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726 lines
45 KiB
Python
726 lines
45 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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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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default_inter_dims = self.options['inter_dims'] = self.load_or_def_option('inter_dims', 1024)
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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', 0.5)
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default_uniform_yaw = self.options['uniform_yaw'] = self.load_or_def_option('uniform_yaw', False)
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default_blur_out_mask = self.options['blur_out_mask'] = self.load_or_def_option('blur_out_mask', False)
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default_lr_dropout = self.options['lr_dropout'] = self.load_or_def_option('lr_dropout', 'n')
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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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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(8)
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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, 64, 640)
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self.options['resolution'] = resolution
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self.options['face_type'] = io.input_str ("Face type", default_face_type, ['f','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="Typical fine value is 0.5"), 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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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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self.options['blur_out_mask'] = io.input_bool ("Blur out mask", default_blur_out_mask, help_message='Blurs nearby area outside of applied face mask of training samples. The result is the background near the face is smoothed and less noticeable on swapped face. The exact xseg mask in src and dst faceset is required.')
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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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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['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 .. 5.0", help_message="Forces the neural network to learn small details of the face. Enable it only when the face is trained enough with 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, 5.0 )
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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="4-512", 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, 512 )
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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. If src faceset is deverse enough, then lct mode is fine in most cases.")
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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.gan_model_changed = (default_gan_patch_size != self.options['gan_patch_size']) or (default_gan_dims != self.options['gan_dims'])
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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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input_ch=3
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resolution = self.resolution = self.options['resolution']
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e_dims = self.options['e_dims']
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ae_dims = self.options['ae_dims']
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inter_dims = self.inter_dims = self.options['inter_dims']
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inter_res = self.inter_res = resolution // 32
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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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face_type = self.face_type = {'f' : FaceType.FULL,
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'wf' : FaceType.WHOLE_FACE,
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'head' : FaceType.HEAD}[ self.options['face_type'] ]
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morph_factor = self.options['morph_factor']
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gan_power = self.gan_power = self.options['gan_power']
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random_warp = self.options['random_warp']
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blur_out_mask = self.options['blur_out_mask']
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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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use_fp16 = False
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if self.is_exporting:
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use_fp16 = io.input_bool ("Export quantized?", False, help_message='Makes the exported model faster. If you have problems, disable this option.')
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conv_dtype = tf.float16 if use_fp16 else tf.float32
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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', dtype=conv_dtype)
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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', dtype=conv_dtype)
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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', dtype=conv_dtype)
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self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
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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( (( resolution//(2**5) )**2) * e_dims*8, ae_dims )
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def forward(self, x):
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if use_fp16:
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x = tf.cast(x, tf.float16)
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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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if use_fp16:
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x = tf.cast(x, tf.float32)
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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, inter_res * inter_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, inter_res, inter_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', dtype=conv_dtype)
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self.out_conv = nn.Conv2D( d_dims*2, 3, kernel_size=1, padding='SAME', dtype=conv_dtype)
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self.out_conv1 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
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self.out_conv2 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
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self.out_conv3 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
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def forward(self, z):
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if use_fp16:
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z = tf.cast(z, tf.float16)
