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https://github.com/iperov/DeepFaceLab.git
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803 lines
50 KiB
Python
803 lines
50 KiB
Python
import multiprocessing
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import operator
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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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from core.cv2ex import *
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class AMPModel(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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min_res = 64
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max_res = 640
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default_resolution = self.options['resolution'] = self.load_or_def_option('resolution', 224)
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default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'wf')
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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_ae_dims = self.options['ae_dims'] = self.load_or_def_option('ae_dims', 256)
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inter_dims = self.load_or_def_option('inter_dims', None)
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if inter_dims is None:
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inter_dims = self.options['ae_dims']
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default_inter_dims = self.options['inter_dims'] = inter_dims
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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 = self.options['d_dims'] = self.options.get('d_dims', None)
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default_d_mask_dims = self.options['d_mask_dims'] = self.options.get('d_mask_dims', None)
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default_morph_factor = self.options['morph_factor'] = self.options.get('morph_factor', 0.33)
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default_masked_training = self.options['masked_training'] = self.load_or_def_option('masked_training', True)
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default_eyes_mouth_prio = self.options['eyes_mouth_prio'] = self.load_or_def_option('eyes_mouth_prio', True)
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default_uniform_yaw = self.options['uniform_yaw'] = self.load_or_def_option('uniform_yaw', False)
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lr_dropout = self.load_or_def_option('lr_dropout', 'n')
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lr_dropout = {True:'y', False:'n'}.get(lr_dropout, lr_dropout) #backward comp
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default_lr_dropout = self.options['lr_dropout'] = lr_dropout
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default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
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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_autobackup_hour()
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self.ask_write_preview_history()
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self.ask_target_iter()
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self.ask_random_src_flip()
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self.ask_random_dst_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="64-640", help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 32 .")
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resolution = np.clip ( (resolution // 32) * 32, min_res, max_res)
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self.options['resolution'] = resolution
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self.options['face_type'] = io.input_str ("Face type", default_face_type, ['wf','head'], help_message="whole face / head").lower()
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default_d_dims = self.options['d_dims'] = self.load_or_def_option('d_dims', 64)
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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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if self.is_first_run():
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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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self.options['inter_dims'] = np.clip ( io.input_int("Inter dimensions", default_inter_dims, add_info="32-2048", help_message="Should be equal or more than AutoEncoder dimensions. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 2048 )
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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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morph_factor = np.clip ( io.input_number ("Morph factor.", default_morph_factor, add_info="0.1 .. 0.5", help_message="The smaller the value, the more src-like facial expressions will appear. The larger the value, the less space there is to train a large dst faceset in the neural network. Typical fine value is 0.33"), 0.1, 0.5 )
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self.options['morph_factor'] = morph_factor
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if self.is_first_run() or ask_override:
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if self.options['face_type'] == 'wf' or self.options['face_type'] == 'head':
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self.options['masked_training'] = io.input_bool ("Masked training", default_masked_training, help_message="This option is available only for 'whole_face' or 'head' type. Masked training clips training area to full_face mask or XSeg mask, thus network will train the faces properly.")
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self.options['eyes_mouth_prio'] = io.input_bool ("Eyes and mouth priority", default_eyes_mouth_prio, help_message='Helps to fix eye problems during training like "alien eyes" and wrong eyes direction. Also makes the detail of the teeth higher.')
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self.options['uniform_yaw'] = io.input_bool ("Uniform yaw distribution of samples", default_uniform_yaw, help_message='Helps to fix blurry side faces due to small amount of them in the faceset.')
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default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
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default_gan_patch_size = self.options['gan_patch_size'] = self.load_or_def_option('gan_patch_size', self.options['resolution'] // 8)
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default_gan_dims = self.options['gan_dims'] = self.load_or_def_option('gan_dims', 16)
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if self.is_first_run() or ask_override:
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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['lr_dropout'] = io.input_str (f"Use learning rate dropout", default_lr_dropout, ['n','y','cpu'], help_message="When the face is trained enough, you can enable this option to get extra sharpness and reduce subpixel shake for less amount of iterations. Enabled it before `disable random warp` and before GAN. \nn - disabled.\ny - enabled\ncpu - enabled on CPU. This allows not to use extra VRAM, sacrificing 20% time of iteration.")
