mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 13:02:15 -07:00
fix
This commit is contained in:
Colombo
parent
1e003170bd
commit
235315dd70
4 changed files with 646 additions and 16 deletions
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@ -1,3 +1,5 @@
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from .estimate_sharpness import estimate_sharpness
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from .equalize_and_stack_square import equalize_and_stack_square
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from .text import get_text_image, get_draw_text_lines
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@ -31,9 +31,7 @@ goods or services; loss of use, data, or profits; or business interruption) howe
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import numpy as np
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import cv2
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from math import atan2, pi
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from scipy.ndimage import convolve
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from skimage.filters.edges import HSOBEL_WEIGHTS
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from skimage.feature import canny
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def sobel(image):
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# type: (numpy.ndarray) -> numpy.ndarray
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@ -42,10 +40,11 @@ def sobel(image):
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Inspired by the [Octave implementation](https://sourceforge.net/p/octave/image/ci/default/tree/inst/edge.m#l196).
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"""
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from skimage.filters.edges import HSOBEL_WEIGHTS
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h1 = np.array(HSOBEL_WEIGHTS)
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h1 /= np.sum(abs(h1)) # normalize h1
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from scipy.ndimage import convolve
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strength2 = np.square(convolve(image, h1.T))
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# Note: https://sourceforge.net/p/octave/image/ci/default/tree/inst/edge.m#l59
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@ -103,6 +102,7 @@ def compute(image):
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# edge detection using canny and sobel canny edge detection is done to
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# classify the blocks as edge or non-edge blocks and sobel edge
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# detection is done for the purpose of edge width measurement.
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from skimage.feature import canny
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canny_edges = canny(image)
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sobel_edges = sobel(image)
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@ -1,17 +1,17 @@
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from core.leras import nn
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tf = nn.tf
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class DeepFakeArchi(nn.ArchiBase):
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class DeepFakeArchi(nn.ArchiBase):
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"""
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resolution
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mod None - default
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'uhd'
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'quick'
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"""
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def __init__(self, resolution, mod=None):
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def __init__(self, resolution, mod=None):
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super().__init__()
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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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@ -113,7 +113,7 @@ class DeepFakeArchi(nn.ArchiBase):
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else:
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x = nn.flatten(self.down1(inp))
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return x
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lowest_dense_res = resolution // 16
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class Inter(nn.ModelBase):
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@ -134,11 +134,11 @@ class DeepFakeArchi(nn.ArchiBase):
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x = nn.reshape_4D (x, lowest_dense_res, lowest_dense_res, self.ae_out_ch)
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x = self.upscale1(x)
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return x
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@staticmethod
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def get_code_res():
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return lowest_dense_res
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def get_out_ch(self):
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return self.ae_out_ch
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@ -275,7 +275,7 @@ class DeepFakeArchi(nn.ArchiBase):
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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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@ -331,8 +331,636 @@ class DeepFakeArchi(nn.ArchiBase):
