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https://github.com/iperov/DeepFaceLab.git
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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.
823 lines
48 KiB
Python
823 lines
48 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 SAEHDModel(ModelBase):
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#override
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def on_initialize_options(self):
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device_config = nn.getCurrentDeviceConfig()
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lowest_vram = 2
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if len(device_config.devices) != 0:
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lowest_vram = device_config.devices.get_worst_device().total_mem_gb
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if lowest_vram >= 4:
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suggest_batch_size = 8
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else:
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suggest_batch_size = 4
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yn_str = {True:'y',False:'n'}
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default_resolution = self.options['resolution'] = self.load_or_def_option('resolution', 128)
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default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'f')
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default_models_opt_on_gpu = self.options['models_opt_on_gpu'] = self.load_or_def_option('models_opt_on_gpu', True)
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default_archi = self.options['archi'] = self.load_or_def_option('archi', 'dfhd')
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default_ae_dims = self.options['ae_dims'] = self.load_or_def_option('ae_dims', 256)
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default_e_dims = self.options['e_dims'] = self.load_or_def_option('e_dims', 64)
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default_d_dims = 48 if self.options['archi'] == 'dfhd' else 64
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default_d_dims = self.options['d_dims'] = self.load_or_def_option('d_dims', default_d_dims)
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default_d_mask_dims = default_d_dims // 3
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default_d_mask_dims += default_d_mask_dims % 2
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default_d_mask_dims = self.options['d_mask_dims'] = self.load_or_def_option('d_mask_dims', default_d_mask_dims)
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default_use_float16 = self.options['use_float16'] = self.load_or_def_option('use_float16', False)
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default_learn_mask = self.options['learn_mask'] = self.load_or_def_option('learn_mask', True)
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default_lr_dropout = self.options['lr_dropout'] = self.load_or_def_option('lr_dropout', False)
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default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
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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_true_face_power = self.options['true_face_power'] = self.load_or_def_option('true_face_power', 0.0)
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default_face_style_power = self.options['face_style_power'] = self.load_or_def_option('face_style_power', 0.0)
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default_bg_style_power = self.options['bg_style_power'] = self.load_or_def_option('bg_style_power', 0.0)
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default_ct_mode = self.options['ct_mode'] = self.load_or_def_option('ct_mode', 'none')
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default_clipgrad = self.options['clipgrad'] = self.load_or_def_option('clipgrad', False)
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default_pretrain = self.options['pretrain'] = self.load_or_def_option('pretrain', False)
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ask_override = self.ask_override()
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if self.is_first_run() or ask_override:
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self.ask_enable_autobackup()
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self.ask_write_preview_history()
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self.ask_target_iter()
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self.ask_random_flip()
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self.ask_batch_size(suggest_batch_size)
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if self.is_first_run():
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resolution = io.input_int("Resolution", default_resolution, add_info="64-256", help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
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resolution = np.clip ( (resolution // 16) * 16, 64, 256)
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self.options['resolution'] = resolution
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self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f'], help_message="Half / mid face / full face. Half face has better resolution, but covers less area of cheeks. Mid face is 30% wider than half face.").lower()
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self.options['archi'] = io.input_str ("AE architecture", default_archi, ['dfhd','liaehd','df','liae'], help_message="'df' keeps faces more natural. 'liae' can fix overly different face shapes. 'hd' is heavyweight version for the best quality.").lower() #-s version is slower, but has decreased change to collapse.
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self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dimensions", default_ae_dims, add_info="32-1024", help_message="All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
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e_dims = np.clip ( io.input_int("Encoder dimensions", default_e_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
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self.options['e_dims'] = e_dims + e_dims % 2
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d_dims = np.clip ( io.input_int("Decoder dimensions", default_d_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
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self.options['d_dims'] = d_dims + d_dims % 2
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d_mask_dims = np.clip ( io.input_int("Decoder mask dimensions", default_d_mask_dims, add_info="16-256", help_message="Typical mask dimensions = decoder dimensions / 3. If you manually cut out obstacles from the dst mask, you can increase this parameter to achieve better quality." ), 16, 256 )
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self.options['d_mask_dims'] = d_mask_dims + d_mask_dims % 2
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if self.is_first_run() or ask_override:
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self.options['learn_mask'] = io.input_bool ("Learn mask", default_learn_mask, help_message="Learning mask can help model to recognize face directions. Learn without mask can reduce model size, in this case merger forced to use 'not predicted mask' that is not smooth as predicted.")
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if self.is_first_run() or ask_override:
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if len(device_config.devices) == 1:
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self.options['models_opt_on_gpu'] = io.input_bool ("Place models and optimizer on GPU", default_models_opt_on_gpu, help_message="When you train on one GPU, by default model and optimizer weights are placed on GPU to accelerate the process. You can place they on CPU to free up extra VRAM, thus set bigger dimensions.")