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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 and reduce subpixel shake 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 .. 1.0", help_message="Forces the neural network to learn small details of the face. Enable it only when the face is trained enough with lr_dropout(on) and random_warp(off), and don't disable. The higher the value, the higher the chances of artifacts. Typical fine value is 0.1"), 0.0, 1.0 )
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if self.options['gan_power'] != 0.0:
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gan_patch_size = np.clip ( io.input_int("GAN patch size", default_gan_patch_size, add_info="3-640", help_message="The higher patch size, the higher the quality, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is resolution / 8." ), 3, 640 )
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self.options['gan_patch_size'] = gan_patch_size
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gan_dims = np.clip ( io.input_int("GAN dimensions", default_gan_dims, add_info="4-64", help_message="The dimensions of the GAN network. The higher dimensions, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is 16." ), 4, 64 )
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self.options['gan_dims'] = gan_dims
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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. Forces random_warp=N, random_flips=Y, gan_power=0.0, lr_dropout=N, uniform_yaw=Y")
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self.gan_model_changed = (default_gan_patch_size != self.options['gan_patch_size']) or (default_gan_dims != self.options['gan_dims'])
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self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)
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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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devices = device_config.devices
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self.model_data_format = "NCHW"
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nn.initialize(data_format=self.model_data_format)
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tf = nn.tf
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self.resolution = resolution = self.options['resolution']
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input_ch=3
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ae_dims = self.ae_dims = self.options['ae_dims']
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inter_dims = self.inter_dims = self.options['inter_dims']
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e_dims = self.options['e_dims']
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d_dims = self.options['d_dims']
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d_mask_dims = self.options['d_mask_dims']
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lowest_dense_res = self.lowest_dense_res = resolution // 32
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class Downscale(nn.ModelBase):
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def on_build(self, in_ch, out_ch, kernel_size=5 ):
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self.conv1 = nn.Conv2D( in_ch, out_ch, kernel_size=kernel_size, strides=2, padding='SAME')
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def forward(self, x):
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return tf.nn.leaky_relu(self.conv1(x), 0.1)
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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')
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def forward(self, x):
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x = nn.depth_to_space(tf.nn.leaky_relu(self.conv1(x), 0.1), 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')
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self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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def forward(self, inp):
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x = self.conv1(inp)
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x = tf.nn.leaky_relu(x, 0.2)
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x = self.conv2(x)
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x = tf.nn.leaky_relu(inp+x, 0.2)
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return x
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class Encoder(nn.ModelBase):
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def on_build(self):
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self.down1 = Downscale(input_ch, e_dims, kernel_size=5)
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self.res1 = ResidualBlock(e_dims)
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self.down2 = Downscale(e_dims, e_dims*2, kernel_size=5)
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self.down3 = Downscale(e_dims*2, e_dims*4, kernel_size=5)
