mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
7386a9d6fd
SAEHD: added new option GAN power 0.0 .. 10.0 Train the network in Generative Adversarial manner. Forces the neural network to learn small details of the face. You can enable/disable this option at any time, but better to enable it when the network is trained enough. Typical value is 1.0 GAN power with pretrain mode will not work. Example of enabling GAN on 81k iters +5k iters https://i.imgur.com/OdXHLhU.jpg https://i.imgur.com/CYAJmJx.jpg dfhd: default Decoder dimensions are now 48 the preview for 256 res is now correctly displayed fixed model naming/renaming/removing Improvements for those involved in post-processing in AfterEffects: Codec is reverted back to x264 in order to properly use in AfterEffects and video players. Merger now always outputs the mask to workspace\data_dst\merged_mask removed raw modes except raw-rgb raw-rgb mode now outputs selected face mask_mode (before square mask) 'export alpha mask' button is replaced by 'show alpha mask'. You can view the alpha mask without recompute the frames. 8) 'merged *.bat' now also output 'result_mask.' video file. 8) 'merged lossless' now uses x264 lossless codec (before PNG codec) result_mask video file is always lossless. Thus you can use result_mask video file as mask layer in the AfterEffects.
459 lines
22 KiB
Python
459 lines
22 KiB
Python
import multiprocessing
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from functools import partial
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import numpy as np
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from core import mathlib
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from core.interact import interact as io
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from core.leras import nn
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from facelib import FaceType
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from models import ModelBase
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from samplelib import *
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class QModel(ModelBase):
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#override
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def on_initialize(self):
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device_config = nn.getCurrentDeviceConfig()
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self.model_data_format = "NCHW" if len(device_config.devices) != 0 and not self.is_debug() else "NHWC"
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nn.initialize(data_format=self.model_data_format)
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tf = nn.tf
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conv_kernel_initializer = nn.initializers.ca()
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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, kernel_initializer=conv_kernel_initializer )
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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.tf_space_to_depth(x, 2)
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if self.use_activator:
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x = nn.tf_gelu(x)
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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
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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', kernel_initializer=conv_kernel_initializer)
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def forward(self, x):
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x = self.conv1(x)
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x = nn.tf_gelu(x)
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x = nn.tf_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', kernel_initializer=conv_kernel_initializer)
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self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', kernel_initializer=conv_kernel_initializer)
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def forward(self, inp):
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x = self.conv1(inp)
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x = nn.tf_gelu(x)
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x = self.conv2(x)
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x = inp + x
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x = nn.tf_gelu(x)
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return x
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class Encoder(nn.ModelBase):
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def on_build(self, in_ch, e_ch):
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self.down1 = DownscaleBlock(in_ch, e_ch, n_downscales=4, kernel_size=5)
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def forward(self, inp):
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return nn.tf_flatten(self.down1(inp))
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class Inter(nn.ModelBase):
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def __init__(self, in_ch, lowest_dense_res, ae_ch, ae_out_ch, d_ch, **kwargs):
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self.in_ch, self.lowest_dense_res, self.ae_ch, self.ae_out_ch, self.d_ch = in_ch, lowest_dense_res, ae_ch, ae_out_ch, d_ch
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super().__init__(**kwargs)
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def on_build(self):
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in_ch, lowest_dense_res, ae_ch, ae_out_ch, d_ch = self.in_ch, self.lowest_dense_res, self.ae_ch, self.ae_out_ch, self.d_ch
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self.dense1 = nn.Dense( in_ch, ae_ch, kernel_initializer=tf.initializers.orthogonal )
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self.dense2 = nn.Dense( ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch, maxout_features=4, kernel_initializer=tf.initializers.orthogonal )
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self.upscale1 = Upscale(ae_out_ch, d_ch*8)
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self.res1 = ResidualBlock(d_ch*8)
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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.tf_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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x = self.res1(x)
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return x
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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):
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self.upscale1 = Upscale(in_ch, d_ch*4)
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self.res1 = ResidualBlock(d_ch*4)
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self.upscale2 = Upscale(d_ch*4, d_ch*2)
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self.res2 = ResidualBlock(d_ch*2)
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self.upscale3 = Upscale(d_ch*2, d_ch*1)
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self.res3 = ResidualBlock(d_ch*1)
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self.upscalem1 = Upscale(in_ch, d_ch)
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self.upscalem2 = Upscale(d_ch, d_ch//2)
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self.upscalem3 = Upscale(d_ch//2, d_ch//2)