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self.options['use_float16'] = io.input_bool ("Use float16", default_use_float16, help_message="Experimental option. Reduces the model size by half. Increases the speed of training. Decreases the accuracy of the model. The model may collapse. Model does not study the mask in large resolutions.")
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self.options['lr_dropout'] = io.input_bool ("Use learning rate dropout", default_lr_dropout, help_message="When the face is trained enough, you can enable this option to get extra sharpness for less amount of iterations.")
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self.options['random_warp'] = io.input_bool ("Enable random warp of samples", default_random_warp, help_message="Random warp is required to generalize facial expressions of both faces. When the face is trained enough, you can disable it to get extra sharpness for less amount of iterations.")
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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. Accelerates the speed of training. Forces the neural network to learn small details of the face. You can enable/disable this option at any time. Typical value is 1.0"), 0.0, 10.0 )
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if 'df' in self.options['archi']:
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self.options['true_face_power'] = np.clip ( io.input_number ("'True face' power.", default_true_face_power, add_info="0.0000 .. 1.0", help_message="Experimental option. Discriminates result face to be more like src face. Higher value - stronger discrimination. Typical value is 0.01 . Comparison - https://i.imgur.com/czScS9q.png"), 0.0, 1.0 )
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else:
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self.options['true_face_power'] = 0.0
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self.options['face_style_power'] = np.clip ( io.input_number("Face style power", default_face_style_power, add_info="0.0..100.0", help_message="Learn to transfer face style details such as light and color conditions. Warning: Enable it only after 10k iters, when predicted face is clear enough to start learn style. Start from 0.1 value and check history changes. Enabling this option increases the chance of model collapse."), 0.0, 100.0 )
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self.options['bg_style_power'] = np.clip ( io.input_number("Background style power", default_bg_style_power, add_info="0.0..100.0", help_message="Learn to transfer background around face. This can make face more like dst. Enabling this option increases the chance of model collapse. Typical value is 2.0"), 0.0, 100.0 )
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self.options['ct_mode'] = io.input_str (f"Color transfer for src faceset", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
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self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")
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self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain, help_message="Pretrain the model with large amount of various faces. After that, model can be used to train the fakes more quickly.")
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if self.options['pretrain'] and self.get_pretraining_data_path() is None:
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raise Exception("pretraining_data_path is not defined")
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self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)
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if self.pretrain_just_disabled:
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self.set_iter(1)
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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(floatx="float16" if self.options['use_float16'] else "float32",
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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 = tf.nn.leaky_relu(x, 0.1)
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return x
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def get_out_ch(self):
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return (self.out_ch // 4) * 4
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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 = tf.nn.leaky_relu(x, 0.1)
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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 = tf.nn.leaky_relu(x, 0.2)
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x = self.conv2(x)
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x = tf.nn.leaky_relu(inp + x, 0.2)
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return x
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class UpdownResidualBlock(nn.ModelBase):
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def on_build(self, ch, inner_ch, kernel_size=3 ):
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self.up = Upscale (ch, inner_ch, kernel_size=kernel_size)
