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if separable:
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self.conv1 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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self.conv2 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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else:
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self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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def forward(self, inp):
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x = self.conv1(inp)
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@ -269,25 +308,25 @@ class DeepFakeArchi(nn.ArchiBase):
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return x
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class Encoder(nn.ModelBase):
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def on_build(self, in_ch, e_ch):
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self.down1 = DownscaleBlock(in_ch, e_ch, n_downscales=4, kernel_size=5)
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def on_build(self, in_ch, e_ch, separable=False):
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self.down1 = DownscaleBlock(in_ch, e_ch, n_downscales=4, kernel_size=5, separable=separable)
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def forward(self, inp):
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return nn.flatten(self.down1(inp))
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lowest_dense_res = resolution // 16
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class Inter(nn.ModelBase):
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def __init__(self, in_ch, ae_ch, ae_out_ch, d_ch, **kwargs):
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self.in_ch, self.ae_ch, self.ae_out_ch, self.d_ch = in_ch, ae_ch, ae_out_ch, d_ch
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def __init__(self, in_ch, ae_ch, ae_out_ch, d_ch, separable=False**kwargs):
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self.in_ch, self.ae_ch, self.ae_out_ch, self.d_ch, self.separable = in_ch, ae_ch, ae_out_ch, d_ch, separable
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super().__init__(**kwargs)
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def on_build(self):
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in_ch, ae_ch, ae_out_ch, d_ch = self.in_ch, self.ae_ch, self.ae_out_ch, self.d_ch
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in_ch, ae_ch, ae_out_ch, d_ch, separable = self.in_ch, self.ae_ch, self.ae_out_ch, self.d_ch, self.separable
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self.dense1 = nn.Dense( in_ch, ae_ch, kernel_initializer=tf.initializers.orthogonal )
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self.dense2 = nn.Dense( ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch, kernel_initializer=tf.initializers.orthogonal )
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self.upscale1 = Upscale(ae_out_ch, d_ch*8)
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self.res1 = ResidualBlock(d_ch*8)
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self.upscale1 = Upscale(ae_out_ch, d_ch*8, separable=separable)
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self.res1 = ResidualBlock(d_ch*8, separable=separable)
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def forward(self, inp):
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x = self.dense1(inp)
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@ -301,20 +340,25 @@ class DeepFakeArchi(nn.ArchiBase):
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return self.ae_out_ch
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class Decoder(nn.ModelBase):
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def on_build(self, in_ch, d_ch):
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self.upscale1 = Upscale(in_ch, d_ch*4)
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self.res1 = ResidualBlock(d_ch*4)
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self.upscale2 = Upscale(d_ch*4, d_ch*2)