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self.down4 = Downscale(e_dims*4, e_dims*8, kernel_size=5)
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self.down5 = Downscale(e_dims*8, e_dims*8, kernel_size=5)
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self.res5 = ResidualBlock(e_dims*8)
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self.dense1 = nn.Dense( lowest_dense_res*lowest_dense_res*e_dims*8, ae_dims )
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def forward(self, inp):
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x = inp
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x = self.down1(x)
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x = self.res1(x)
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x = self.down2(x)
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x = self.down3(x)
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x = self.down4(x)
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x = self.down5(x)
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x = self.res5(x)
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x = nn.pixel_norm(nn.flatten(x), axes=-1)
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x = self.dense1(x)
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return x
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class Inter(nn.ModelBase):
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def on_build(self):
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self.dense2 = nn.Dense( ae_dims, lowest_dense_res * lowest_dense_res * inter_dims )
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def forward(self, inp):
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x = inp
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x = self.dense2(x)
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x = nn.reshape_4D (x, lowest_dense_res, lowest_dense_res, inter_dims)
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return x
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class Decoder(nn.ModelBase):
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def on_build(self ):
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self.upscale0 = Upscale(inter_dims, d_dims*8, kernel_size=3)
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self.upscale1 = Upscale(d_dims*8, d_dims*8, kernel_size=3)
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self.upscale2 = Upscale(d_dims*8, d_dims*4, kernel_size=3)
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self.upscale3 = Upscale(d_dims*4, d_dims*2, kernel_size=3)
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self.res0 = ResidualBlock(d_dims*8, kernel_size=3)
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self.res1 = ResidualBlock(d_dims*8, kernel_size=3)
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self.res2 = ResidualBlock(d_dims*4, kernel_size=3)
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self.res3 = ResidualBlock(d_dims*2, kernel_size=3)
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self.upscalem0 = Upscale(inter_dims, d_mask_dims*8, kernel_size=3)
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self.upscalem1 = Upscale(d_mask_dims*8, d_mask_dims*8, kernel_size=3)
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self.upscalem2 = Upscale(d_mask_dims*8, d_mask_dims*4, kernel_size=3)
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self.upscalem3 = Upscale(d_mask_dims*4, d_mask_dims*2, kernel_size=3)
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self.upscalem4 = Upscale(d_mask_dims*2, d_mask_dims*1, kernel_size=3)
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self.out_convm = nn.Conv2D( d_mask_dims*1, 1, kernel_size=1, padding='SAME')
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self.out_conv = nn.Conv2D( d_dims*2, 3, kernel_size=1, padding='SAME')
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self.out_conv1 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME')
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self.out_conv2 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME')
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self.out_conv3 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME')
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def forward(self, inp):
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z = inp
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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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x = self.upscale3(x)
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x = self.res3(x)
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x = tf.nn.sigmoid( nn.depth_to_space(tf.concat( (self.out_conv(x),
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self.out_conv1(x),
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self.out_conv2(x),
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self.out_conv3(x)), nn.conv2d_ch_axis), 2) )