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self.out_conv = nn.Conv2D( d_ch*1, 3, kernel_size=1, padding='SAME', kernel_initializer=conv_kernel_initializer)
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self.out_convm = nn.Conv2D( d_ch//2, 1, kernel_size=1, padding='SAME', kernel_initializer=conv_kernel_initializer)
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def forward(self, inp):
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z = inp
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x = self.upscale1 (z)
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x = self.res1 (x)
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x = self.upscale2 (x)
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x = self.res2 (x)
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x = self.upscale3 (x)
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x = self.res3 (x)
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y = self.upscalem1 (z)
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y = self.upscalem2 (y)
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y = self.upscalem3 (y)
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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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device_config = nn.getCurrentDeviceConfig()
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devices = device_config.devices
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resolution = self.resolution = 96
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ae_dims = 128
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e_dims = 128
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d_dims = 64
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self.pretrain = False
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self.pretrain_just_disabled = False
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masked_training = True
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models_opt_on_gpu = len(devices) == 1 and devices[0].total_mem_gb >= 4
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models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
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optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
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input_ch = 3
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output_ch = 3
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bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
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mask_shape = nn.get4Dshape(resolution,resolution,1)
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lowest_dense_res = resolution // 16
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self.model_filename_list = []
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with tf.device ('/CPU:0'):
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#Place holders on CPU
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self.warped_src = tf.placeholder (nn.tf_floatx, bgr_shape)
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self.warped_dst = tf.placeholder (nn.tf_floatx, bgr_shape)
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self.target_src = tf.placeholder (nn.tf_floatx, bgr_shape)
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self.target_dst = tf.placeholder (nn.tf_floatx, bgr_shape)
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self.target_srcm = tf.placeholder (nn.tf_floatx, mask_shape)
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self.target_dstm = tf.placeholder (nn.tf_floatx, mask_shape)
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# Initializing model classes
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with tf.device (models_opt_device):
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self.encoder = Encoder(in_ch=input_ch, e_ch=e_dims, name='encoder')
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encoder_out_ch = self.encoder.compute_output_channels ( (nn.tf_floatx, bgr_shape))
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self.inter = Inter (in_ch=encoder_out_ch, lowest_dense_res=lowest_dense_res, ae_ch=ae_dims, ae_out_ch=ae_dims, d_ch=d_dims, name='inter')
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inter_out_ch = self.inter.compute_output_channels ( (nn.tf_floatx, (None,encoder_out_ch)))
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self.decoder_src = Decoder(in_ch=inter_out_ch, d_ch=d_dims, name='decoder_src')
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self.decoder_dst = Decoder(in_ch=inter_out_ch, d_ch=d_dims, name='decoder_dst')
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self.model_filename_list += [ [self.encoder, 'encoder.npy' ],
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[self.inter, 'inter.npy' ],
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[self.decoder_src, 'decoder_src.npy'],
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[self.decoder_dst, 'decoder_dst.npy'] ]
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if self.is_training:
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self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
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# Initialize optimizers
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self.src_dst_opt = nn.TFRMSpropOptimizer(lr=2e-4, lr_dropout=0.3, name='src_dst_opt')
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self.src_dst_opt.initialize_variables(self.src_dst_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu )
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self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
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if self.is_training:
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# Adjust batch size for multiple GPU
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gpu_count = max(1, len(devices) )
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bs_per_gpu = max(1, 4 // gpu_count)
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self.set_batch_size( gpu_count*bs_per_gpu)
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# Compute losses per GPU
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gpu_pred_src_src_list = []
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gpu_pred_dst_dst_list = []
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gpu_pred_src_dst_list = []
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gpu_pred_src_srcm_list = []
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gpu_pred_dst_dstm_list = []
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gpu_pred_src_dstm_list = []
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gpu_src_losses = []
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gpu_dst_losses = []
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gpu_src_dst_loss_gvs = []
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for gpu_id in range(gpu_count):
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with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
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batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
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with tf.device(f'/CPU:0'):
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# slice on CPU, otherwise all batch data will be transfered to GPU first
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gpu_warped_src = self.warped_src [batch_slice,:,:,:]