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self.res = ResidualBlock (inner_ch, kernel_size=kernel_size)
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self.down = Downscale (inner_ch, ch, kernel_size=kernel_size, use_activator=False)
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def forward(self, inp):
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x = self.up(inp)
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x = upx = self.res(x)
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x = self.down(x)
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x = x + inp
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x = tf.nn.leaky_relu(x, 0.2)
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return x, upx
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class Encoder(nn.ModelBase):
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def on_build(self, in_ch, e_ch, is_hd):
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self.is_hd=is_hd
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if self.is_hd:
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self.down1 = DownscaleBlock(in_ch, e_ch*2, n_downscales=4, kernel_size=3, dilations=1)
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self.down2 = DownscaleBlock(in_ch, e_ch*2, n_downscales=4, kernel_size=5, dilations=1)
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self.down3 = DownscaleBlock(in_ch, e_ch//2, n_downscales=4, kernel_size=5, dilations=2)
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self.down4 = DownscaleBlock(in_ch, e_ch//2, n_downscales=4, kernel_size=7, dilations=2)
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else:
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self.down1 = DownscaleBlock(in_ch, e_ch, n_downscales=4, kernel_size=5, dilations=1, subpixel=False)
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def forward(self, inp):
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if self.is_hd:
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x = tf.concat([ nn.tf_flatten(self.down1(inp)),
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nn.tf_flatten(self.down2(inp)),
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nn.tf_flatten(self.down3(inp)),
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nn.tf_flatten(self.down4(inp)) ], -1 )
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else:
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x = nn.tf_flatten(self.down1(inp))
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return x
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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, **kwargs):
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self.in_ch, self.lowest_dense_res, self.ae_ch, self.ae_out_ch = in_ch, lowest_dense_res, ae_ch, ae_out_ch
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super().__init__(**kwargs)
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def on_build(self):
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in_ch, lowest_dense_res, ae_ch, ae_out_ch = self.in_ch, self.lowest_dense_res, self.ae_ch, self.ae_out_ch
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self.dense1 = nn.Dense( in_ch, ae_ch )
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self.dense2 = nn.Dense( ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch )
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self.upscale1 = Upscale(ae_out_ch, ae_out_ch)
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def forward(self, inp):
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x = self.dense1(inp)
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x = self.dense2(x)
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x = nn.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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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, d_mask_ch, is_hd ):
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self.is_hd = is_hd
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self.upscale0 = Upscale(in_ch, d_ch*8, kernel_size=3)
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self.upscale1 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
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self.upscale2 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
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if is_hd:
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self.res0 = UpdownResidualBlock(in_ch, d_ch*8, kernel_size=3)
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self.res1 = UpdownResidualBlock(d_ch*8, d_ch*4, kernel_size=3)
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self.res2 = UpdownResidualBlock(d_ch*4, d_ch*2, kernel_size=3)
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self.res3 = UpdownResidualBlock(d_ch*2, d_ch, kernel_size=3)
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else:
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self.res0 = ResidualBlock(d_ch*8, kernel_size=3)