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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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m = self.upscalem3(m)
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m = self.upscalem4(m)
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m = tf.nn.sigmoid(self.out_convm(m))
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return x, m
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self.face_type = {'wf' : FaceType.WHOLE_FACE,
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'head' : FaceType.HEAD}[ self.options['face_type'] ]
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if 'eyes_prio' in self.options:
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self.options.pop('eyes_prio')
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eyes_mouth_prio = self.options['eyes_mouth_prio']
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morph_factor = self.options['morph_factor']
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pretrain = self.pretrain = self.options['pretrain']
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if self.pretrain_just_disabled:
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self.set_iter(0)
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self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
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random_warp = False if self.pretrain else self.options['random_warp']
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random_src_flip = self.random_src_flip if not self.pretrain else True
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random_dst_flip = self.random_dst_flip if not self.pretrain else True
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if self.pretrain:
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self.options_show_override['gan_power'] = 0.0
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self.options_show_override['random_warp'] = False
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self.options_show_override['lr_dropout'] = 'n'
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self.options_show_override['uniform_yaw'] = True
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masked_training = self.options['masked_training']
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ct_mode = self.options['ct_mode']
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if ct_mode == 'none':
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ct_mode = None
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models_opt_on_gpu = False if len(devices) == 0 else self.options['models_opt_on_gpu']
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models_opt_device = nn.tf_default_device_name 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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bgr_shape = self.bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
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mask_shape = nn.get4Dshape(resolution,resolution,1)
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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.floatx, bgr_shape, name='warped_src')
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self.warped_dst = tf.placeholder (nn.floatx, bgr_shape, name='warped_dst')
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self.target_src = tf.placeholder (nn.floatx, bgr_shape, name='target_src')
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self.target_dst = tf.placeholder (nn.floatx, bgr_shape, name='target_dst')
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self.target_srcm = tf.placeholder (nn.floatx, mask_shape, name='target_srcm')
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self.target_srcm_em = tf.placeholder (nn.floatx, mask_shape, name='target_srcm_em')
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self.target_dstm = tf.placeholder (nn.floatx, mask_shape, name='target_dstm')
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self.target_dstm_em = tf.placeholder (nn.floatx, mask_shape, name='target_dstm_em')
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self.morph_value_t = tf.placeholder (nn.floatx, (1,), name='morph_value_t')
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# Initializing model classes
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with tf.device (models_opt_device):
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self.encoder = Encoder(name='encoder')
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self.inter_src = Inter(name='inter_src')
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self.inter_dst = Inter(name='inter_dst')
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self.decoder = Decoder(name='decoder')
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self.model_filename_list += [ [self.encoder, 'encoder.npy'],
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[self.inter_src, 'inter_src.npy'],
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[self.inter_dst , 'inter_dst.npy'],