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gpu_warped_dst = self.warped_dst [batch_slice,:,:,:]
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gpu_target_src = self.target_src [batch_slice,:,:,:]
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gpu_target_dst = self.target_dst [batch_slice,:,:,:]
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gpu_target_srcm = self.target_srcm[batch_slice,:,:,:]
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gpu_target_dstm = self.target_dstm[batch_slice,:,:,:]
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# process model tensors
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gpu_src_code = self.inter(self.encoder(gpu_warped_src))
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gpu_dst_code = self.inter(self.encoder(gpu_warped_dst))
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gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(gpu_src_code)
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gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
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gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
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gpu_pred_src_src_list.append(gpu_pred_src_src)
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gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
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gpu_pred_src_dst_list.append(gpu_pred_src_dst)
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gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
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gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
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gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
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gpu_target_srcm_blur = nn.tf_gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
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gpu_target_dstm_blur = nn.tf_gaussian_blur(gpu_target_dstm, max(1, resolution // 32) )
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gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
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gpu_target_dst_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_blur)
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gpu_target_src_masked_opt = gpu_target_src*gpu_target_srcm_blur if masked_training else gpu_target_src
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gpu_target_dst_masked_opt = gpu_target_dst_masked if masked_training else gpu_target_dst
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gpu_pred_src_src_masked_opt = gpu_pred_src_src*gpu_target_srcm_blur if masked_training else gpu_pred_src_src
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gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
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gpu_psd_target_dst_masked = gpu_pred_src_dst*gpu_target_dstm_blur
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gpu_psd_target_dst_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_blur)
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gpu_src_loss = tf.reduce_mean ( 10*nn.tf_dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
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gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked_opt - gpu_pred_src_src_masked_opt ), axis=[1,2,3])
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gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
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gpu_dst_loss = tf.reduce_mean ( 10*nn.tf_dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
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gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dst_masked_opt- gpu_pred_dst_dst_masked_opt ), axis=[1,2,3])
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gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
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gpu_src_losses += [gpu_src_loss]
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gpu_dst_losses += [gpu_dst_loss]
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gpu_G_loss = gpu_src_loss + gpu_dst_loss
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gpu_src_dst_loss_gvs += [ nn.tf_gradients ( gpu_G_loss, self.src_dst_trainable_weights ) ]
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# Average losses and gradients, and create optimizer update ops
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with tf.device (models_opt_device):
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pred_src_src = nn.tf_concat(gpu_pred_src_src_list, 0)
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pred_dst_dst = nn.tf_concat(gpu_pred_dst_dst_list, 0)
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pred_src_dst = nn.tf_concat(gpu_pred_src_dst_list, 0)
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pred_src_srcm = nn.tf_concat(gpu_pred_src_srcm_list, 0)
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pred_dst_dstm = nn.tf_concat(gpu_pred_dst_dstm_list, 0)
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pred_src_dstm = nn.tf_concat(gpu_pred_src_dstm_list, 0)
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src_loss = nn.tf_average_tensor_list(gpu_src_losses)
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dst_loss = nn.tf_average_tensor_list(gpu_dst_losses)
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src_dst_loss_gv = nn.tf_average_gv_list (gpu_src_dst_loss_gvs)
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src_dst_loss_gv_op = self.src_dst_opt.get_update_op (src_dst_loss_gv)
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# Initializing training and view functions
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def src_dst_train(warped_src, target_src, target_srcm, \
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warped_dst, target_dst, target_dstm):
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s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
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feed_dict={self.warped_src :warped_src,
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self.target_src :target_src,
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self.target_srcm:target_srcm,
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self.warped_dst :warped_dst,
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self.target_dst :target_dst,
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self.target_dstm:target_dstm,
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})
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s = np.mean(s)
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d = np.mean(d)
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return s, d
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self.src_dst_train = src_dst_train
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def AE_view(warped_src, warped_dst):
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return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
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feed_dict={self.warped_src:warped_src,
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self.warped_dst:warped_dst})
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self.AE_view = AE_view
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else:
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# Initializing merge function