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return tf.nn.sigmoid(self.out_conv(x)), \
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tf.nn.sigmoid(self.out_convm(y))
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elif mod == 'm':
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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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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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super().__init__(*kwargs)
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def on_build(self, *args, **kwargs ):
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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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padding='SAME', dilations=self.dilations)
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def forward(self, x):
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x = self.conv1(x)
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if self.subpixel:
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x = nn.space_to_depth(x, 2)
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if self.use_activator:
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x = tf.nn.leaky_relu(x, 0.1)
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return x
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def get_out_ch(self):
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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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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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last_ch = self.downs[-1].get_out_ch()
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def forward(self, inp):
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x = inp
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for down in self.downs:
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x = down(x)
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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.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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x = tf.nn.leaky_relu(x, 0.1)
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x = nn.depth_to_space(x, 2)
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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.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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x = tf.nn.leaky_relu(x, 0.2)
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x = self.conv2(x)
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x = tf.nn.leaky_relu(inp + x, 0.2)
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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 forward(self, inp):
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x = self.up(inp)
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x = upx = self.res(x)
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x = self.down(x)
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x = x + inp
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x = tf.nn.leaky_relu(x, 0.2)
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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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self.is_hd=is_hd
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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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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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def forward(self, inp):
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if self.is_hd:
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x = tf.concat([ nn.flatten(self.down1(inp)),
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nn.flatten(self.down2(inp)),
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nn.flatten(self.down3(inp)),
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nn.flatten(self.down4(inp)) ], -1 )
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else:
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x = nn.flatten(self.down1(inp))
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return x
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lowest_dense_res = resolution // 32
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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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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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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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def forward(self, inp):
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x = self.dense1(inp)
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x = self.dense2(x)
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x = nn.reshape_4D (x, lowest_dense_res, lowest_dense_res, self.ae_out_ch)
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x = self.upscale1(x)
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return x
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@staticmethod
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def get_code_res():
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return lowest_dense_res
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def get_out_ch(self):
