mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-11 23:57:03 -07:00
e8b04053e4
Changed help for “Learning rate dropout” option: When the face is trained enough, you can enable this option to get extra sharpness and reduce subpixel shake for less amount of iterations. Enabled it before “disable random warp” and before GAN. n disabled. y enabled cpu enabled on CPU. This allows not to use extra VRAM, sacrificing 20% time of iteration. Changed help for GAN option: Train the network in Generative Adversarial manner. Forces the neural network to learn small details of the face. Enable it only when the face is trained enough and don't disable. Typical value is 0.1 improved GAN. Now it produces better skin detail, less patterned aggressive artifacts, works faster. https://i.imgur.com/Nbh3mw1.png
191 lines
7.1 KiB
Python
191 lines
7.1 KiB
Python
import numpy as np
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from core.leras import nn
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tf = nn.tf
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patch_discriminator_kernels = \
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{ 1 : (512, [ [1,1] ]),
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2 : (512, [ [2,1] ]),
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3 : (512, [ [2,1], [2,1] ]),
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4 : (512, [ [2,2], [2,2] ]),
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5 : (512, [ [3,2], [2,2] ]),
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6 : (512, [ [4,2], [2,2] ]),
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7 : (512, [ [3,2], [3,2] ]),
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8 : (512, [ [4,2], [3,2] ]),
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9 : (512, [ [3,2], [4,2] ]),
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10 : (512, [ [4,2], [4,2] ]),
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11 : (512, [ [3,2], [3,2], [2,1] ]),
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12 : (512, [ [4,2], [3,2], [2,1] ]),
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13 : (512, [ [3,2], [4,2], [2,1] ]),
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14 : (512, [ [4,2], [4,2], [2,1] ]),
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15 : (512, [ [3,2], [3,2], [3,1] ]),
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16 : (512, [ [4,2], [3,2], [3,1] ]),
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17 : (512, [ [3,2], [4,2], [3,1] ]),
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18 : (512, [ [4,2], [4,2], [3,1] ]),
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19 : (512, [ [3,2], [3,2], [4,1] ]),
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20 : (512, [ [4,2], [3,2], [4,1] ]),
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21 : (512, [ [3,2], [4,2], [4,1] ]),
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22 : (512, [ [4,2], [4,2], [4,1] ]),
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23 : (256, [ [3,2], [3,2], [3,2], [2,1] ]),
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24 : (256, [ [4,2], [3,2], [3,2], [2,1] ]),
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25 : (256, [ [3,2], [4,2], [3,2], [2,1] ]),
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26 : (256, [ [4,2], [4,2], [3,2], [2,1] ]),
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27 : (256, [ [3,2], [4,2], [4,2], [2,1] ]),
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28 : (256, [ [4,2], [3,2], [4,2], [2,1] ]),
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29 : (256, [ [3,2], [4,2], [4,2], [2,1] ]),
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30 : (256, [ [4,2], [4,2], [4,2], [2,1] ]),
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31 : (256, [ [3,2], [3,2], [3,2], [3,1] ]),
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32 : (256, [ [4,2], [3,2], [3,2], [3,1] ]),
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33 : (256, [ [3,2], [4,2], [3,2], [3,1] ]),
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34 : (256, [ [4,2], [4,2], [3,2], [3,1] ]),
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35 : (256, [ [3,2], [4,2], [4,2], [3,1] ]),
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36 : (256, [ [4,2], [3,2], [4,2], [3,1] ]),
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37 : (256, [ [3,2], [4,2], [4,2], [3,1] ]),
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38 : (256, [ [4,2], [4,2], [4,2], [3,1] ]),
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39 : (256, [ [3,2], [3,2], [3,2], [4,1] ]),
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40 : (256, [ [4,2], [3,2], [3,2], [4,1] ]),
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41 : (256, [ [3,2], [4,2], [3,2], [4,1] ]),
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42 : (256, [ [4,2], [4,2], [3,2], [4,1] ]),
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43 : (256, [ [3,2], [4,2], [4,2], [4,1] ]),
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44 : (256, [ [4,2], [3,2], [4,2], [4,1] ]),
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45 : (256, [ [3,2], [4,2], [4,2], [4,1] ]),
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46 : (256, [ [4,2], [4,2], [4,2], [4,1] ]),
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}
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class PatchDiscriminator(nn.ModelBase):
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def on_build(self, patch_size, in_ch, base_ch=None, conv_kernel_initializer=None):
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suggested_base_ch, kernels_strides = patch_discriminator_kernels[patch_size]
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if base_ch is None:
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base_ch = suggested_base_ch
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prev_ch = in_ch
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self.convs = []
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for i, (kernel_size, strides) in enumerate(kernels_strides):
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cur_ch = base_ch * min( (2**i), 8 )
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self.convs.append ( nn.Conv2D( prev_ch, cur_ch, kernel_size=kernel_size, strides=strides, padding='SAME', kernel_initializer=conv_kernel_initializer) )
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prev_ch = cur_ch
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self.out_conv = nn.Conv2D( prev_ch, 1, kernel_size=1, padding='VALID', kernel_initializer=conv_kernel_initializer)
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def forward(self, x):
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for conv in self.convs:
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x = tf.nn.leaky_relu( conv(x), 0.1 )
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return self.out_conv(x)
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nn.PatchDiscriminator = PatchDiscriminator