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self.res2 = ResidualBlock(d_ch*2)
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self.upscale3 = Upscale(d_ch*2, d_ch*1)
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self.res3 = ResidualBlock(d_ch*1)
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def on_build(self, in_ch, d_ch, separable=False):
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self.upscale1 = Upscale(in_ch, d_ch*4, separable=separable)
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self.res1 = ResidualBlock(d_ch*4, separable=separable)
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self.upscale2 = Upscale(d_ch*4, d_ch*2, separable=separable)
|
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self.res2 = ResidualBlock(d_ch*2, separable=separable)
|
||||
self.upscale3 = Upscale(d_ch*2, d_ch*1, separable=separable)
|
||||
self.res3 = ResidualBlock(d_ch*1, separable=separable)
|
||||
|
||||
self.upscalem1 = Upscale(in_ch, d_ch)
|
||||
self.upscalem2 = Upscale(d_ch, d_ch//2)
|
||||
self.upscalem3 = Upscale(d_ch//2, d_ch//2)
|
||||
self.upscalem1 = Upscale(in_ch, d_ch, separable=separable)
|
||||
self.upscalem2 = Upscale(d_ch, d_ch//2, separable=separable)
|
||||
self.upscalem3 = Upscale(d_ch//2, d_ch//2, separable=separable)
|
||||
|
||||
self.out_conv = nn.SeparableConv2D( d_ch*1, 3, kernel_size=1, padding='SAME')
|
||||
self.out_convm = nn.SeparableConv2D( d_ch//2, 1, kernel_size=1, padding='SAME')
|
||||
|
||||
if separable:
|
||||
self.out_conv = nn.SeparableConv2D( d_ch*1, 3, kernel_size=1, padding='SAME')
|
||||
self.out_convm = nn.SeparableConv2D( d_ch//2, 1, kernel_size=1, padding='SAME')
|
||||
else:
|
||||
self.out_conv = nn.Conv2D( d_ch*1, 3, kernel_size=1, padding='SAME')
|
||||
self.out_convm = nn.Conv2D( d_ch//2, 1, kernel_size=1, padding='SAME')
|
||||
|
||||
def forward(self, inp):
|
||||
z = inp
|
||||
|
|
@ -334,17 +378,25 @@ class DeepFakeArchi(nn.ArchiBase):
|
|||
elif mod == 'uhd':
|
||||
|
||||
class Downscale(nn.ModelBase):
|
||||
def __init__(self, in_ch, out_ch, kernel_size=5, dilations=1, subpixel=True, use_activator=True, *kwargs ):
|
||||
def __init__(self, in_ch, out_ch, kernel_size=5, dilations=1, subpixel=True, use_activator=True, separable=False, *kwargs ):
|
||||
self.in_ch = in_ch
|
||||
self.out_ch = out_ch
|
||||
self.kernel_size = kernel_size
|
||||
self.dilations = dilations
|
||||
self.subpixel = subpixel
|
||||
self.use_activator = use_activator
|
||||
self.separable = separable
|
||||
super().__init__(*kwargs)
|
||||
|
||||
def on_build(self, *args, **kwargs ):
|
||||
self.conv1 = nn.SeparableConv2D( self.in_ch,
|
||||
if self.separable:
|
||||
self.conv1 = nn.SeparableConv2D( self.in_ch,
|
||||
self.out_ch // (4 if self.subpixel else 1),
|
||||
kernel_size=self.kernel_size,
|
||||
strides=1 if self.subpixel else 2,
|
||||
padding='SAME', dilations=self.dilations)
|
||||
else:
|
||||
self.conv1 = nn.Conv2D( self.in_ch,
|
||||
self.out_ch // (4 if self.subpixel else 1),
|
||||
kernel_size=self.kernel_size,
|
||||
strides=1 if self.subpixel else 2,
|
||||
|
|
@ -362,13 +414,13 @@ class DeepFakeArchi(nn.ArchiBase):
|
|||
return (self.out_ch // 4) * 4 if self.subpixel else self.out_ch
|
||||
|
||||
class DownscaleBlock(nn.ModelBase):
|
||||
def on_build(self, in_ch, ch, n_downscales, kernel_size, dilations=1, subpixel=True):
|
||||
def on_build(self, in_ch, ch, n_downscales, kernel_size, dilations=1, subpixel=True, separable=False):
|
||||
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, dilations=dilations, subpixel=subpixel) )
|
||||
self.downs.append ( Downscale(last_ch, cur_ch, kernel_size=kernel_size, dilations=dilations, subpixel=subpixel, separable=separable) )
|
||||
last_ch = self.downs[-1].get_out_ch()
|
||||
|
||||
def forward(self, inp):
|
||||
|
|
@ -378,8 +430,11 @@ class DeepFakeArchi(nn.ArchiBase):
|
|||
return x
|
||||
|
||||
class Upscale(nn.ModelBase):
|
||||
def on_build(self, in_ch, out_ch, kernel_size=3 ):
|
||||
self.conv1 = nn.SeparableConv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME')
|
||||
def on_build(self, in_ch, out_ch, kernel_size=3, separable=False ):