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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, d_mask_ch, is_hd ):
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d_ch = d_ch // 4
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self.upscale00 = Upscale(in_ch, d_ch*8, kernel_size=3)
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self.upscale01 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
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self.upscale02 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
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self.res00 = ResidualBlock(d_ch*8, kernel_size=3)
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self.res01 = ResidualBlock(d_ch*4, kernel_size=3)
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self.res02 = ResidualBlock(d_ch*2, kernel_size=3)
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self.out_conv0 = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME')
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self.upscale10 = Upscale(in_ch, d_ch*8, kernel_size=3)
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self.upscale11 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
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self.upscale12 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
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self.res10 = ResidualBlock(d_ch*8, kernel_size=3)
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self.res11 = ResidualBlock(d_ch*4, kernel_size=3)
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self.res12 = ResidualBlock(d_ch*2, kernel_size=3)
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self.out_conv1 = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME')
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self.upscale20 = Upscale(in_ch, d_ch*8, kernel_size=3)
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self.upscale21 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
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self.upscale22 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
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self.res20 = ResidualBlock(d_ch*8, kernel_size=3)
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self.res21 = ResidualBlock(d_ch*4, kernel_size=3)
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self.res22 = ResidualBlock(d_ch*2, kernel_size=3)
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self.out_conv2 = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME')
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self.upscale30 = Upscale(in_ch, d_ch*8, kernel_size=3)
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self.upscale31 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
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self.upscale32 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
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self.res30 = ResidualBlock(d_ch*8, kernel_size=3)
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self.res31 = ResidualBlock(d_ch*4, kernel_size=3)
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self.res32 = ResidualBlock(d_ch*2, kernel_size=3)
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self.out_conv3 = 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.upscalem3 = Upscale(d_mask_ch*2, d_mask_ch*1, kernel_size=3)
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self.out_convm = nn.Conv2D( d_mask_ch*1, 1, kernel_size=1, padding='SAME')
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def forward(self, inp):
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z = inp
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x0 = self.upscale00(z)
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x0 = self.res00(x0)
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x0 = self.upscale01(x0)
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x0 = self.res01(x0)
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x0 = self.upscale02(x0)
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x0 = self.res02(x0)
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x0 = tf.nn.sigmoid(self.out_conv0(x0))
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x0 = nn.upsample2d(x0)
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x1 = self.upscale10(z)
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x1 = self.res10(x1)
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x1 = self.upscale11(x1)
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x1 = self.res11(x1)
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x1 = self.upscale12(x1)
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x1 = self.res12(x1)
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x1 = tf.nn.sigmoid(self.out_conv1(x1))
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x1 = nn.upsample2d(x1)
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x2 = self.upscale20(z)
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x2 = self.res20(x2)
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x2 = self.upscale21(x2)
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x2 = self.res21(x2)
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x2 = self.upscale22(x2)
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x2 = self.res22(x2)
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x2 = tf.nn.sigmoid(self.out_conv2(x2))
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x2 = nn.upsample2d(x2)
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x3 = self.upscale30(z)
