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saehd - moved to gan 2 only
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seranus
parent
dc7512dea1
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1efbf98cfa
2 changed files with 11 additions and 153 deletions
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@ -111,7 +111,7 @@ class UNetPatchDiscriminator(nn.ModelBase):
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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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sum_st += st
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rf = self.calc_receptive_field_size(layers)
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@ -150,7 +150,7 @@ class UNetPatchDiscriminator(nn.ModelBase):
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self.convs = []
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self.upconvs = []
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layers = self.find_archi(patch_size)
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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', dtype=conv_dtype)
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@ -158,18 +158,7 @@ class UNetPatchDiscriminator(nn.ModelBase):
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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', dtype=conv_dtype) )
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self.res1.append ( ResidualBlock(level_chs[i]) )
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self.res2.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.upres1.insert (0, ResidualBlock(level_chs[i-1]*2) )
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self.upres2.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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# Used in iperovs version, iperov doesn't use above for block
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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', dtype=conv_dtype) )
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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', dtype=conv_dtype) )
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self.out_conv = nn.Conv2D( level_chs[-1]*2, 1, kernel_size=1, padding='VALID', dtype=conv_dtype)
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@ -187,9 +176,7 @@ class UNetPatchDiscriminator(nn.ModelBase):
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for conv in self.convs:
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encs.insert(0, x)
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x = tf.nn.leaky_relu( conv(x), 0.2 )
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x = res1(x)
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x = res2(x)
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center_out, x = self.center_out(x), tf.nn.leaky_relu( self.center_conv(x), 0.2 )
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for i, (upconv, enc) in enumerate(zip(self.upconvs, encs)):
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@ -204,118 +191,4 @@ class UNetPatchDiscriminator(nn.ModelBase):
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return center_out, x
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nn.UNetPatchDiscriminator = UNetPatchDiscriminator
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class UNetPatchDiscriminatorV2(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.UNetPatchDiscriminatorV2 = UNetPatchDiscriminatorV2
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nn.UNetPatchDiscriminator = UNetPatchDiscriminator
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@ -150,7 +150,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.options['uniform_yaw'] = io.input_bool ("Uniform yaw distribution of samples", default_uniform_yaw, help_message='Helps to fix blurry side faces due to small amount of them in the faceset.')
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default_gan_version = self.options['gan_version'] = self.load_or_def_option('gan_version', 2)
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default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
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default_gan_patch_size = self.options['gan_patch_size'] = self.load_or_def_option('gan_patch_size', self.options['resolution'] // 8)
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default_gan_dims = self.options['gan_dims'] = self.load_or_def_option('gan_dims', 16)
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@ -174,12 +173,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.options['random_blur'] = io.input_bool("Enable random blur of samples", default_random_blur, help_message="")
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self.options['random_jpeg'] = io.input_bool("Enable random jpeg compression of samples", default_random_jpeg, help_message="")
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self.options['gan_version'] = np.clip (io.input_int("GAN version", default_gan_version, add_info="2 or 3", help_message="Choose GAN version (v2: 7/16/2020, v3: 1/3/2021):"), 2, 3)
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if self.options['gan_version'] == 2:
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self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 10.0", help_message="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"), 0.0, 10.0 )
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else:
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self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 1.0", help_message="Forces the neural network to learn small details of the face. Enable it only when the face is trained enough with lr_dropout(on) and random_warp(off), and don't disable. The higher the value, the higher the chances of artifacts. Typical fine value is 0.1"), 0.0, 1.0 )
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self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 10.0", help_message="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"), 0.0, 10.0 )
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if self.options['gan_power'] != 0.0:
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if self.options['gan_version'] == 3:
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@ -340,12 +334,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if self.is_training:
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if gan_power != 0:
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if self.options['gan_version'] == 2:
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self.D_src = nn.UNetPatchDiscriminatorV2(patch_size=resolution//16, in_ch=input_ch, name="D_src")
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self.model_filename_list += [ [self.D_src, 'D_src_v2.npy'] ]
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else:
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self.D_src = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="D_src")
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self.model_filename_list += [ [self.D_src, 'GAN.npy'] ]
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self.D_src = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="D_src")
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self.model_filename_list += [ [self.D_src, 'GAN.npy'] ]
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# Initialize optimizers
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lr=5e-5
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@ -370,14 +360,9 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.model_filename_list += [ (self.D_code_opt, 'D_code_opt.npy') ]
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if gan_power != 0:
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if self.options['gan_version'] == 2:
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self.D_src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_src_dst_opt')
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self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')#+self.D_src_x2.get_weights()
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self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_v2_opt.npy') ]
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else:
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self.D_src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='GAN_opt')
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self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')#+self.D_src_x2.get_weights()
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self.model_filename_list += [ (self.D_src_dst_opt, 'GAN_opt.npy') ]
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self.D_src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='GAN_opt')
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self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')#+self.D_src_x2.get_weights()
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self.model_filename_list += [ (self.D_src_dst_opt, 'GAN_opt.npy') ]
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if self.is_training:
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# Adjust batch size for multiple GPU
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