|
||||
if separable:
|
||||
self.conv1 = nn.SeparableConv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME')
|
||||
else:
|
||||
self.conv1 = nn.Conv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME')
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
|
|
@ -388,9 +443,13 @@ class DeepFakeArchi(nn.ArchiBase):
|
|||
return x
|
||||
|
||||
class ResidualBlock(nn.ModelBase):
|
||||
def on_build(self, ch, kernel_size=3 ):
|
||||
self.conv1 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
|
||||
self.conv2 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
|
||||
def on_build(self, ch, kernel_size=3, separable=False ):
|
||||
if separable:
|
||||
self.conv1 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
|
||||
self.conv2 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
|
||||
else:
|
||||
self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
|
||||
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
|
||||
|
||||
def forward(self, inp):
|
||||
x = self.conv1(inp)
|
||||
|
|
@ -400,8 +459,8 @@ class DeepFakeArchi(nn.ArchiBase):
|
|||
return x
|
||||
|
||||
class Encoder(nn.ModelBase):
|
||||
def on_build(self, in_ch, e_ch, **kwargs):
|
||||
self.down1 = DownscaleBlock(in_ch, e_ch, n_downscales=4, kernel_size=5, dilations=1, subpixel=False)
|
||||
def on_build(self, in_ch, e_ch, separable=False, **kwargs):
|
||||
self.down1 = DownscaleBlock(in_ch, e_ch, n_downscales=4, kernel_size=5, dilations=1, subpixel=False, separable=separable)
|
||||
|
||||
def forward(self, inp):
|
||||
x = nn.flatten(self.down1(inp))
|
||||
|
|
@ -410,12 +469,12 @@ class DeepFakeArchi(nn.ArchiBase):
|
|||
lowest_dense_res = resolution // 16
|
||||
|
||||
class Inter(nn.ModelBase):
|
||||
def on_build(self, in_ch, ae_ch, ae_out_ch, **kwargs):
|
||||
def on_build(self, in_ch, ae_ch, ae_out_ch, separable=False, **kwargs):
|
||||
self.ae_out_ch = ae_out_ch
|
||||
self.dense_norm = nn.DenseNorm()
|
||||
self.dense1 = nn.Dense( in_ch, ae_ch )
|
||||
self.dense2 = nn.Dense( ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch )
|
||||
self.upscale1 = Upscale(ae_out_ch, ae_out_ch)
|
||||
self.upscale1 = Upscale(ae_out_ch, ae_out_ch, separable=separable)
|
||||
|
||||
def forward(self, inp):
|
||||
x = self.dense_norm(inp)
|
||||
|
|
@ -433,22 +492,30 @@ class DeepFakeArchi(nn.ArchiBase):
|
|||
return self.ae_out_ch
|
||||
|
||||
class Decoder(nn.ModelBase):
|
||||
def on_build(self, in_ch, d_ch, d_mask_ch, **kwargs ):
|
||||
def on_build(self, in_ch, d_ch, d_mask_ch, separable=False, **kwargs ):
|
||||
|
||||
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.upscale0 = Upscale(in_ch, d_ch*8, kernel_size=3, separable=separable)
|
||||
self.upscale1 = Upscale(d_ch*8, d_ch*4, kernel_size=3, separable=separable)
|
||||
self.upscale2 = Upscale(d_ch*4, d_ch*2, kernel_size=3, separable=separable)
|
||||
|
||||
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.res0 = ResidualBlock(d_ch*8, kernel_size=3, separable=separable)
|
||||
self.res1 = ResidualBlock(d_ch*4, kernel_size=3, separable=separable)
|
||||
self.res2 = ResidualBlock(d_ch*2, kernel_size=3, separable=separable)
|
||||
|
||||
self.out_conv = nn.SeparableConv2D( d_ch*2, 3, kernel_size=1, padding='SAME')
|
||||
|
||||
if separable:
|
||||
self.out_conv = nn.SeparableConv2D( d_ch*2, 3, kernel_size=1, padding='SAME')
|
||||
else:
|
||||
self.out_conv = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME')
|
||||
|
||||
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_convm = nn.SeparableConv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME')
|
||||
self.upscalem0 = Upscale(in_ch, d_mask_ch*8, kernel_size=3, separable=separable)
|
||||
self.upscalem1 = Upscale(d_mask_ch*8, d_mask_ch*4, kernel_size=3, separable=separable)
|
||||
self.upscalem2 = Upscale(d_mask_ch*4, d_mask_ch*2, kernel_size=3, separable=separable)
|
||||
|
||||
if separable:
|
||||
self.out_convm = nn.SeparableConv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME')
|
||||
else:
|
||||
self.out_convm = nn.Conv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME')
|
||||
|
||||
def forward(self, inp):
|
||||