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x3 = self.res30(x3)
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x3 = self.upscale31(x3)
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x3 = self.res31(x3)
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x3 = self.upscale32(x3)
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x3 = self.res32(x3)
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x3 = tf.nn.sigmoid(self.out_conv3(x3))
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x3 = nn.upsample2d(x3)
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if nn.data_format == "NHWC":
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tile_cfg = ( 1, resolution // 2, resolution //2, 1)
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else:
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tile_cfg = ( 1, 1, resolution // 2, resolution //2 )
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z0 = tf.concat ( ( tf.concat ( ( tf.ones ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
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tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
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z0 = tf.tile ( z0, tile_cfg )
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z1 = tf.concat ( ( tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.ones ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
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tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
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z1 = tf.tile ( z1, tile_cfg )
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z2 = tf.concat ( ( tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
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tf.concat ( ( tf.ones ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
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z2 = tf.tile ( z2, tile_cfg )
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z3 = tf.concat ( ( tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
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tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.ones ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
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z3 = tf.tile ( z3, tile_cfg )
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x = x0*z0 + x1*z1 + x2*z2 + x3*z3
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m = self.upscalem0(z)
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m = self.upscalem1(m)
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m = self.upscalem2(m)
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m = self.upscalem3(m)
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return x, \
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tf.nn.sigmoid(self.out_convm(m))
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elif mod == 'uhd':
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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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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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super().__init__(*kwargs)
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def on_build(self, *args, **kwargs ):
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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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padding='SAME', dilations=self.dilations)
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def forward(self, x):
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x = self.conv1(x)
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if self.subpixel:
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x = nn.space_to_depth(x, 2)
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if self.use_activator:
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x = tf.nn.leaky_relu(x, 0.1)
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return x
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def get_out_ch(self):
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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):
|
||||
def on_build(self, in_ch, ch, n_downscales, kernel_size, dilations=1, subpixel=True):
|
||||
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) )
|
||||
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')
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = tf.nn.leaky_relu(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')
|
||||
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
|
||||
|
||||
def forward(self, inp):
|
||||
x = self.conv1(inp)
|
||||
x = tf.nn.leaky_relu(x, 0.2)
|
||||
x = self.conv2(x)
|
||||
x = tf.nn.leaky_relu(inp + x, 0.2)
|
||||
return x
|
||||
|
||||
class Encoder(nn.ModelBase):
|
||||
def on_build(self, in_ch, e_ch, **kwargs):
|
||||
self.c000 = nn.Conv2D( in_ch, e_ch*1, kernel_size=5, strides=2, padding='SAME')
|
||||
|
||||
self.c010 = nn.Conv2D( in_ch, e_ch*1, kernel_size=5, strides=2, padding='SAME')
|
||||
self.c011 = nn.Conv2D( e_ch*1, e_ch*1, kernel_size=5, padding='SAME')
|
||||
|
||||
self.c020 = nn.Conv2D( in_ch, e_ch*1, kernel_size=5, padding='SAME')
|
||||
self.c021 = nn.Conv2D( e_ch*1, e_ch*1, kernel_size=5, strides=2, padding='SAME')
|
||||
self.c022 = nn.Conv2D( e_ch*1, e_ch*1, kernel_size=5, padding='SAME')
|
||||
|
||||
|
||||
self.c100 = nn.Conv2D( e_ch*3, e_ch*1, kernel_size=5, strides=2, padding='SAME')
|
||||
|
||||
self.c110 = nn.Conv2D( e_ch*3, e_ch*1, kernel_size=5, strides=2, padding='SAME')