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self.res1 = ResidualBlock(d_ch*4, kernel_size=3)
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self.res2 = ResidualBlock(d_ch*2, kernel_size=3)
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self.out_conv = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME', kernel_initializer=conv_kernel_initializer)
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self.upscalem0 = Upscale(in_ch, d_mask_ch*8, kernel_size=3)
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self.upscalem1 = Upscale(d_mask_ch*8, d_mask_ch*4, kernel_size=3)
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self.upscalem2 = Upscale(d_mask_ch*4, d_mask_ch*2, kernel_size=3)
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self.out_convm = nn.Conv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME', kernel_initializer=conv_kernel_initializer)
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def get_weights_ex(self, include_mask):
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# Call internal get_weights in order to initialize inner logic
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self.get_weights()
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weights = self.upscale0.get_weights() + self.upscale1.get_weights() + self.upscale2.get_weights() \
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+ self.res0.get_weights() + self.res1.get_weights() + self.res2.get_weights() + self.out_conv.get_weights()
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if include_mask:
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weights += self.upscalem0.get_weights() + self.upscalem1.get_weights() + self.upscalem2.get_weights() \
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+ self.out_convm.get_weights()
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return weights
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def forward(self, inp):
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z = inp
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if self.is_hd:
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x, upx = self.res0(z)
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x = self.upscale0(x)
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x = tf.nn.leaky_relu(x + upx, 0.2)
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x, upx = self.res1(x)
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x = self.upscale1(x)
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x = tf.nn.leaky_relu(x + upx, 0.2)
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x, upx = self.res2(x)
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x = self.upscale2(x)
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x = tf.nn.leaky_relu(x + upx, 0.2)
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x, upx = self.res3(x)
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else:
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x = self.upscale0(z)
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x = self.res0(x)
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x = self.upscale1(x)
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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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m = self.upscalem0(z)
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m = self.upscalem1(m)
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m = self.upscalem2(m)
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return tf.nn.sigmoid(self.out_conv(x)), \
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tf.nn.sigmoid(self.out_convm(m))
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class CodeDiscriminator(nn.ModelBase):
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def on_build(self, in_ch, code_res, ch=256):
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n_downscales = 1 + code_res // 8
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self.convs = []
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prev_ch = in_ch
|
|
for i in range(n_downscales):
|
|
cur_ch = ch * min( (2**i), 8 )
|
|
self.convs.append ( nn.Conv2D( prev_ch, cur_ch, kernel_size=4 if i == 0 else 3, strides=2, padding='SAME', kernel_initializer=conv_kernel_initializer) )
|
|
prev_ch = cur_ch
|
|
|
|
self.out_conv = nn.Conv2D( prev_ch, 1, kernel_size=1, padding='VALID', kernel_initializer=conv_kernel_initializer)
|
|
|
|
def forward(self, x):
|
|
for conv in self.convs:
|
|
x = tf.nn.leaky_relu( conv(x), 0.1 )
|
|
return self.out_conv(x)
|
|
|
|
device_config = nn.getCurrentDeviceConfig()
|
|
devices = device_config.devices
|
|
|
|
self.resolution = resolution = self.options['resolution']
|
|
learn_mask = self.options['learn_mask']
|
|
archi = self.options['archi']
|
|
ae_dims = self.options['ae_dims']
|
|
e_dims = self.options['e_dims']
|
|
d_dims = self.options['d_dims']
|
|
d_mask_dims = self.options['d_mask_dims']
|
|
self.pretrain = self.options['pretrain']
|
|
|
|
self.gan_power = gan_power = self.options['gan_power'] if not self.pretrain else 0.0
|
|
|
|
masked_training = True
|
|
|
|