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[self.decoder , 'decoder.npy'] ]
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|
if self.is_training:
|
|
if gan_power != 0:
|
|
self.GAN = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="GAN")
|
|
self.model_filename_list += [ [self.GAN, 'GAN.npy'] ]
|
|
|
|
# Initialize optimizers
|
|
lr=5e-5
|
|
lr_dropout = 0.3 if self.options['lr_dropout'] in ['y','cpu'] and not self.pretrain else 1.0
|
|
|
|
clipnorm = 1.0 if self.options['clipgrad'] else 0.0
|
|
|
|
self.all_weights = self.encoder.get_weights() + self.inter_src.get_weights() + self.inter_dst.get_weights() + self.decoder.get_weights()
|
|
if pretrain:
|
|
self.trainable_weights = self.encoder.get_weights() + self.inter_dst.get_weights() + self.decoder.get_weights()
|
|
else:
|
|
self.trainable_weights = self.encoder.get_weights() + self.inter_src.get_weights() + self.inter_dst.get_weights() + self.decoder.get_weights()
|
|
|
|
self.src_dst_opt = nn.AdaBelief(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
|
|
self.src_dst_opt.initialize_variables (self.all_weights, vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')
|
|
self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
|
|
|
|
if gan_power != 0:
|
|
self.GAN_opt = nn.AdaBelief(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='GAN_opt')
|
|
self.GAN_opt.initialize_variables ( self.GAN.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')#+self.D_src_x2.get_weights()
|
|
self.model_filename_list += [ (self.GAN_opt, 'GAN_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_src_dst_loss_gvs = []
|
|
|
|
for gpu_id in range(gpu_count):
|
|
with tf.device( f'/{devices[gpu_id].tf_dev_type}:{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_srcm_em = self.target_srcm_em[batch_slice,:,:,:]
|
|
gpu_target_dstm = self.target_dstm[batch_slice,:,:,:]
|
|
gpu_target_dstm_em = self.target_dstm_em[batch_slice,:,:,:]
|
|
|
|
# process model tensors
|
|
gpu_src_code = self.encoder (gpu_warped_src)
|
|
gpu_dst_code = self.encoder (gpu_warped_dst)
|
|
|
|
if pretrain:
|
|
gpu_src_inter_src_code = self.inter_src (gpu_src_code)
|
|
gpu_dst_inter_dst_code = self.inter_dst (gpu_dst_code)
|
|
gpu_src_code = gpu_src_inter_src_code * nn.random_binomial( [bs_per_gpu, gpu_src_inter_src_code.shape.as_list()[1], 1,1] , p=morph_factor)
|
|
gpu_dst_code = gpu_src_dst_code = gpu_dst_inter_dst_code * nn.random_binomial( [bs_per_gpu, gpu_dst_inter_dst_code.shape.as_list()[1], 1,1] , p=0.25)
|
|
else:
|
|
gpu_src_inter_src_code = self.inter_src (gpu_src_code)
|
|
gpu_src_inter_dst_code = self.inter_dst (gpu_src_code)
|
|
gpu_dst_inter_src_code = self.inter_src (gpu_dst_code)
|
|
gpu_dst_inter_dst_code = self.inter_dst (gpu_dst_code)
|
|
|
|
inter_rnd_binomial = nn.random_binomial( [bs_per_gpu, gpu_src_inter_src_code.shape.as_list()[1], 1,1] , p=morph_factor)
|
|
gpu_src_code = gpu_src_inter_src_code * inter_rnd_binomial + gpu_src_inter_dst_code * (1-inter_rnd_binomial)
|
|
gpu_dst_code = gpu_dst_inter_dst_code
|
|
|
|
inter_dims_slice = tf.cast(inter_dims*self.morph_value_t[0], tf.int32)
|
|
gpu_src_dst_code = tf.concat( (tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, inter_dims_slice , lowest_dense_res, lowest_dense_res]),
|
|
tf.slice(gpu_dst_inter_dst_code, [0,inter_dims_slice,0,0], [-1,inter_dims-inter_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
|
|
|
|
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.gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
|
|
gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_blur, 0, 0.5) * 2
|
|
|
|
gpu_target_dstm_blur = nn.gaussian_blur(gpu_target_dstm, max(1, resolution // 32) )
|
|
gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
|
|
|
|
gpu_target_dst_anti_masked = gpu_target_dst*(1.0-gpu_target_dstm_blur)
|
|
gpu_target_src_anti_masked = gpu_target_src*(1.0-gpu_target_srcm_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*gpu_target_dstm_blur 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_src_src_anti_masked = gpu_pred_src_src*(1.0-gpu_target_srcm_blur)
|
|
gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
|
|
gpu_pred_dst_dst_anti_masked = gpu_pred_dst_dst*(1.0-gpu_target_dstm_blur)
|
|
|
|
if resolution < 256:
|
|
gpu_dst_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
|
|
else:
|
|
gpu_dst_loss = tf.reduce_mean ( 5*nn.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 ( 5*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/23.2) ), 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 eyes_mouth_prio:
|
|
gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_em - gpu_pred_dst_dst*gpu_target_dstm_em ), axis=[1,2,3])
|
|
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
|
|
gpu_dst_loss += 0.1*tf.reduce_mean(tf.square(gpu_pred_dst_dst_anti_masked-gpu_target_dst_anti_masked),axis=[1,2,3] )
|
|
gpu_dst_losses += [gpu_dst_loss]
|
|
|
|
if not pretrain:
|
|
if resolution < 256:
|
|
gpu_src_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
|
|
else:
|
|