z = inp
|
||||
|
|
|
|||
|
|
@ -32,6 +32,10 @@ class SAEHDModel(ModelBase):
|
|||
default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'f')
|
||||
default_models_opt_on_gpu = self.options['models_opt_on_gpu'] = self.load_or_def_option('models_opt_on_gpu', True)
|
||||
default_archi = self.options['archi'] = self.load_or_def_option('archi', 'df')
|
||||
default_separable = self.options['separable'] = self.load_or_def_option('separable', False)
|
||||
default_separable_enc = self.options['separable_enc'] = self.load_or_def_option('separable_enc', False)
|
||||
default_separable_inter = self.options['separable_inter'] = self.load_or_def_option('separable_inter', False)
|
||||
default_separable_dec = self.options['separable_dec'] = self.load_or_def_option('separable_dec', False)
|
||||
default_ae_dims = self.options['ae_dims'] = self.load_or_def_option('ae_dims', 256)
|
||||
default_e_dims = self.options['e_dims'] = self.load_or_def_option('e_dims', 64)
|
||||
default_d_dims = self.options['d_dims'] = self.options.get('d_dims', None)
|
||||
|
|
@ -60,8 +64,18 @@ class SAEHDModel(ModelBase):
|
|||
resolution = io.input_int("Resolution", default_resolution, add_info="64-512", help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
|
||||
resolution = np.clip ( (resolution // 16) * 16, 64, 512)
|
||||
self.options['resolution'] = resolution
|
||||
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf','head'], help_message="Half / mid face / full face / whole face / head. Half face has better resolution, but covers less area of cheeks. Mid face is 30% wider than half face. 'Whole face' covers full area of face include forehead. 'head' covers full head, but requires XSeg for src and dst faceset.").lower()
|
||||
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf','head'], help_message="Half / mid face / full face / whole face / head. Half face has better resolution, but covers less area of cheeks. Mid face is 30% wider than half face. 'Whole face' covers full area of face including forehead. 'head' covers full head, but requires XSeg for src and dst faceset.").lower()
|
||||
self.options['archi'] = io.input_str ("AE architecture", default_archi, ['df','liae','dfhd','liaehd','dfuhd','liaeuhd'], help_message="'df' keeps faces more natural.\n'liae' can fix overly different face shapes.\n'hd' are experimental versions.").lower()
|
||||
self.options['separable'] = io.input_bool ("Use depthwise separable convolutions throughout model", default_separable, help_message="Use lighter and more effecient layers in the encoder, bottleneck, and decoder. This speeds up iterations (~15-200%) and reduces memory usage considerably in the encoder and decoder (~60%) - allowing for increased settings or faster training - but it may take more iterations for the model to become good and possibly hurt quality. Set this to false if you wish to only have these layers in certain parts of the model (e.g. only in the encoder) or if you do not want them at all.").lower()
|
||||
|
||||
if not self.options['separable']:
|
||||
self.options['separable_enc'] = io.input_bool ("Use depthwise separable convolutions in the encoder", default_separable_enc, help_message="This is the part of the model second most impacted by using these more efficient layers in terms of better iteration speed, memory savings, possible quality loss, and possible increase in required iterations as it may have anywhere from one to four downscaling operations for UHD and HD architectures respectively.").lower()
|
||||
self.options['separable_inter'] = io.input_bool ("Use depthwise separable convolutions in the bottleneck", default_separable_inter, help_message="This is the part of the model least impacted by using these more efficient layers in terms of better iteration speed, memory savings, possible quality loss, and possible increase in required iterations as it only has a single upscaling operation regardless of architecture.").lower()
|
||||