models_opt_on_gpu = False if len(devices) != 1 else self.options['models_opt_on_gpu']
|
|
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
|
|
optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
|
|
|
|
input_ch = 3
|
|
output_ch = 3
|
|
bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
|
|
mask_shape = nn.get4Dshape(resolution,resolution,1)
|
|
lowest_dense_res = resolution // 16
|
|
|
|
self.model_filename_list = []
|
|
|
|
|
|
with tf.device ('/CPU:0'):
|
|
#Place holders on CPU
|
|
self.warped_src = tf.placeholder (nn.tf_floatx, bgr_shape)
|
|
self.warped_dst = tf.placeholder (nn.tf_floatx, bgr_shape)
|
|
|
|
self.target_src = tf.placeholder (nn.tf_floatx, bgr_shape)
|
|
self.target_dst = tf.placeholder (nn.tf_floatx, bgr_shape)
|
|
|
|
self.target_srcm = tf.placeholder (nn.tf_floatx, mask_shape)
|
|
self.target_dstm = tf.placeholder (nn.tf_floatx, mask_shape)
|
|
|
|
# Initializing model classes
|
|
with tf.device (models_opt_device):
|
|
if 'df' in archi:
|
|
self.encoder = Encoder(in_ch=input_ch, e_ch=e_dims, is_hd='hd' in archi, name='encoder')
|
|
encoder_out_ch = self.encoder.compute_output_channels ( (nn.tf_floatx, bgr_shape))
|
|
|
|
self.inter = Inter (in_ch=encoder_out_ch, lowest_dense_res=lowest_dense_res, ae_ch=ae_dims, ae_out_ch=ae_dims, name='inter')
|
|
inter_out_ch = self.inter.compute_output_channels ( (nn.tf_floatx, (None,encoder_out_ch)))
|
|
|
|
self.decoder_src = Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd='hd' in archi, name='decoder_src')
|
|
self.decoder_dst = Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd='hd' in archi, name='decoder_dst')
|
|
|
|
self.model_filename_list += [ [self.encoder, 'encoder.npy' ],
|
|
[self.inter, 'inter.npy' ],
|
|
[self.decoder_src, 'decoder_src.npy'],
|
|
[self.decoder_dst, 'decoder_dst.npy'] ]
|
|
|
|
if self.is_training:
|
|
if self.options['true_face_power'] != 0:
|
|
self.code_discriminator = CodeDiscriminator(ae_dims, code_res=lowest_dense_res*2, name='dis' )
|
|
self.model_filename_list += [ [self.code_discriminator, 'code_discriminator.npy'] ]
|
|
|
|
elif 'liae' in archi:
|
|
self.encoder = Encoder(in_ch=input_ch, e_ch=e_dims, is_hd='hd' in archi, name='encoder')
|
|
encoder_out_ch = self.encoder.compute_output_channels ( (nn.tf_floatx, bgr_shape))
|
|
|
|
self.inter_AB = Inter(in_ch=encoder_out_ch, lowest_dense_res=lowest_dense_res, ae_ch=ae_dims, ae_out_ch=ae_dims*2, name='inter_AB')
|
|
self.inter_B = Inter(in_ch=encoder_out_ch, lowest_dense_res=lowest_dense_res, ae_ch=ae_dims, ae_out_ch=ae_dims*2, name='inter_B')
|
|
|
|
inter_AB_out_ch = self.inter_AB.compute_output_channels ( (nn.tf_floatx, (None,encoder_out_ch)))
|
|
inter_B_out_ch = self.inter_B.compute_output_channels ( (nn.tf_floatx, (None,encoder_out_ch)))
|
|
inters_out_ch = inter_AB_out_ch+inter_B_out_ch
|
|
self.decoder = Decoder(in_ch=inters_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd='hd' in archi, name='decoder')
|
|
|
|
self.model_filename_list += [ [self.encoder, 'encoder.npy'],
|
|
[self.inter_AB, 'inter_AB.npy'],
|
|
[self.inter_B , 'inter_B.npy'],
|
|
[self.decoder , 'decoder.npy'] ]
|
|
|
|
if self.is_training:
|
|
if gan_power != 0:
|
|
self.D_src = nn.PatchDiscriminator(patch_size=resolution//16, in_ch=output_ch, base_ch=512, name="D_src")
|
|
self.D_dst = nn.PatchDiscriminator(patch_size=resolution//16, in_ch=output_ch, base_ch=512, name="D_dst")
|
|
self.model_filename_list += [ [self.D_src, 'D_src.npy'] ]
|
|
self.model_filename_list += [ [self.D_dst, 'D_dst.npy'] ]
|
|
|
|
# Initialize optimizers
|
|
lr=5e-5
|
|
lr_dropout = 0.3 if self.options['lr_dropout'] else 1.0
|
|
clipnorm = 1.0 if self.options['clipgrad'] else 0.0
|
|
self.src_dst_opt = nn.TFRMSpropOptimizer(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
|
|
self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
|
|
if 'df' in archi:
|
|
self.src_dst_all_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
|
|
self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights_ex(learn_mask) + self.decoder_dst.get_weights_ex(learn_mask)
|
|
|
|
elif 'liae' in archi:
|
|
self.src_dst_all_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights()
|
|
self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights_ex(learn_mask)
|
|
|
|
self.src_dst_opt.initialize_variables (self.src_dst_all_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu)
|
|
|
|
if self.options['true_face_power'] != 0:
|
|
self.D_code_opt = nn.TFRMSpropOptimizer(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_code_opt')
|
|
self.D_code_opt.initialize_variables ( self.code_discriminator.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
|
|
self.model_filename_list += [ (self.D_code_opt, 'D_code_opt.npy') ]
|
|
|
|
if gan_power != 0:
|
|
self.D_src_dst_opt = nn.TFRMSpropOptimizer(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_src_dst_opt')
|
|
self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights()+self.D_dst.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
|
|
self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_dst_opt.npy') ]
|
|
|
|
if self.is_training:
|
|
# Adjust batch size for multiple GPU
|
|
gpu_count = max(1, len(devices) )
|
|
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
|
|
self.set_batch_size( gpu_count*bs_per_gpu)
|
|
|
|
|
|
# Compute losses per GPU
|
|
gpu_pred_src_src_list = []
|
|
gpu_pred_dst_dst_list = []
|
|
gpu_pred_src_dst_list = []
|
|
gpu_pred_src_srcm_list = []
|
|
gpu_pred_dst_dstm_list = []
|
|
gpu_pred_src_dstm_list = []
|
|
|
|
gpu_src_losses = []
|
|
gpu_dst_losses = []
|
|
gpu_G_loss_gvs = []
|
|
gpu_D_code_loss_gvs = []
|
|
gpu_D_src_dst_loss_gvs = []
|
|
for gpu_id in range(gpu_count):
|
|
with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
|
|
|
|
with tf.device(f'/CPU:0'):
|
|
# slice on CPU, otherwise all batch data will be transfered to GPU first
|
|
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
|
|
gpu_warped_src = self.warped_src [batch_slice,:,:,:]
|
|
gpu_warped_dst = self.warped_dst [batch_slice,:,:,:]
|
|
gpu_target_src = self.target_src [batch_slice,:,:,:]
|
|
gpu_target_dst = self.target_dst [batch_slice,:,:,:]
|
|
gpu_target_srcm = self.target_srcm[batch_slice,:,:,:]
|
|
gpu_target_dstm = self.target_dstm[batch_slice,:,:,:]
|
|
|
|
# process model tensors
|
|
if 'df' in archi:
|
|
gpu_src_code = self.inter(self.encoder(gpu_warped_src))
|
|
gpu_dst_code = self.inter(self.encoder(gpu_warped_dst))
|
|
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(gpu_src_code)
|
|
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
|
|
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
|
|
|
|
elif 'liae' in archi:
|
|
gpu_src_code = self.encoder (gpu_warped_src)
|
|
gpu_src_inter_AB_code = self.inter_AB (gpu_src_code)
|
|
gpu_src_code = tf.concat([gpu_src_inter_AB_code,gpu_src_inter_AB_code], nn.conv2d_ch_axis )
|
|
gpu_dst_code = self.encoder (gpu_warped_dst)
|
|
gpu_dst_inter_B_code = self.inter_B (gpu_dst_code)
|
|
gpu_dst_inter_AB_code = self.inter_AB (gpu_dst_code)
|
|
gpu_dst_code = tf.concat([gpu_dst_inter_B_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis )
|
|
gpu_src_dst_code = tf.concat([gpu_dst_inter_AB_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis )
|
|
|
|
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder(gpu_src_code)
|
|
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
|
|
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
|
|
|
|
gpu_pred_src_src_list.append(gpu_pred_src_src)
|
|
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
|
|
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
|
|
|
|
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
|
|
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
|
|
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
|
|
|
|
gpu_target_srcm_blur = nn.tf_gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
|
|
gpu_target_dstm_blur = nn.tf_gaussian_blur(gpu_target_dstm, max(1, resolution // 32) )
|
|
|
|
gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
|
|
gpu_target_dst_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_blur)
|
|
|
|
gpu_target_src_masked_opt = gpu_target_src*gpu_target_srcm_blur if masked_training else gpu_target_src
|
|
gpu_target_dst_masked_opt = gpu_target_dst_masked if masked_training else gpu_target_dst
|
|
|
|
gpu_pred_src_src_masked_opt = gpu_pred_src_src*gpu_target_srcm_blur if masked_training else gpu_pred_src_src
|
|
gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
|
|
|
|
gpu_psd_target_dst_masked = gpu_pred_src_dst*gpu_target_dstm_blur
|
|
gpu_psd_target_dst_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_blur)
|
|
|
|
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])
|
|
gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked_opt - gpu_pred_src_src_masked_opt ), axis=[1,2,3])
|
|
if learn_mask:
|
|
gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
|
|
|
|
face_style_power = self.options['face_style_power'] / 100.0
|
|
if face_style_power != 0 and not self.pretrain:
|
|
gpu_src_loss += nn.tf_style_loss(gpu_psd_target_dst_masked, gpu_target_dst_masked, gaussian_blur_radius=resolution//16, loss_weight=10000*face_style_power)
|
|
|
|
bg_style_power = self.options['bg_style_power'] / 100.0
|
|
if bg_style_power != 0 and not self.pretrain:
|
|
gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*nn.tf_dssim(gpu_psd_target_dst_anti_masked, gpu_target_dst_anti_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
|
|
gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*tf.square( gpu_psd_target_dst_anti_masked - gpu_target_dst_anti_masked), axis=[1,2,3] )
|
|
|
|
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])
|
|
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dst_masked_opt- gpu_pred_dst_dst_masked_opt ), axis=[1,2,3])
|
|
if learn_mask:
|
|
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
|
|
|
|
gpu_src_losses += [gpu_src_loss]
|
|
gpu_dst_losses += [gpu_dst_loss]
|
|
|
|
gpu_G_loss = gpu_src_loss + gpu_dst_loss
|
|
|
|
def DLoss(labels,logits):
|
|
return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits), axis=[1,2,3])
|
|
|
|
if self.options['true_face_power'] != 0:
|
|
gpu_src_code_d = self.code_discriminator( gpu_src_code )