gpu_src_loss = tf.reduce_mean ( 5*nn.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 ( 5*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/23.2)), 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 eyes_mouth_prio:
|
|
gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_srcm_em - gpu_pred_src_src*gpu_target_srcm_em ), axis=[1,2,3])
|
|
|
|
gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
|
|
else:
|
|
gpu_src_loss = gpu_dst_loss
|
|
|
|
gpu_src_losses += [gpu_src_loss]
|
|
|
|
if pretrain:
|
|
gpu_G_loss = gpu_dst_loss
|
|
else:
|
|
gpu_G_loss = gpu_src_loss + gpu_dst_loss
|
|
|
|
def DLossOnes(logits):
|
|
return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(logits), logits=logits), axis=[1,2,3])
|
|
|
|
def DLossZeros(logits):
|
|
return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(logits), logits=logits), axis=[1,2,3])
|
|
|
|
if gan_power != 0:
|
|
gpu_pred_src_src_d, gpu_pred_src_src_d2 = self.GAN(gpu_pred_src_src_masked_opt)
|
|
gpu_pred_dst_dst_d, gpu_pred_dst_dst_d2 = self.GAN(gpu_pred_dst_dst_masked_opt)
|
|
gpu_target_src_d, gpu_target_src_d2 = self.GAN(gpu_target_src_masked_opt)
|
|
gpu_target_dst_d, gpu_target_dst_d2 = self.GAN(gpu_target_dst_masked_opt)
|
|
|
|
gpu_D_src_dst_loss = (DLossOnes (gpu_target_src_d) + DLossOnes (gpu_target_src_d2) + \
|
|
DLossZeros(gpu_pred_src_src_d) + DLossZeros(gpu_pred_src_src_d2) + \
|
|
DLossOnes (gpu_target_dst_d) + DLossOnes (gpu_target_dst_d2) + \
|
|
DLossZeros(gpu_pred_dst_dst_d) + DLossZeros(gpu_pred_dst_dst_d2)
|
|
) * ( 1.0 / 8)
|
|
|
|
gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.GAN.get_weights() ) ]
|
|
|
|
gpu_G_loss += (DLossOnes(gpu_pred_src_src_d) + DLossOnes(gpu_pred_src_src_d2) + \
|
|
DLossOnes(gpu_pred_dst_dst_d) + DLossOnes(gpu_pred_dst_dst_d2)
|
|
) * gan_power
|
|
|
|
if masked_training:
|
|
# Minimal src-src-bg rec with total_variation_mse to suppress random bright dots from gan
|
|
gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_src_src)
|
|
gpu_G_loss += 0.02*tf.reduce_mean(tf.square(gpu_pred_src_src_anti_masked-gpu_target_src_anti_masked),axis=[1,2,3] )
|
|
|
|
gpu_G_loss_gvs += [ nn.gradients ( gpu_G_loss, self.trainable_weights ) ]
|
|
|
|
|
|
# Average losses and gradients, and create optimizer update ops
|
|
with tf.device(f'/CPU:0'):
|
|
pred_src_src = nn.concat(gpu_pred_src_src_list, 0)
|
|
pred_dst_dst = nn.concat(gpu_pred_dst_dst_list, 0)
|
|
pred_src_dst = nn.concat(gpu_pred_src_dst_list, 0)
|
|
pred_src_srcm = nn.concat(gpu_pred_src_srcm_list, 0)
|
|
pred_dst_dstm = nn.concat(gpu_pred_dst_dstm_list, 0)
|
|
pred_src_dstm = nn.concat(gpu_pred_src_dstm_list, 0)
|
|
|
|
with tf.device (models_opt_device):
|
|
src_loss = tf.concat(gpu_src_losses, 0)
|
|
dst_loss = tf.concat(gpu_dst_losses, 0)
|
|
src_dst_loss_gv_op = self.src_dst_opt.get_update_op (nn.average_gv_list (gpu_G_loss_gvs))
|
|
|
|
if gan_power != 0:
|
|
src_D_src_dst_loss_gv_op = self.GAN_opt.get_update_op (nn.average_gv_list(gpu_D_src_dst_loss_gvs) )
|
|
|
|
# Initializing training and view functions
|
|
def src_dst_train(warped_src, target_src, target_srcm, target_srcm_em, \
|
|
warped_dst, target_dst, target_dstm, target_dstm_em, ):
|
|
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.target_srcm_em:target_srcm_em,
|
|
self.warped_dst :warped_dst,
|
|
self.target_dst :target_dst,
|
|
self.target_dstm:target_dstm,
|
|
self.target_dstm_em:target_dstm_em,
|
|
})
|
|
return s, d
|
|
self.src_dst_train = src_dst_train
|
|
|
|
if gan_power != 0:
|
|
def D_src_dst_train(warped_src, target_src, target_srcm, target_srcm_em, \
|
|
warped_dst, target_dst, target_dstm, target_dstm_em, ):
|
|
nn.tf_sess.run ([src_D_src_dst_loss_gv_op], feed_dict={self.warped_src :warped_src,
|
|
self.target_src :target_src,
|
|
self.target_srcm:target_srcm,
|
|
self.target_srcm_em:target_srcm_em,
|
|
self.warped_dst :warped_dst,
|
|
self.target_dst :target_dst,
|
|
self.target_dstm:target_dstm,
|
|
self.target_dstm_em:target_dstm_em})
|
|
self.D_src_dst_train = D_src_dst_train
|
|
|
|
def AE_view(warped_src, warped_dst, morph_value):
|
|
return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
|
|
feed_dict={self.warped_src:warped_src, self.warped_dst:warped_dst, self.morph_value_t:[morph_value] })
|
|
|
|
self.AE_view = AE_view
|
|
else:
|
|
#Initializing merge function
|
|
with tf.device( nn.tf_default_device_name if len(devices) != 0 else f'/CPU:0'):
|
|
gpu_dst_code = self.encoder (self.warped_dst)
|
|
gpu_dst_inter_src_code = self.inter_src ( gpu_dst_code)
|
|
gpu_dst_inter_dst_code = self.inter_dst ( gpu_dst_code)
|
|
|
|
inter_dims_slice = tf.cast(inter_dims*self.morph_value_t[0], tf.int32)
|
|
gpu_src_dst_code = tf.concat( ( tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, inter_dims_slice , lowest_dense_res, lowest_dense_res]),
|
|
tf.slice(gpu_dst_inter_dst_code, [0,inter_dims_slice,0,0], [-1,inter_dims-inter_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
|
|
|
|
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
|
|
_, gpu_pred_dst_dstm = self.decoder(gpu_dst_inter_dst_code)