|
||||
self.c111 = nn.Conv2D( e_ch*1, e_ch*2, kernel_size=5, padding='SAME')
|
||||
|
||||
self.c120 = nn.Conv2D( e_ch*3, e_ch*1, kernel_size=5, padding='SAME')
|
||||
self.c121 = nn.Conv2D( e_ch*1, e_ch*2, kernel_size=5, strides=2, padding='SAME')
|
||||
self.c122 = nn.Conv2D( e_ch*2, e_ch*3, kernel_size=5, padding='SAME')
|
||||
|
||||
|
||||
|
||||
self.c200 = nn.Conv2D( e_ch*6, e_ch*2, kernel_size=5, strides=2, padding='SAME')
|
||||
|
||||
self.c210 = nn.Conv2D( e_ch*6, e_ch*1, kernel_size=5, strides=2, padding='SAME')
|
||||
self.c211 = nn.Conv2D( e_ch*1, e_ch*2, kernel_size=5, padding='SAME')
|
||||
|
||||
self.c220 = nn.Conv2D( e_ch*6, e_ch*2, kernel_size=5, padding='SAME')
|
||||
self.c221 = nn.Conv2D( e_ch*2, e_ch*3, kernel_size=5, strides=2, padding='SAME')
|
||||
self.c222 = nn.Conv2D( e_ch*3, e_ch*4, kernel_size=5, padding='SAME')
|
||||
|
||||
|
||||
self.c300 = nn.Conv2D( e_ch*8, e_ch*2, kernel_size=5, strides=2, padding='SAME')
|
||||
|
||||
self.c310 = nn.Conv2D( e_ch*8, e_ch*1, kernel_size=5, strides=2, padding='SAME')
|
||||
self.c311 = nn.Conv2D( e_ch*1, e_ch*2, kernel_size=5, padding='SAME')
|
||||
|
||||
self.c320 = nn.Conv2D( e_ch*8, e_ch*2, kernel_size=5, padding='SAME')
|
||||
self.c321 = nn.Conv2D( e_ch*2, e_ch*3, kernel_size=5, strides=2, padding='SAME')
|
||||
self.c322 = nn.Conv2D( e_ch*3, e_ch*4, kernel_size=5, padding='SAME')
|
||||
|
||||
def forward(self, inp):
|
||||
x = inp
|
||||
|
||||
x0 = self.c000(x)
|
||||
x0 = tf.nn.leaky_relu(x0, 0.1)
|
||||
|
||||
x1 = self.c010(x)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
x1 = self.c011(x1)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
|
||||
x2 = self.c020(x)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c021(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c022(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
|
||||
x = tf.concat ([x0,x1,x2], nn.conv2d_ch_axis)
|
||||
|
||||
x0 = self.c100(x)
|
||||
x0 = tf.nn.leaky_relu(x0, 0.1)
|
||||
|
||||
x1 = self.c110(x)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
x1 = self.c111(x1)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
|
||||
x2 = self.c120(x)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c121(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c122(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
|
||||
x = tf.concat ([x0,x1,x2], nn.conv2d_ch_axis)
|
||||
|
||||
x0 = self.c200(x)
|
||||
x0 = tf.nn.leaky_relu(x0, 0.1)
|
||||
|
||||
x1 = self.c210(x)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
x1 = self.c211(x1)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
|
||||
x2 = self.c220(x)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c221(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c222(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
|
||||
x = tf.concat ([x0,x1,x2], nn.conv2d_ch_axis)
|
||||
|
||||
x0 = self.c300(x)
|
||||
x0 = tf.nn.leaky_relu(x0, 0.1)
|
||||
|
||||
x1 = self.c310(x)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
x1 = self.c311(x1)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
|
||||
x2 = self.c320(x)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c321(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c322(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
|
||||
x = tf.concat ([x0,x1,x2], nn.conv2d_ch_axis)
|
||||
|
||||
x = nn.flatten(x)
|
||||
return x
|
||||
|
||||
lowest_dense_res = resolution // 32
|
||||
|
||||
class Inter(nn.ModelBase):
|
||||
def on_build(self, in_ch, ae_ch, ae_out_ch, **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)
|
||||
|
||||
def forward(self, inp):
|
||||
x = self.dense_norm(inp)
|
||||
x = self.dense1(x)
|
||||
x = self.dense2(x)
|
||||
x = nn.reshape_4D (x, lowest_dense_res, lowest_dense_res, self.ae_out_ch)
|
||||
x = self.upscale1(x)
|
||||
return x
|
||||
|
||||
@staticmethod
|
||||
def get_code_res():
|
||||
return lowest_dense_res
|
||||
|
||||
def get_out_ch(self):
|
||||
return self.ae_out_ch
|
||||
|
||||
class BaseDecoder(nn.ModelBase):
|
||||
def on_build(self, in_ch, d_ch, **kwargs ):
|
||||
|
||||
self.c000 = nn.Conv2D( in_ch, d_ch*2 *4, kernel_size=3, padding='SAME')
|
||||
|
||||
self.c010 = nn.Conv2D( in_ch, d_ch*2 , kernel_size=3, padding='SAME')
|
||||
self.c011 = nn.Conv2D( d_ch*2, d_ch*2 *4, kernel_size=3, padding='SAME')
|
||||
|
||||
self.c020 = nn.Conv2D( in_ch, d_ch*2 , kernel_size=3, padding='SAME')
|
||||
self.c021 = nn.Conv2D( d_ch*2, d_ch*2 , kernel_size=3, padding='SAME')
|
||||
self.c022 = nn.Conv2D( d_ch*2, d_ch*2 *4, kernel_size=3, padding='SAME')
|
||||
self.res0 = ResidualBlock(d_ch*6, kernel_size=3)
|
||||
|
||||
|
||||
self.c100 = nn.Conv2D( d_ch*6, d_ch*2 *4, kernel_size=3, padding='SAME')
|
||||
|
||||
self.c110 = nn.Conv2D( d_ch*6, d_ch*2 , kernel_size=3, padding='SAME')
|
||||
self.c111 = nn.Conv2D( d_ch*2, d_ch*2 *4, kernel_size=3, padding='SAME')
|
||||
|
||||
self.c120 = nn.Conv2D( d_ch*6, d_ch*2 , kernel_size=3, padding='SAME')
|
||||
self.c121 = nn.Conv2D( d_ch*2, d_ch*2 , kernel_size=3, padding='SAME')
|
||||
self.c122 = nn.Conv2D( d_ch*2, d_ch*2 *4, kernel_size=3, padding='SAME')
|
||||
self.res1 = ResidualBlock(d_ch*6, kernel_size=3)
|
||||
|
||||
|
||||
self.c200 = nn.Conv2D( d_ch*6, d_ch*1 *4, kernel_size=3, padding='SAME')
|
||||
|
||||
self.c210 = nn.Conv2D( d_ch*6, d_ch*1 , kernel_size=3, padding='SAME')
|
||||
self.c211 = nn.Conv2D( d_ch*1, d_ch*1 *4, kernel_size=3, padding='SAME')
|
||||
|
||||
self.c220 = nn.Conv2D( d_ch*6, d_ch*1 , kernel_size=3, padding='SAME')
|
||||
self.c221 = nn.Conv2D( d_ch*1, d_ch*1 , kernel_size=3, padding='SAME')