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with tf.device( f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
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gpu_dst_code = self.inter(self.encoder(self.warped_dst))
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gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
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_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
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def AE_merge( warped_dst):
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return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst})
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self.AE_merge = AE_merge
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# Loading/initializing all models/optimizers weights
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for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
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do_init = self.is_first_run()
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if self.pretrain_just_disabled:
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if model == self.inter:
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do_init = True
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if not do_init:
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do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
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if do_init and self.pretrained_model_path is not None:
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pretrained_filepath = self.pretrained_model_path / filename
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if pretrained_filepath.exists():
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do_init = not model.load_weights(pretrained_filepath)
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if do_init:
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model.init_weights()
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# initializing sample generators
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if self.is_training:
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t = SampleProcessor.Types
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face_type = t.FACE_TYPE_FULL
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training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
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training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path()
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cpu_count = min(multiprocessing.cpu_count(), 8)
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src_generators_count = cpu_count // 2
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dst_generators_count = cpu_count // 2
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|
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self.set_training_data_generators ([
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SampleGeneratorFace(training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(random_flip=True if self.pretrain else False),
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output_sample_types = [ {'types' : (t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution':resolution, },
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{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution': resolution, },
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{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'data_format':nn.data_format, 'resolution': resolution } ],
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generators_count=src_generators_count ),
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|
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SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(random_flip=True if self.pretrain else False),
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output_sample_types = [ {'types' : (t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution':resolution},
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{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution': resolution},
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|
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'data_format':nn.data_format, 'resolution': resolution} ],
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|
generators_count=dst_generators_count )
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|
])
|
|
|
|
self.last_samples = None
|
|
|
|
#override
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|
def get_model_filename_list(self):
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|
return self.model_filename_list
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|
|
|
#override
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|
def onSave(self):
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|
for model, filename in io.progress_bar_generator(self.get_model_filename_list(), "Saving", leave=False):
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|
model.save_weights ( self.get_strpath_storage_for_file(filename) )
|
|
|
|
#override
|
|
def onTrainOneIter(self):
|
|
|
|
if self.get_iter() % 3 == 0 and self.last_samples is not None:
|
|
( (warped_src, target_src, target_srcm), \
|
|
(warped_dst, target_dst, target_dstm) ) = self.last_samples
|
|
warped_src = target_src
|
|
warped_dst = target_dst
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|
else:
|
|
samples = self.last_samples = self.generate_next_samples()
|
|
( (warped_src, target_src, target_srcm), \
|
|
(warped_dst, target_dst, target_dstm) ) = samples
|
|
|
|
src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm,
|
|
warped_dst, target_dst, target_dstm)
|
|
|
|
return ( ('src_loss', src_loss), ('dst_loss', dst_loss), )
|
|
|
|
#override
|
|
def onGetPreview(self, samples):
|
|
( (warped_src, target_src, target_srcm),
|
|
(warped_dst, target_dst, target_dstm) ) = samples
|
|
|
|
S, D, SS, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ]
|
|
DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
|
|
|
|
target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )]
|
|
|
|
n_samples = min(4, self.get_batch_size() )
|
|
result = []
|
|
st = []
|
|
for i in range(n_samples):
|
|
ar = S[i], SS[i], D[i], DD[i], SD[i]
|
|
st.append ( np.concatenate ( ar, axis=1) )
|
|
|
|
result += [ ('Quick96', np.concatenate (st, axis=0 )), ]
|
|
|
|
st_m = []
|
|
for i in range(n_samples):
|
|
ar = S[i]*target_srcm[i], SS[i], D[i]*target_dstm[i], DD[i]*DDM[i], SD[i]*(DDM[i]*SDM[i])
|
|
st_m.append ( np.concatenate ( ar, axis=1) )
|
|
|
|
result += [ ('Quick96 masked', np.concatenate (st_m, axis=0 )), ]
|
|
|
|
return result
|
|
|
|
def predictor_func (self, face=None):
|
|
face = nn.to_data_format(face[None,...], self.model_data_format, "NHWC")
|
|
|
|
bgr, mask_dst_dstm, mask_src_dstm = [ nn.to_data_format(x, "NHWC", self.model_data_format).astype(np.float32) for x in self.AE_merge (face) ]
|
|
mask = mask_dst_dstm[0] * mask_src_dstm[0]
|
|
return bgr[0], mask[...,0]
|
|
|
|
#override
|
|
def get_MergerConfig(self):
|
|
face_type = FaceType.FULL
|
|
|
|
import merger
|
|
return self.predictor_func, (self.resolution, self.resolution, 3), merger.MergerConfigMasked(face_type=face_type,
|
|
default_mode = 'overlay',
|
|
clip_hborder_mask_per=0.0625 if (face_type != FaceType.HALF) else 0,
|
|
)
|
|
|
|
Model = QModel
|