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class UNetPatchDiscriminator(nn.ModelBase):
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"""
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Inspired by https://arxiv.org/abs/2002.12655 "A U-Net Based Discriminator for Generative Adversarial Networks"
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"""
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def calc_receptive_field_size(self, layers):
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"""
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result the same as https://fomoro.com/research/article/receptive-field-calculatorindex.html
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"""
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rf = 0
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ts = 1
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for i, (k, s) in enumerate(layers):
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if i == 0:
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rf = k
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else:
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rf += (k-1)*ts
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ts *= s
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return rf
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def find_archi(self, target_patch_size, max_layers=6):
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"""
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Find the best configuration of layers using only 3x3 convs for target patch size
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"""
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s = {}
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for layers_count in range(1,max_layers+1):
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val = 1 << (layers_count-1)
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while True:
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val -= 1
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layers = []
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sum_st = 0
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for i in range(layers_count-1):
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st = 1 + (1 if val & (1 << i) !=0 else 0 )
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layers.append ( [3, st ])
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sum_st += st
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layers.append ( [3, 2])
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sum_st += 2
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rf = self.calc_receptive_field_size(layers)
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s_rf = s.get(rf, None)
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if s_rf is None:
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s[rf] = (layers_count, sum_st, layers)
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else:
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if layers_count < s_rf[0] or \
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( layers_count == s_rf[0] and sum_st > s_rf[1] ):
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s[rf] = (layers_count, sum_st, layers)
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if val == 0:
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break
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x = sorted(list(s.keys()))
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q=x[np.abs(np.array(x)-target_patch_size).argmin()]
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return s[q][2]
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def on_build(self, patch_size, in_ch):
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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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prev_ch = in_ch
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self.convs = []
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self.res = []
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self.upconvs = []
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self.upres = []
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layers = self.find_archi(patch_size)
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base_ch = 16
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level_chs = { i-1:v for i,v in enumerate([ min( base_ch * (2**i), 512 ) for i in range(len(layers)+1)]) }
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self.in_conv = nn.Conv2D( in_ch, level_chs[-1], kernel_size=1, padding='VALID')
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for i, (kernel_size, strides) in enumerate(layers):
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self.convs.append ( nn.Conv2D( level_chs[i-1], level_chs[i], kernel_size=kernel_size, strides=strides, padding='SAME') )
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self.res.append ( ResidualBlock(level_chs[i]) )
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self.upconvs.insert (0, nn.Conv2DTranspose( level_chs[i]*(2 if i != len(layers)-1 else 1), level_chs[i-1], kernel_size=kernel_size, strides=strides, padding='SAME') )
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self.upres.insert (0, ResidualBlock(level_chs[i-1]*2) )
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self.out_conv = nn.Conv2D( level_chs[-1]*2, 1, kernel_size=1, padding='VALID')
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self.center_out = nn.Conv2D( level_chs[len(layers)-1], 1, kernel_size=1, padding='VALID')
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self.center_conv = nn.Conv2D( level_chs[len(layers)-1], level_chs[len(layers)-1], kernel_size=1, padding='VALID')
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def forward(self, x):
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x = tf.nn.leaky_relu( self.in_conv(x), 0.1 )
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encs = []
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for conv, res in zip(self.convs, self.res):
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encs.insert(0, x)
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x = tf.nn.leaky_relu( conv(x), 0.1 )
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x = res(x)
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center_out, x = self.center_out(x), self.center_conv(x)
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for i, (upconv, enc, upres) in enumerate(zip(self.upconvs, encs, self.upres)):
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x = tf.nn.leaky_relu( upconv(x), 0.1 )
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x = tf.concat( [enc, x], axis=nn.conv2d_ch_axis)
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x = upres(x)
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return center_out, self.out_conv(x)
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nn.UNetPatchDiscriminator = UNetPatchDiscriminator
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