|
|
gpu_src_code_d_ones = tf.ones_like (gpu_src_code_d)
|
|
gpu_src_code_d_zeros = tf.zeros_like(gpu_src_code_d)
|
|
gpu_dst_code_d = self.code_discriminator( gpu_dst_code )
|
|
gpu_dst_code_d_ones = tf.ones_like(gpu_dst_code_d)
|
|
|
|
gpu_G_loss += self.options['true_face_power']*DLoss(gpu_src_code_d_ones, gpu_src_code_d)
|
|
|
|
gpu_D_code_loss = (DLoss(gpu_src_code_d_ones , gpu_dst_code_d) + \
|
|
DLoss(gpu_src_code_d_zeros, gpu_src_code_d) ) * 0.5
|
|
|
|
gpu_D_code_loss_gvs += [ nn.tf_gradients (gpu_D_code_loss, self.code_discriminator.get_weights() ) ]
|
|
|
|
if gan_power != 0:
|
|
gpu_pred_src_src_d = self.D_src(gpu_pred_src_src_masked_opt)
|
|
gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d)
|
|
gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d)
|
|
gpu_target_src_d = self.D_src(gpu_target_src_masked_opt)
|
|
gpu_target_src_d_ones = tf.ones_like(gpu_target_src_d)
|
|
gpu_pred_dst_dst_d = self.D_dst(gpu_pred_dst_dst_masked_opt)
|
|
gpu_pred_dst_dst_d_ones = tf.ones_like (gpu_pred_dst_dst_d)
|
|
gpu_pred_dst_dst_d_zeros = tf.zeros_like(gpu_pred_dst_dst_d)
|
|
gpu_target_dst_d = self.D_dst(gpu_target_dst_masked_opt)
|
|
gpu_target_dst_d_ones = tf.ones_like(gpu_target_dst_d)
|
|
|
|
gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \
|
|
DLoss(gpu_pred_src_src_d_zeros, gpu_pred_src_src_d) ) * 0.5 + \
|
|
(DLoss(gpu_target_dst_d_ones , gpu_target_dst_d) + \
|
|
DLoss(gpu_pred_dst_dst_d_zeros, gpu_pred_dst_dst_d) ) * 0.5
|
|
|
|
gpu_D_src_dst_loss_gvs += [ nn.tf_gradients (gpu_D_src_dst_loss, self.D_src.get_weights()+self.D_dst.get_weights() ) ]
|
|
|
|
gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + DLoss(gpu_pred_dst_dst_d_ones, gpu_pred_dst_dst_d))
|
|
|
|
|
|
gpu_G_loss_gvs += [ nn.tf_gradients ( gpu_G_loss, self.src_dst_trainable_weights ) ]
|
|
|
|
|
|
# Average losses and gradients, and create optimizer update ops
|
|
with tf.device (models_opt_device):
|
|
pred_src_src = nn.tf_concat(gpu_pred_src_src_list, 0)
|
|
pred_dst_dst = nn.tf_concat(gpu_pred_dst_dst_list, 0)
|
|
pred_src_dst = nn.tf_concat(gpu_pred_src_dst_list, 0)
|
|
pred_src_srcm = nn.tf_concat(gpu_pred_src_srcm_list, 0)
|
|
pred_dst_dstm = nn.tf_concat(gpu_pred_dst_dstm_list, 0)
|
|
pred_src_dstm = nn.tf_concat(gpu_pred_src_dstm_list, 0)
|
|
src_loss = nn.tf_average_tensor_list(gpu_src_losses)
|
|
dst_loss = nn.tf_average_tensor_list(gpu_dst_losses)
|
|
src_dst_loss_gv_op = self.src_dst_opt.get_update_op (nn.tf_average_gv_list (gpu_G_loss_gvs))
|
|
|
|
if self.options['true_face_power'] != 0:
|
|
D_loss_gv_op = self.D_code_opt.get_update_op (nn.tf_average_gv_list(gpu_D_code_loss_gvs))
|
|
|
|
if gan_power != 0:
|
|
src_D_src_dst_loss_gv_op = self.D_src_dst_opt.get_update_op (nn.tf_average_gv_list(gpu_D_src_dst_loss_gvs) )
|
|
|
|
|
|
# Initializing training and view functions
|
|
def src_dst_train(warped_src, target_src, target_srcm, \
|
|
warped_dst, target_dst, target_dstm):
|
|
s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
|
|
feed_dict={self.warped_src :warped_src,
|
|
self.target_src :target_src,
|
|
self.target_srcm:target_srcm,
|
|
self.warped_dst :warped_dst,
|
|
self.target_dst :target_dst,
|
|
self.target_dstm:target_dstm,
|
|
})
|
|
s = np.mean(s)
|
|
d = np.mean(d)
|
|
return s, d
|
|
self.src_dst_train = src_dst_train
|
|
|
|
if self.options['true_face_power'] != 0:
|
|
def D_train(warped_src, warped_dst):
|
|
nn.tf_sess.run ([D_loss_gv_op], feed_dict={self.warped_src: warped_src, self.warped_dst: warped_dst})
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|
self.D_train = D_train
|
|
|
|
if gan_power != 0:
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def D_src_dst_train(warped_src, target_src, target_srcm, \
|
|
warped_dst, target_dst, target_dstm):
|
|
nn.tf_sess.run ([src_D_src_dst_loss_gv_op], 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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|
self.D_src_dst_train = D_src_dst_train
|
|
|
|
if learn_mask:
|
|
def AE_view(warped_src, warped_dst):
|
|
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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|
else:
|
|
def AE_view(warped_src, warped_dst):
|
|
return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_src_dst],
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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:
|
|
# Initializing merge function
|
|
with tf.device( f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
|
|
if 'df' in archi:
|
|
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)
|
|
|
|
elif 'liae' in archi:
|
|
gpu_dst_code = self.encoder (self.warped_dst)
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|
gpu_dst_inter_B_code = self.inter_B (gpu_dst_code)
|
|
gpu_dst_inter_AB_code = self.inter_AB (gpu_dst_code)
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|
gpu_dst_code = tf.concat([gpu_dst_inter_B_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis)
|
|
gpu_src_dst_code = tf.concat([gpu_dst_inter_AB_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis)
|
|
|
|