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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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if use_fp16:
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x = tf.cast(x, tf.float32)
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m = tf.cast(m, tf.float32)
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return x, m
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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:
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# Initialize optimizers
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clipnorm = 1.0 if self.options['clipgrad'] else 0.0
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if self.options['lr_dropout'] in ['y','cpu']:
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lr_cos = 500
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lr_dropout = 0.3
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else:
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lr_cos = 0
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lr_dropout = 1.0
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self.G_weights = self.encoder.get_weights() + self.decoder.get_weights()
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self.src_dst_opt = nn.AdaBelief(lr=5e-5, lr_dropout=lr_dropout, lr_cos=lr_cos, clipnorm=clipnorm, name='src_dst_opt')
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self.src_dst_opt.initialize_variables (self.G_weights, vars_on_cpu=optimizer_vars_on_cpu)
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self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
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if gan_power != 0:
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self.GAN = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="GAN")
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self.GAN_opt = nn.AdaBelief(lr=5e-5, lr_dropout=lr_dropout, lr_cos=lr_cos, clipnorm=clipnorm, name='GAN_opt')
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self.GAN_opt.initialize_variables ( self.GAN.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
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self.model_filename_list += [ [self.GAN, 'GAN.npy'],
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[self.GAN_opt, 'GAN_opt.npy'] ]
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if self.is_training:
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# Adjust batch size for multiple GPU
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gpu_count = max(1, len(devices) )
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bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
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self.set_batch_size( gpu_count*bs_per_gpu)
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# Compute losses per GPU
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gpu_pred_src_src_list = []
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gpu_pred_dst_dst_list = []
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gpu_pred_src_dst_list = []
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gpu_pred_src_srcm_list = []
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gpu_pred_dst_dstm_list = []
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gpu_pred_src_dstm_list = []
|
|
|
|
gpu_src_losses = []
|
|
gpu_dst_losses = []
|
|
gpu_G_loss_gradients = []
|
|
gpu_GAN_loss_gradients = []
|
|
|
|
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])
|
|
|
|
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)
|
|
|
|
gpu_src_inter_src_code, gpu_src_inter_dst_code = self.inter_src (gpu_src_code), self.inter_dst (gpu_src_code)
|
|
gpu_dst_inter_src_code, gpu_dst_inter_dst_code = self.inter_src (gpu_dst_code), self.inter_dst (gpu_dst_code)
|
|
|
|
inter_dims_bin = int(inter_dims*morph_factor)
|
|
with tf.device(f'/CPU:0'):
|
|
inter_rnd_binomial = tf.stack([tf.random.shuffle(tf.concat([tf.tile(tf.constant([1], tf.float32), ( inter_dims_bin, )),
|
|
tf.tile(tf.constant([0], tf.float32), ( inter_dims-inter_dims_bin, ))], 0 )) for _ in range(bs_per_gpu)], 0)
|
|
|
|
inter_rnd_binomial = tf.stop_gradient(inter_rnd_binomial[...,None,None])
|
|
|
|
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 , inter_res, inter_res]),
|
|
tf.slice(gpu_dst_inter_dst_code, [0,inter_dims_slice,0,0], [-1,inter_dims-inter_dims_slice, inter_res,inter_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_src_srcm_list.append(gpu_pred_src_srcm)
|
|
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst), gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
|
|
gpu_pred_src_dst_list.append(gpu_pred_src_dst), gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
|
|
|
|
gpu_target_srcm_anti = 1-gpu_target_srcm
|
|
gpu_target_dstm_anti = 1-gpu_target_dstm
|
|
|
|
gpu_target_srcm_gblur = nn.gaussian_blur(gpu_target_srcm, resolution // 32)
|
|
gpu_target_dstm_gblur = nn.gaussian_blur(gpu_target_dstm, resolution // 32)
|
|
|
|
gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_gblur, 0, 0.5) * 2
|
|
gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_gblur, 0, 0.5) * 2
|
|
gpu_target_srcm_anti_blur = 1.0-gpu_target_srcm_blur
|
|
gpu_target_dstm_anti_blur = 1.0-gpu_target_dstm_blur
|
|
|
|
if blur_out_mask:
|
|
sigma = resolution / 128
|
|
|
|
x = nn.gaussian_blur(gpu_target_src*gpu_target_srcm_anti, sigma)
|
|
y = 1-nn.gaussian_blur(gpu_target_srcm, sigma)
|
|
y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
|
|
gpu_target_src = gpu_target_src*gpu_target_srcm + (x/y)*gpu_target_srcm_anti
|
|
|
|
x = nn.gaussian_blur(gpu_target_dst*gpu_target_dstm_anti, sigma)
|
|
y = 1-nn.gaussian_blur(gpu_target_dstm, sigma)
|
|
y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
|
|
gpu_target_dst = gpu_target_dst*gpu_target_dstm + (x/y)*gpu_target_dstm_anti
|
|
|
|
gpu_target_src_masked = gpu_target_src*gpu_target_srcm_blur
|
|
gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
|
|
gpu_target_src_anti_masked = gpu_target_src*gpu_target_srcm_anti_blur
|
|
gpu_target_dst_anti_masked = gpu_target_dst*gpu_target_dstm_anti_blur
|
|
|
|
gpu_pred_src_src_masked = gpu_pred_src_src*gpu_target_srcm_blur
|
|
gpu_pred_dst_dst_masked = gpu_pred_dst_dst*gpu_target_dstm_blur
|
|
gpu_pred_src_src_anti_masked = gpu_pred_src_src*gpu_target_srcm_anti_blur
|
|
gpu_pred_dst_dst_anti_masked = gpu_pred_dst_dst*gpu_target_dstm_anti_blur
|
|
|
|
# Structural loss
|
|
gpu_src_loss = tf.reduce_mean (5*nn.dssim(gpu_target_src_masked, gpu_pred_src_src_masked, 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, gpu_pred_src_src_masked, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
|
|
gpu_dst_loss = tf.reduce_mean (5*nn.dssim(gpu_target_dst_masked, gpu_pred_dst_dst_masked, 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, gpu_pred_dst_dst_masked, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
|
|
|
|
# Pixel loss
|
|
gpu_src_loss += tf.reduce_mean (10*tf.square(gpu_target_src_masked-gpu_pred_src_src_masked), axis=[1,2,3])
|
|
gpu_dst_loss += tf.reduce_mean (10*tf.square(gpu_target_dst_masked-gpu_pred_dst_dst_masked), axis=[1,2,3])
|
|
|
|
# Eyes+mouth prio loss
|
|
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_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])
|
|
|
|
# Mask loss
|
|
gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),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_src_losses += [gpu_src_loss]
|
|
gpu_dst_losses += [gpu_dst_loss]
|
|
gpu_G_loss = gpu_src_loss + gpu_dst_loss
|
|
# dst-dst background weak loss
|
|
gpu_G_loss += tf.reduce_mean(0.1*tf.square(gpu_pred_dst_dst_anti_masked-gpu_target_dst_anti_masked),axis=[1,2,3] )
|
|
gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_dst_dst_anti_masked)
|
|
|
|
|
|
if gan_power != 0:
|
|
gpu_pred_src_src_d, gpu_pred_src_src_d2 = self.GAN(gpu_pred_src_src_masked)
|
|
gpu_pred_dst_dst_d, gpu_pred_dst_dst_d2 = self.GAN(gpu_pred_dst_dst_masked)
|
|
gpu_target_src_d, gpu_target_src_d2 = self.GAN(gpu_target_src_masked)
|
|
gpu_target_dst_d, gpu_target_dst_d2 = self.GAN(gpu_target_dst_masked)
|
|
|
|
gpu_GAN_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_GAN_loss_gradients += [ nn.gradients (gpu_GAN_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
|
|
|
|
# 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_gradients += [ nn.gradients ( gpu_G_loss, self.G_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)
|
|
train_op = self.src_dst_opt.get_update_op (nn.average_gv_list (gpu_G_loss_gradients))
|
|
|
|
if gan_power != 0:
|
|
GAN_train_op = self.GAN_opt.get_update_op (nn.average_gv_list(gpu_GAN_loss_gradients) )
|
|
|
|
# Initializing training and view functions
|
|
def 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, train_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.train = train
|
|
|
|
if gan_power != 0:
|
|
def GAN_train(warped_src, target_src, target_srcm, target_srcm_em, \
|
|
warped_dst, target_dst, target_dstm, target_dstm_em, ):
|
|
nn.tf_sess.run ([GAN_train_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.GAN_train = GAN_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 , inter_res, inter_res]),
|
|
tf.slice(gpu_dst_inter_dst_code, [0,inter_dims_slice,0,0], [-1,inter_dims-inter_dims_slice, inter_res,inter_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"):
|
|
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 else None #and not self.pretrain
|
|
|
|
cpu_count = multiprocessing.cpu_count()
|
|
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(scale_range=[-0.15, 0.15], random_flip=self.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':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':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':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':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(scale_range=[-0.15, 0.15], random_flip=self.random_dst_flip),
|
|
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
|
{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':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':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':face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
|
],
|
|
uniform_yaw_distribution=self.options['uniform_yaw'],# or self.pretrain,
|
|
generators_count=dst_generators_count )
|
|
])
|
|
|
|
def export_dfm (self):
|
|
output_path=self.get_strpath_storage_for_file('model.dfm')
|
|
|
|
io.log_info(f'Dumping .dfm to {output_path}')
|
|
|
|
tf = nn.tf
|
|
with tf.device (nn.tf_default_device_name):
|
|
warped_dst = tf.placeholder (nn.floatx, (None, self.resolution, self.resolution, 3), name='in_face')
|
|
warped_dst = tf.transpose(warped_dst, (0,3,1,2))
|
|
morph_value = tf.placeholder (nn.floatx, (1,), name='morph_value')
|
|
|
|
gpu_dst_code = self.encoder (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(self.inter_dims*morph_value[0], tf.int32)
|
|
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]),
|
|
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 )
|
|
|
|
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)
|
|
|
|
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')
|
|
tf.identity(gpu_pred_src_dst, name='out_celeb_face')
|
|
tf.identity(gpu_pred_src_dstm, name='out_celeb_face_mask')
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output_graph_def = tf.graph_util.convert_variables_to_constants(
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nn.tf_sess,
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tf.get_default_graph().as_graph_def(),
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['out_face_mask','out_celeb_face','out_celeb_face_mask']
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)
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import tf2onnx
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with tf.device("/CPU:0"):
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model_proto, _ = tf2onnx.convert._convert_common(
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output_graph_def,
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name='AMP',
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input_names=['in_face:0','morph_value:0'],
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output_names=['out_face_mask:0','out_celeb_face:0','out_celeb_face_mask:0'],
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opset=12,
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output_path=output_path)
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|
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#override
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|
def get_model_filename_list(self):
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return self.model_filename_list
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|
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#override
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|
def onSave(self):
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for model, filename in io.progress_bar_generator(self.get_model_filename_list(), "Saving", leave=False):
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model.save_weights ( self.get_strpath_storage_for_file(filename) )
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|
|
|
#override
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|
def should_save_preview_history(self):
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|
return (not io.is_colab() and self.iter % ( 10*(max(1,self.resolution // 64)) ) == 0) or \
|
|
(io.is_colab() and self.iter % 100 == 0)
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|
|
|
#override
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|
def onTrainOneIter(self):
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|
bs = self.get_batch_size()
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|
|
|
( (warped_src, target_src, target_srcm, target_srcm_em), \
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|
(warped_dst, target_dst, target_dstm, target_dstm_em) ) = self.generate_next_samples()
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|
|
|
src_loss, dst_loss = self.train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
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|
|
|
if self.gan_power != 0:
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|
self.GAN_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
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|
|
|
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_100[i] ), axis=1) ]
|
|
|
|
result += [ ('AMP morph 1.0', 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", 1.0, 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
|