|
|
|
|
def AE_merge(warped_dst, morph_value):
|
|
return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst, self.morph_value_t:[morph_value] })
|
|
|
|
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"):
|
|
if self.pretrain_just_disabled:
|
|
do_init = False
|
|
if model == self.inter_src or model == self.inter_dst:
|
|
do_init = True
|
|
else:
|
|
do_init = self.is_first_run()
|
|
if self.is_training and gan_power != 0 and model == self.GAN:
|
|
if self.gan_model_changed:
|
|
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:
|
|
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 ct_mode is not None and not self.pretrain else None
|
|
|
|
cpu_count = min(multiprocessing.cpu_count(), 8)
|
|
src_generators_count = cpu_count // 2
|
|
dst_generators_count = cpu_count // 2
|
|
if ct_mode is not 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=random_src_flip),
|
|
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
|
{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
|
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
|
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
|
],
|
|
uniform_yaw_distribution=self.options['uniform_yaw'] or self.pretrain,
|
|
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=random_dst_flip),
|
|
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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uniform_yaw_distribution=self.options['uniform_yaw'] or self.pretrain,
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generators_count=dst_generators_count )
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|
])
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|
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self.last_src_samples_loss = []
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self.last_dst_samples_loss = []
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if self.pretrain_just_disabled:
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self.update_sample_for_preview(force_new=True)
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|
|
|
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def export_dfm (self):
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output_path=self.get_strpath_storage_for_file('model.dfm')
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|
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io.log_info(f'Dumping .dfm to {output_path}')
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|
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tf = nn.tf
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with tf.device (nn.tf_default_device_name):
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warped_dst = tf.placeholder (nn.floatx, (None, self.resolution, self.resolution, 3), name='in_face')
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warped_dst = tf.transpose(warped_dst, (0,3,1,2))
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morph_value = tf.placeholder (nn.floatx, (1,), name='morph_value')
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|
|
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gpu_dst_code = self.encoder (warped_dst)
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gpu_dst_inter_src_code = self.inter_src ( gpu_dst_code)
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gpu_dst_inter_dst_code = self.inter_dst ( gpu_dst_code)
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|
|
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inter_dims_slice = tf.cast(self.inter_dims*morph_value[0], tf.int32)
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gpu_src_dst_code = tf.concat( (tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, inter_dims_slice , self.inter_res, self.inter_res]),
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|
tf.slice(gpu_dst_inter_dst_code, [0,inter_dims_slice,0,0], [-1,self.inter_dims-inter_dims_slice, self.inter_res,self.inter_res]) ), 1 )
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|
|
|
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
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|
_, gpu_pred_dst_dstm = self.decoder(gpu_dst_inter_dst_code)
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|
|
|
gpu_pred_src_dst = tf.transpose(gpu_pred_src_dst, (0,2,3,1))
|
|
gpu_pred_dst_dstm = tf.transpose(gpu_pred_dst_dstm, (0,2,3,1))
|
|
gpu_pred_src_dstm = tf.transpose(gpu_pred_src_dstm, (0,2,3,1))
|
|
|
|
tf.identity(gpu_pred_dst_dstm, name='out_face_mask')
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|
tf.identity(gpu_pred_src_dst, name='out_celeb_face')
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|
tf.identity(gpu_pred_src_dstm, name='out_celeb_face_mask')
|
|
|
|
output_graph_def = tf.graph_util.convert_variables_to_constants(
|
|
nn.tf_sess,
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|
tf.get_default_graph().as_graph_def(),
|
|
['out_face_mask','out_celeb_face','out_celeb_face_mask']
|
|
)
|
|
|
|
import tf2onnx
|
|
with tf.device("/CPU:0"):
|
|