|
||||
self.c222 = nn.Conv2D( d_ch*1, d_ch*1 *4, kernel_size=3, padding='SAME')
|
||||
self.res2 = ResidualBlock(d_ch*3, kernel_size=3)
|
||||
self.out_conv = nn.Conv2D( d_ch*3, 3, kernel_size=1, padding='SAME')
|
||||
|
||||
|
||||
def forward(self, inp):
|
||||
x = inp
|
||||
|
||||
x0 = self.c000(x)
|
||||
x0 = tf.nn.leaky_relu(x0, 0.1)
|
||||
x0 = nn.depth_to_space(x0, 2)
|
||||
|
||||
x1 = self.c010(x)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
x1 = self.c011(x1)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
x1 = nn.depth_to_space(x1, 2)
|
||||
|
||||
x2 = self.c020(x)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c021(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c022(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = nn.depth_to_space(x2, 2)
|
||||
|
||||
x = tf.concat ([x0,x1,x2], nn.conv2d_ch_axis)
|
||||
x = self.res0(x)
|
||||
|
||||
x0 = self.c100(x)
|
||||
x0 = tf.nn.leaky_relu(x0, 0.1)
|
||||
x0 = nn.depth_to_space(x0, 2)
|
||||
|
||||
x1 = self.c110(x)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
x1 = self.c111(x1)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
x1 = nn.depth_to_space(x1, 2)
|
||||
|
||||
x2 = self.c120(x)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c121(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c122(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = nn.depth_to_space(x2, 2)
|
||||
|
||||
x = tf.concat ([x0,x1,x2], nn.conv2d_ch_axis)
|
||||
x = self.res1(x)
|
||||
|
||||
|
||||
x0 = self.c200(x)
|
||||
x0 = tf.nn.leaky_relu(x0, 0.1)
|
||||
x0 = nn.depth_to_space(x0, 2)
|
||||
|
||||
x1 = self.c210(x)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
x1 = self.c211(x1)
|
||||
x1 = tf.nn.leaky_relu(x1, 0.1)
|
||||
x1 = nn.depth_to_space(x1, 2)
|
||||
|
||||
x2 = self.c220(x)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c221(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = self.c222(x2)
|
||||
x2 = tf.nn.leaky_relu(x2, 0.1)
|
||||
x2 = nn.depth_to_space(x2, 2)
|
||||
|
||||
x = tf.concat ([x0,x1,x2], nn.conv2d_ch_axis)
|
||||
x = self.res2(x)
|
||||
x = tf.nn.sigmoid(self.out_conv(x))
|
||||
|
||||
return x
|
||||
|
||||
class Decoder(nn.ModelBase):
|
||||
def on_build(self, in_ch, d_ch, d_mask_ch, **kwargs ):
|
||||
|
||||
self.dec0 = BaseDecoder (in_ch, d_ch)
|
||||
self.dec1 = BaseDecoder (in_ch, d_ch)
|
||||
self.dec2 = BaseDecoder (in_ch, d_ch)
|
||||
self.dec3 = BaseDecoder (in_ch, d_ch)
|
||||
|
||||
|
||||
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.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')
|
||||
|
||||
def forward(self, inp):
|
||||
z = inp
|
||||
|
||||
x0 = self.dec0(inp)
|
||||
x0 = nn.upsample2d(x0)
|
||||
x1 = self.dec1(inp)
|
||||
x1 = nn.upsample2d(x1)
|
||||
x2 = self.dec2(inp)
|
||||
x2 = nn.upsample2d(x2)
|
||||
x3 = self.dec3(inp)
|
||||
x3 = nn.upsample2d(x3)
|
||||
|
||||
if nn.data_format == "NHWC":
|
||||
tile_cfg = ( 1, resolution // 2, resolution //2, 1)
|
||||
else:
|
||||
tile_cfg = ( 1, 1, resolution // 2, resolution //2 )
|
||||
|
||||
z0 = tf.concat ( ( tf.concat ( ( tf.ones ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
|
||||
tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
|
||||
|
||||
z0 = tf.tile ( z0, tile_cfg )
|
||||
|
||||
z1 = tf.concat ( ( tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.ones ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
|
||||
tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
|
||||
z1 = tf.tile ( z1, tile_cfg )
|
||||
|
||||
z2 = tf.concat ( ( tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
|
||||
tf.concat ( ( tf.ones ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
|
||||
z2 = tf.tile ( z2, tile_cfg )
|
||||
|
||||
z3 = tf.concat ( ( tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.zeros ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ),
|
||||
tf.concat ( ( tf.zeros ( (1,1,1,1) ), tf.ones ( (1,1,1,1) ) ), axis=nn.conv2d_spatial_axes[1] ) ), axis=nn.conv2d_spatial_axes[0] )
|
||||
z3 = tf.tile ( z3, tile_cfg )
|
||||
|
||||
x = x0*z0 + x1*z1 + x2*z2 + x3*z3
|
||||
|
||||
|
||||
m = self.upscalem0(z)
|
||||
m = self.upscalem1(m)
|
||||
m = self.upscalem2(m)
|
||||
m = self.upscalem3(m)
|
||||
|
||||
return x, \
|
||||
tf.nn.sigmoid(self.out_convm(m))
|
||||
"""
|
||||
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 ):
|
||||
self.in_ch = in_ch
|
||||
|
|
@ -415,7 +1043,7 @@ class DeepFakeArchi(nn.ArchiBase):
|
|||
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 )
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||||
self.upscale1 = Upscale(ae_out_ch, ae_out_ch)
|
||||
self.upscale1 = Upscale(ae_out_ch, ae_out_ch)
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||||
|
||||
def forward(self, inp):
|
||||
x = self.dense_norm(inp)
|
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|
|
@ -466,7 +1094,7 @@ class DeepFakeArchi(nn.ArchiBase):
|
|||
|
||||
return tf.nn.sigmoid(self.out_conv(x)), \
|
||||
tf.nn.sigmoid(self.out_convm(m))
|
||||
|
||||
"""
|
||||
self.Encoder = Encoder
|
||||
self.Inter = Inter
|
||||
self.Decoder = Decoder
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ from numpy import linalg as npla
|
|||
|
||||
from core import imagelib, mathlib, pathex
|
||||
from core.cv2ex import *
|
||||
from core.imagelib.estimate_sharpness import estimate_sharpness
|
||||
from core.imagelib import estimate_sharpness
|
||||
from core.interact import interact as io
|
||||
from core.joblib import Subprocessor
|
||||
from core.leras import nn
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue