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Update DeepFakeArchi.py

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TalosOfCrete 2020-05-21 23:09:41 -05:00
commit 0a1e33b6fc
1 changed files with 41 additions and 18 deletions
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core/leras/archis/DeepFakeArchi.py
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@ -352,14 +352,20 @@ class DeepFakeArchi(nn.ArchiBase):
depth_multiplier=self.depth_multiplier,
strides=1 if self.subpixel else 2,
padding='SAME', dilations=self.dilations)
#self.frn1 = nn.FRNorm2D(self.out_ch//(4 if self.subpixel else 1))
#self.tlu1 = nn.TLU(self.out_ch//(4 if self.subpixel else 1))
self.frn1 = nn.FRNorm2D(self.out_ch//(4 if self.subpixel else 1))
self.tlu1 = nn.TLU(self.out_ch//(4 if self.subpixel else 1))
def forward(self, x):
x = self.conv1(x)
#x = self.frn1(x)
#x = self.tlu1(x)
x = self.frn1(x)
x = self.tlu1(x)
if self.subpixel:
if (x.get_shape().as_list()[-2] % 2 != 0): #or (x.get_shape().as_list()[-1] % 2 != 0):
#padding = self.kernel_size//2
if nn.data_format == "NHWC":
x = tf.pad(x, [ [0,0], [1,0], [1,0], [0,0] ])
else:
x = tf.pad(x, [ [0,0], [0,0], [1,0], [1,0] ])
x = nn.space_to_depth(x, 2)
if self.use_activator:
x = tf.nn.leaky_relu(x, 0.1)
@ -386,7 +392,7 @@ class DeepFakeArchi(nn.ArchiBase):
return x
class DecayingDownscaleBlock(nn.ModelBase):
def on_build(self, in_ch, ch, n_downscales=4, init_kernel_size=5, kernel_floor=3, dilations=1, subpixel=True, use_activator=True):
def on_build(self, in_ch, ch, n_downscales=4, init_kernel_size=5, kernel_floor=3, alternating_dilations=True, dilations=1, subpixel=True, use_activator=True):
self.downs = []
@ -400,56 +406,73 @@ class DeepFakeArchi(nn.ArchiBase):
if not init_kernel_size > kernel_floor:
raise ValueError("The initial kernel size must be larger than the kernel floor.")
d = 0
cur_kernel_size = init_kernel_size
last_ch = in_ch
for i in range(n_downscales):
cur_ch = ch*( min(2**i, 8) )
if (d % 2 == 0) and alternating_dilations:
dil = True
d += 1
else:
dil = False
if dil:
dilations=dilations
else:
dilations=1
self.downs.append ( Downscale(last_ch, cur_ch, kernel_size=cur_kernel_size, dilations=dilations, subpixel=subpixel, use_activator=use_activator) )
last_ch = self.downs[-1].get_out_ch()
if cur_kernel_size != kernel_floor and cur_kernel_size-2 != 1:
cur_kernel_size -= 2
self.bp1 = nn.BlurPool()
def forward(self, inp):
x = inp
for down in self.downs:
x = down(x)
x = self.bp1(x)
return x
class Upscale(nn.ModelBase):
def on_build(self, in_ch, out_ch, kernel_size=3, depth_multiplier=1 ):
self.conv1 = nn.SeparableConv2D( in_ch, out_ch*4, kernel_size=kernel_size, depth_multiplier=depth_multiplier, padding='SAME')
#self.frn1 = nn.FRNorm2D(out_ch*4)
#self.tlu1 = nn.TLU(out_ch*4)
self.frn1 = nn.FRNorm2D(out_ch*4)
self.tlu1 = nn.TLU(out_ch*4)
def forward(self, x):
x = self.conv1(x)
#x = self.frn1(x)
#x = self.tlu1(x)
x = tf.nn.leaky_relu(x, 0.1)# (TLU replaces relu)
x = self.frn1(x)
x = self.tlu1(x)
#x = tf.nn.leaky_relu(x, 0.1) (TLU replaces relu)
x = nn.depth_to_space(x, 2)
return x
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, kernel_size=3, depth_multiplier=1 ):
self.conv1 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, depth_multiplier=depth_multiplier, padding='SAME')
#self.frn1 = nn.FRNorm2D(ch)
#self.tlu1 = nn.TLU(ch)
self.frn1 = nn.FRNorm2D(ch)
self.tlu1 = nn.TLU(ch)
self.conv2 = nn.SeparableConv2D( ch, ch, kernel_size=kernel_size, depth_multiplier=depth_multiplier, padding='SAME')
#self.frn2 = nn.FRNorm2D(ch)
#self.tlu2 = nn.TLU(ch)
self.frn2 = nn.FRNorm2D(ch)
self.tlu2 = nn.TLU(ch)
def forward(self, inp):
x = self.conv1(inp)
x = tf.nn.leaky_relu(x, 0.2)
#x = tf.nn.leaky_relu(x, 0.2)
x = self.frn1(x)
x = self.tlu1(x)
x = self.conv2(x)
x = self.frn2(x)
x = tf.nn.leaky_relu(inp + x, 0.2)
#x = self.tlu2(inp + x)
x = self.tlu2(inp + x)
return x
"""
class UpdownResidualBlock(nn.ModelBase):
@ -472,7 +495,7 @@ class DeepFakeArchi(nn.ArchiBase):
"""
class Encoder(nn.ModelBase):
def on_build(self, in_ch, e_ch, **kwargs):
self.down1 = DownscaleBlock(in_ch, e_ch, kernel_size=5, n_downscales=4, dilations=1)
self.down1 = DecayingDownscaleBlock(in_ch, e_ch, init_kernel_size=7, n_downscales=8, dilations=2, use_activator=False)
#self.down2 = DecayingDownscaleBlock(in_ch, e_ch//2, n_downscales=6, dilations=2, use_activator=False)