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
|
|
_, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
|
|
|
|
if learn_mask:
|
|
def AE_merge( warped_dst):
|
|
return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst})
|
|
else:
|
|
def AE_merge( warped_dst):
|
|
return nn.tf_sess.run ( [gpu_pred_src_dst], feed_dict={self.warped_dst:warped_dst})
|
|
|
|
self.AE_merge = AE_merge
|
|
|
|
# Loading/initializing all models/optimizers weights
|
|
for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
|
|
do_init = self.is_first_run()
|
|
|
|
if self.pretrain_just_disabled:
|
|
if 'df' in archi:
|
|
if model == self.inter:
|
|
do_init = True
|
|
elif 'liae' in archi:
|
|
if model == self.inter_AB:
|
|
do_init = True
|
|
|
|
if not do_init:
|
|
do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
|
|
|
|
if do_init:
|
|
model.init_weights()
|
|
|
|
# initializing sample generators
|
|
if self.is_training:
|
|
t = SampleProcessor.Types
|
|
if self.options['face_type'] == 'h':
|
|
face_type = t.FACE_TYPE_HALF
|
|
elif self.options['face_type'] == 'mf':
|
|
face_type = t.FACE_TYPE_MID_FULL
|
|
elif self.options['face_type'] == 'f':
|
|
face_type = t.FACE_TYPE_FULL
|
|
|
|
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
|
|
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path()
|
|
|
|
random_ct_samples_path=training_data_dst_path if self.options['ct_mode'] != 'none' and not self.pretrain else None
|
|
|
|
t_img_warped = t.IMG_WARPED_TRANSFORMED if self.options['random_warp'] else t.IMG_TRANSFORMED
|
|
|
|
cpu_count = min(multiprocessing.cpu_count(), 8)
|
|
src_generators_count = cpu_count // 2
|
|
dst_generators_count = cpu_count // 2
|
|
if self.options['ct_mode'] != 'none':
|
|
src_generators_count = int(src_generators_count * 1.5)
|
|
|
|
self.set_training_data_generators ([
|
|
SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
|
|
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
|
|
output_sample_types = [ {'types' : (t_img_warped, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution': resolution, 'ct_mode': self.options['ct_mode'] },
|
|
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution': resolution, 'ct_mode': self.options['ct_mode'] },
|
|
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'data_format':nn.data_format, 'resolution': resolution } ],
|
|
generators_count=src_generators_count ),
|
|
|
|
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
|
|
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
|
|
output_sample_types = [ {'types' : (t_img_warped, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution': resolution},
|
|
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution': resolution},
|
|
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'data_format':nn.data_format, 'resolution': resolution} ],
|
|
generators_count=dst_generators_count )
|
|
])
|
|
|
|
#override
|
|
def get_model_filename_list(self):
|
|
return self.model_filename_list
|
|
|
|
#override
|
|
def onSave(self):
|
|
for model, filename in io.progress_bar_generator(self.get_model_filename_list(), "Saving", leave=False):
|
|
model.save_weights ( self.get_strpath_storage_for_file(filename) )
|
|
|
|
|
|
#override
|
|
def onTrainOneIter(self):
|
|
( (warped_src, target_src, target_srcm), \
|
|
(warped_dst, target_dst, target_dstm) ) = self.generate_next_samples()
|
|
|
|
src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, warped_dst, target_dst, target_dstm)
|
|
|
|
if self.options['true_face_power'] != 0 and not self.pretrain:
|
|
self.D_train (warped_src, warped_dst)
|
|
|
|
if self.gan_power != 0:
|
|
self.D_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
|
|
|
|
if self.options['learn_mask']:
|
|
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] ]
|
|
else:
|
|
S, D, SS, DD, SD, = [ 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) ) ]
|
|
|
|
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(), 800 // self.resolution )
|
|
|
|
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 += [ ('SAEHD', np.concatenate (st, axis=0 )), ]
|
|
|
|
if self.options['learn_mask']:
|
|
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 += [ ('SAEHD 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")
|
|
|
|
if self.options['learn_mask']:
|
|
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]
|
|
else:
|
|
bgr, = [ nn.to_data_format(x,"NHWC", self.model_data_format).astype(np.float32) for x in self.AE_merge (face) ]
|
|
return bgr[0]
|
|
|
|
#override
|
|
def get_MergerConfig(self):
|
|
if self.options['face_type'] == 'h':
|
|
face_type = FaceType.HALF
|
|
elif self.options['face_type'] == 'mf':
|
|
face_type = FaceType.MID_FULL
|
|
elif self.options['face_type'] == 'f':
|
|
face_type = FaceType.FULL
|
|
|
|
import merger
|
|
return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMasked(face_type=face_type,
|
|
default_mode = 'overlay' if self.options['ct_mode'] != 'none' or self.options['face_style_power'] or self.options['bg_style_power'] else 'seamless',
|
|
clip_hborder_mask_per=0.0625 if (face_type != FaceType.HALF) else 0,
|
|
)
|
|
|
|
Model = SAEHDModel
|