self.options['separable_dec'] = io.input_bool ("Use depthwise separable convolutions in the decoder", default_separable_dec, help_message="This is the part of the model most impacted by using these more efficient layers in terms of better iteration speed, memory savings, possible quality loss, and possible increase in required iterations as it has by far the most operations that these layers use out of all parts of the model.").lower()
|
||||
else:
|
||||
self.options['separable_enc'] = True
|
||||
self.options['separable_inter'] = True
|
||||
self.options['separable_dec'] = True
|
||||
|
||||
default_d_dims = 48 if self.options['archi'] == 'dfhd' else 64
|
||||
default_d_dims = self.options['d_dims'] = self.load_or_def_option('d_dims', default_d_dims)
|
||||
|
|
@ -133,6 +147,12 @@ class SAEHDModel(ModelBase):
|
|||
eyes_prio = self.options['eyes_prio']
|
||||
archi = self.options['archi']
|
||||
is_hd = 'hd' in archi
|
||||
|
||||
separable_enc = self.options['separable_enc']
|
||||
separable_inter = self.options['separable_inter']
|
||||
separable_dec = self.options['separable_dec']
|
||||
|
||||
|
||||
ae_dims = self.options['ae_dims']
|
||||
e_dims = self.options['e_dims']
|
||||
d_dims = self.options['d_dims']
|
||||
|
|
@ -173,14 +193,14 @@ class SAEHDModel(ModelBase):
|
|||
|
||||
with tf.device (models_opt_device):
|
||||
if 'df' in archi:
|
||||
self.encoder = model_archi.Encoder(in_ch=input_ch, e_ch=e_dims, is_hd=is_hd, name='encoder')
|
||||
self.encoder = model_archi.Encoder(in_ch=input_ch, e_ch=e_dims, is_hd=is_hd, separable=separable_enc, name='encoder')
|
||||
encoder_out_ch = self.encoder.compute_output_channels ( (nn.floatx, bgr_shape))
|
||||
|
||||
self.inter = model_archi.Inter (in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims, is_hd=is_hd, name='inter')
|
||||
self.inter = model_archi.Inter (in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims, is_hd=is_hd, separable=separable_inter, name='inter')
|
||||
inter_out_ch = self.inter.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
|
||||
|
||||
self.decoder_src = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd=is_hd, name='decoder_src')
|
||||
self.decoder_dst = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd=is_hd, name='decoder_dst')
|
||||
self.decoder_src = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd=is_hd, separable=separable_dec, name='decoder_src')
|
||||
self.decoder_dst = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd=is_hd, separable=separable_dec, name='decoder_dst')
|
||||
|
||||
self.model_filename_list += [ [self.encoder, 'encoder.npy' ],
|
||||
[self.inter, 'inter.npy' ],
|
||||
|
|
@ -193,16 +213,16 @@ class SAEHDModel(ModelBase):
|
|||
self.model_filename_list += [ [self.code_discriminator, 'code_discriminator.npy'] ]
|
||||
|
||||
elif 'liae' in archi:
|
||||
self.encoder = model_archi.Encoder(in_ch=input_ch, e_ch=e_dims, is_hd=is_hd, name='encoder')
|
||||
self.encoder = model_archi.Encoder(in_ch=input_ch, e_ch=e_dims, is_hd=is_hd, separable=separable_enc, name='encoder')
|
||||
encoder_out_ch = self.encoder.compute_output_channels ( (nn.floatx, bgr_shape))
|
||||
|
||||
self.inter_AB = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, is_hd=is_hd, name='inter_AB')
|
||||
self.inter_B = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, is_hd=is_hd, name='inter_B')
|
||||
self.inter_AB = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, is_hd=is_hd, separable=separable_inter, name='inter_AB')
|
||||
self.inter_B = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, is_hd=is_hd, separable=separable_inter, name='inter_B')
|
||||
|
||||
inter_AB_out_ch = self.inter_AB.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
|
||||
inter_B_out_ch = self.inter_B.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
|
||||
inters_out_ch = inter_AB_out_ch+inter_B_out_ch
|
||||
self.decoder = model_archi.Decoder(in_ch=inters_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd=is_hd, name='decoder')
|
||||
self.decoder = model_archi.Decoder(in_ch=inters_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd=is_hd, separable=separable_dec, name='decoder')
|
||||
|
||||
self.model_filename_list += [ [self.encoder, 'encoder.npy'],
|
||||
[self.inter_AB, 'inter_AB.npy'],
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue