From 85ede1a7e74a858a19f63141dc2e95e15bd17c6d Mon Sep 17 00:00:00 2001 From: Colombo Date: Sun, 7 Jun 2020 12:26:23 +0400 Subject: [PATCH] . --- core/leras/archis/DeepFakeArchi.py | 648 +---------------------------- 1 file changed, 10 insertions(+), 638 deletions(-) diff --git a/core/leras/archis/DeepFakeArchi.py b/core/leras/archis/DeepFakeArchi.py index ec83e47..8f82aaa 100644 --- a/core/leras/archis/DeepFakeArchi.py +++ b/core/leras/archis/DeepFakeArchi.py @@ -1,17 +1,17 @@ from core.leras import nn tf = nn.tf -class DeepFakeArchi(nn.ArchiBase): +class DeepFakeArchi(nn.ArchiBase): """ resolution - + mod None - default 'uhd' 'quick' """ - def __init__(self, resolution, mod=None): + def __init__(self, resolution, mod=None): super().__init__() - + if mod is None: class Downscale(nn.ModelBase): def __init__(self, in_ch, out_ch, kernel_size=5, dilations=1, subpixel=True, use_activator=True, *kwargs ): @@ -113,7 +113,7 @@ class DeepFakeArchi(nn.ArchiBase): else: x = nn.flatten(self.down1(inp)) return x - + lowest_dense_res = resolution // 16 class Inter(nn.ModelBase): @@ -134,11 +134,11 @@ class DeepFakeArchi(nn.ArchiBase): 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 @@ -275,7 +275,7 @@ class DeepFakeArchi(nn.ArchiBase): return nn.flatten(self.down1(inp)) lowest_dense_res = resolution // 16 - + class Inter(nn.ModelBase): def __init__(self, in_ch, ae_ch, ae_out_ch, d_ch, **kwargs): self.in_ch, self.ae_ch, self.ae_out_ch, self.d_ch = in_ch, ae_ch, ae_out_ch, d_ch @@ -331,636 +331,8 @@ class DeepFakeArchi(nn.ArchiBase): return tf.nn.sigmoid(self.out_conv(x)), \ tf.nn.sigmoid(self.out_convm(y)) - elif mod == 'm': - - 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 - self.out_ch = out_ch - self.kernel_size = kernel_size - self.dilations = dilations - self.subpixel = subpixel - self.use_activator = use_activator - super().__init__(*kwargs) - - def on_build(self, *args, **kwargs ): - 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, - padding='SAME', dilations=self.dilations) - - def forward(self, x): - x = self.conv1(x) - if self.subpixel: - x = nn.space_to_depth(x, 2) - if self.use_activator: - x = tf.nn.leaky_relu(x, 0.1) - return x - - def get_out_ch(self): - 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): - 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 UpdownResidualBlock(nn.ModelBase): - def on_build(self, ch, inner_ch, kernel_size=3 ): - self.up = Upscale (ch, inner_ch, kernel_size=kernel_size) - self.res = ResidualBlock (inner_ch, kernel_size=kernel_size) - self.down = Downscale (inner_ch, ch, kernel_size=kernel_size, use_activator=False) - - def forward(self, inp): - x = self.up(inp) - x = upx = self.res(x) - x = self.down(x) - x = x + inp - x = tf.nn.leaky_relu(x, 0.2) - return x, upx - - class Encoder(nn.ModelBase): - def on_build(self, in_ch, e_ch, is_hd): - self.is_hd=is_hd - if self.is_hd: - self.down1 = DownscaleBlock(in_ch, e_ch*2, n_downscales=4, kernel_size=3, dilations=1) - self.down2 = DownscaleBlock(in_ch, e_ch*2, n_downscales=4, kernel_size=5, dilations=1) - self.down3 = DownscaleBlock(in_ch, e_ch//2, n_downscales=4, kernel_size=5, dilations=2) - self.down4 = DownscaleBlock(in_ch, e_ch//2, n_downscales=4, kernel_size=7, dilations=2) - else: - self.down1 = DownscaleBlock(in_ch, e_ch, n_downscales=4, kernel_size=5, dilations=1, subpixel=False) - - def forward(self, inp): - if self.is_hd: - x = tf.concat([ nn.flatten(self.down1(inp)), - nn.flatten(self.down2(inp)), - nn.flatten(self.down3(inp)), - nn.flatten(self.down4(inp)) ], -1 ) - else: - x = nn.flatten(self.down1(inp)) - return x - - lowest_dense_res = resolution // 32 - - class Inter(nn.ModelBase): - def __init__(self, in_ch, ae_ch, ae_out_ch, is_hd=False, **kwargs): - self.in_ch, self.ae_ch, self.ae_out_ch = in_ch, ae_ch, ae_out_ch - super().__init__(**kwargs) - - def on_build(self): - in_ch, ae_ch, ae_out_ch = self.in_ch, self.ae_ch, self.ae_out_ch - - 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.dense1(inp) - 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 Decoder(nn.ModelBase): - def on_build(self, in_ch, d_ch, d_mask_ch, is_hd ): - d_ch = d_ch // 4 - - self.upscale00 = Upscale(in_ch, d_ch*8, kernel_size=3) - self.upscale01 = Upscale(d_ch*8, d_ch*4, kernel_size=3) - self.upscale02 = Upscale(d_ch*4, d_ch*2, kernel_size=3) - - self.res00 = ResidualBlock(d_ch*8, kernel_size=3) - self.res01 = ResidualBlock(d_ch*4, kernel_size=3) - self.res02 = ResidualBlock(d_ch*2, kernel_size=3) - - self.out_conv0 = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME') - - - self.upscale10 = Upscale(in_ch, d_ch*8, kernel_size=3) - self.upscale11 = Upscale(d_ch*8, d_ch*4, kernel_size=3) - self.upscale12 = Upscale(d_ch*4, d_ch*2, kernel_size=3) - - self.res10 = ResidualBlock(d_ch*8, kernel_size=3) - self.res11 = ResidualBlock(d_ch*4, kernel_size=3) - self.res12 = ResidualBlock(d_ch*2, kernel_size=3) - - self.out_conv1 = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME') - - self.upscale20 = Upscale(in_ch, d_ch*8, kernel_size=3) - self.upscale21 = Upscale(d_ch*8, d_ch*4, kernel_size=3) - self.upscale22 = Upscale(d_ch*4, d_ch*2, kernel_size=3) - - self.res20 = ResidualBlock(d_ch*8, kernel_size=3) - self.res21 = ResidualBlock(d_ch*4, kernel_size=3) - self.res22 = ResidualBlock(d_ch*2, kernel_size=3) - - self.out_conv2 = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME') - - self.upscale30 = Upscale(in_ch, d_ch*8, kernel_size=3) - self.upscale31 = Upscale(d_ch*8, d_ch*4, kernel_size=3) - self.upscale32 = Upscale(d_ch*4, d_ch*2, kernel_size=3) - - self.res30 = ResidualBlock(d_ch*8, kernel_size=3) - self.res31 = ResidualBlock(d_ch*4, kernel_size=3) - self.res32 = ResidualBlock(d_ch*2, kernel_size=3) - - self.out_conv3 = 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.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.upscale00(z) - x0 = self.res00(x0) - x0 = self.upscale01(x0) - x0 = self.res01(x0) - x0 = self.upscale02(x0) - x0 = self.res02(x0) - x0 = tf.nn.sigmoid(self.out_conv0(x0)) - x0 = nn.upsample2d(x0) - - x1 = self.upscale10(z) - x1 = self.res10(x1) - x1 = self.upscale11(x1) - x1 = self.res11(x1) - x1 = self.upscale12(x1) - x1 = self.res12(x1) - x1 = tf.nn.sigmoid(self.out_conv1(x1)) - x1 = nn.upsample2d(x1) - - x2 = self.upscale20(z) - x2 = self.res20(x2) - x2 = self.upscale21(x2) - x2 = self.res21(x2) - x2 = self.upscale22(x2) - x2 = self.res22(x2) - x2 = tf.nn.sigmoid(self.out_conv2(x2)) - x2 = nn.upsample2d(x2) - - x3 = self.upscale30(z) - x3 = self.res30(x3) - x3 = self.upscale31(x3) - x3 = self.res31(x3) - x3 = self.upscale32(x3) - x3 = self.res32(x3) - x3 = tf.nn.sigmoid(self.out_conv3(x3)) - 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 - self.out_ch = out_ch - self.kernel_size = kernel_size - self.dilations = dilations - self.subpixel = subpixel - self.use_activator = use_activator - super().__init__(*kwargs) - - def on_build(self, *args, **kwargs ): - 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, - padding='SAME', dilations=self.dilations) - - def forward(self, x): - x = self.conv1(x) - if self.subpixel: - x = nn.space_to_depth(x, 2) - if self.use_activator: - x = tf.nn.leaky_relu(x, 0.1) - return x - - def get_out_ch(self): - 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): - 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 @@ -1043,7 +415,7 @@ 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 ) - self.upscale1 = Upscale(ae_out_ch, ae_out_ch) + self.upscale1 = Upscale(ae_out_ch, ae_out_ch) def forward(self, inp): x = self.dense_norm(inp) @@ -1094,7 +466,7 @@ 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