model_proto, _ = tf2onnx.convert._convert_common(
|
|
output_graph_def,
|
|
name='AMP',
|
|
input_names=['in_face:0','morph_value:0'],
|
|
output_names=['out_face_mask:0','out_celeb_face:0','out_celeb_face_mask:0'],
|
|
opset=13,
|
|
output_path=output_path)
|
|
|
|
#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 should_save_preview_history(self):
|
|
return (not io.is_colab() and self.iter % ( 10*(max(1,self.resolution // 64)) ) == 0) or \
|
|
(io.is_colab() and self.iter % 100 == 0)
|
|
|
|
#override
|
|
def onTrainOneIter(self):
|
|
bs = self.get_batch_size()
|
|
|
|
( (warped_src, target_src, target_srcm, target_srcm_em), \
|
|
(warped_dst, target_dst, target_dstm, target_dstm_em) ) = self.generate_next_samples()
|
|
|
|
src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
|
|
|
|
for i in range(bs):
|
|
self.last_src_samples_loss.append ( (src_loss[i], warped_src[i], target_src[i], target_srcm[i], target_srcm_em[i]) )
|
|
self.last_dst_samples_loss.append ( (dst_loss[i], warped_dst[i], target_dst[i], target_dstm[i], target_dstm_em[i]) )
|
|
|
|
if len(self.last_src_samples_loss) >= bs*16:
|
|
src_samples_loss = sorted(self.last_src_samples_loss, key=operator.itemgetter(0), reverse=True)
|
|
dst_samples_loss = sorted(self.last_dst_samples_loss, key=operator.itemgetter(0), reverse=True)
|
|
|
|
warped_src = np.stack( [ x[1] for x in src_samples_loss[:bs] ] )
|
|
target_src = np.stack( [ x[2] for x in src_samples_loss[:bs] ] )
|
|
target_srcm = np.stack( [ x[3] for x in src_samples_loss[:bs] ] )
|
|
target_srcm_em = np.stack( [ x[4] for x in src_samples_loss[:bs] ] )
|
|
|
|
warped_dst = np.stack( [ x[1] for x in dst_samples_loss[:bs] ] )
|
|
target_dst = np.stack( [ x[2] for x in dst_samples_loss[:bs] ] )
|
|
target_dstm = np.stack( [ x[3] for x in dst_samples_loss[:bs] ] )
|
|
target_dstm_em = np.stack( [ x[4] for x in dst_samples_loss[:bs] ] )
|
|
|
|
src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
|
|
self.last_src_samples_loss = []
|
|
self.last_dst_samples_loss = []
|
|
|
|
if self.gan_power != 0:
|
|
self.D_src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
|
|
|
|
return ( ('src_loss', np.mean(src_loss) ), ('dst_loss', np.mean(dst_loss) ), )
|
|
|
|
#override
|
|
def onGetPreview(self, samples, for_history=False):
|
|
( (warped_src, target_src, target_srcm, target_srcm_em),
|
|
(warped_dst, target_dst, target_dstm, target_dstm_em) ) = samples
|
|
|
|
S, D, SS, DD, DDM_000, _, _ = [ 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, 0.0) ) ]
|
|
|
|
_, _, DDM_025, SD_025, SDM_025 = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in self.AE_view (target_src, target_dst, 0.25) ]
|
|
_, _, DDM_050, SD_050, SDM_050 = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in self.AE_view (target_src, target_dst, 0.50) ]
|
|
_, _, DDM_065, SD_065, SDM_065 = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in self.AE_view (target_src, target_dst, 0.65) ]
|
|
_, _, DDM_075, SD_075, SDM_075 = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in self.AE_view (target_src, target_dst, 0.75) ]
|
|
_, _, DDM_100, SD_100, SDM_100 = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in self.AE_view (target_src, target_dst, 1.00) ]
|
|
|
|
(DDM_000,
|
|
DDM_025, SDM_025,
|
|
DDM_050, SDM_050,
|
|
DDM_065, SDM_065,
|
|
DDM_075, SDM_075,
|
|
DDM_100, SDM_100) = [ np.repeat (x, (3,), -1) for x in (DDM_000,
|
|
DDM_025, SDM_025,
|
|
DDM_050, SDM_050,
|
|
DDM_065, SDM_065,
|
|
DDM_075, SDM_075,
|
|
DDM_100, SDM_100) ]
|
|
|
|
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 = []
|
|
|
|
i = np.random.randint(n_samples) if not for_history else 0
|
|
|
|
st = [ np.concatenate ((S[i], D[i], DD[i]*DDM_000[i]), axis=1) ]
|
|
st += [ np.concatenate ((SS[i], DD[i], SD_075[i] ), axis=1) ]
|
|
|
|
result += [ ('AMP morph 0.75', np.concatenate (st, axis=0 )), ]
|
|
|
|
st = [ np.concatenate ((DD[i], SD_025[i], SD_050[i]), axis=1) ]
|
|
st += [ np.concatenate ((SD_065[i], SD_075[i], SD_100[i]), axis=1) ]
|
|
result += [ ('AMP morph list', np.concatenate (st, axis=0 )), ]
|
|
|
|
st = [ np.concatenate ((DD[i], SD_025[i]*DDM_025[i]*SDM_025[i], SD_050[i]*DDM_050[i]*SDM_050[i]), axis=1) ]
|
|
st += [ np.concatenate ((SD_065[i]*DDM_065[i]*SDM_065[i], SD_075[i]*DDM_075[i]*SDM_075[i], SD_100[i]*DDM_100[i]*SDM_100[i]), axis=1) ]
|
|
result += [ ('AMP morph list masked', np.concatenate (st, axis=0 )), ]
|
|
|
|
return result
|
|
|
|
def predictor_func (self, face, morph_value):
|
|
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, morph_value) ]
|
|
|
|
return bgr[0], mask_src_dstm[0][...,0], mask_dst_dstm[0][...,0]
|
|
|
|
#override
|
|
def get_MergerConfig(self):
|
|
morph_factor = np.clip ( io.input_number ("Morph factor", 0.75, add_info="0.0 .. 1.0"), 0.0, 1.0 )
|
|
|
|
def predictor_morph(face):
|
|
return self.predictor_func(face, morph_factor)
|
|
|
|
|
|
import merger
|
|
return predictor_morph, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMasked(face_type=self.face_type, default_mode = 'overlay')
|
|
|
|
Model = AMPModel
|