models/Model_RTM/Model.py

import multiprocessing
import operator
from functools import partial

import numpy as np

from core import mathlib
from core.interact import interact as io
from core.leras import nn
from facelib import FaceType
from models import ModelBase
from samplelib import *
from core.cv2ex import *

class RTMModel(ModelBase):

    #override
    def on_initialize_options(self):
        device_config = nn.getCurrentDeviceConfig()

        lowest_vram = 2
        if len(device_config.devices) != 0:
            lowest_vram = device_config.devices.get_worst_device().total_mem_gb

        if lowest_vram >= 4:
            suggest_batch_size = 8
        else:
            suggest_batch_size = 4

        yn_str = {True:'y',False:'n'}
        min_res = 64
        max_res = 640
        
        default_usefp16            = self.options['use_fp16']           = self.load_or_def_option('use_fp16', False)
        default_resolution         = self.options['resolution']         = self.load_or_def_option('resolution', 224)
        default_face_type          = self.options['face_type']          = self.load_or_def_option('face_type', 'wf')
        default_models_opt_on_gpu  = self.options['models_opt_on_gpu']  = self.load_or_def_option('models_opt_on_gpu', True)

        default_ae_dims            = self.options['ae_dims']            = self.load_or_def_option('ae_dims', 256)
        
        inter_dims = self.load_or_def_option('inter_dims', None)
        if inter_dims is None:
            inter_dims = self.options['ae_dims']
        default_inter_dims         = self.options['inter_dims'] = inter_dims  
        
        default_e_dims             = self.options['e_dims']             = self.load_or_def_option('e_dims', 64)
        default_d_dims             = self.options['d_dims']             = self.options.get('d_dims', None)
        default_d_mask_dims        = self.options['d_mask_dims']        = self.options.get('d_mask_dims', None)
        default_masked_training    = self.options['masked_training']    = self.load_or_def_option('masked_training', True)
        default_eyes_mouth_prio    = self.options['eyes_mouth_prio']    = self.load_or_def_option('eyes_mouth_prio', True)
        default_uniform_yaw        = self.options['uniform_yaw']        = self.load_or_def_option('uniform_yaw', False)

        default_random_warp        = self.options['random_warp']        = self.load_or_def_option('random_warp', True)
        default_ct_mode            = self.options['ct_mode']            = self.load_or_def_option('ct_mode', 'none')
        default_clipgrad           = self.options['clipgrad']           = self.load_or_def_option('clipgrad', False)
        #default_pretrain           = self.options['pretrain']      = self.load_or_def_option('pretrain', False)


        ask_override = self.ask_override()
        if self.is_first_run() or ask_override:
            self.ask_autobackup_hour()
            self.ask_write_preview_history()
            self.ask_target_iter()
            self.ask_random_src_flip()
            self.ask_random_dst_flip()
            self.ask_batch_size(suggest_batch_size)
            self.options['use_fp16'] = io.input_bool ("Use fp16", default_usefp16, help_message='Increases training/inference speed, reduces model size. Model may crash. Enable it after 1-5k iters.')
            
        if self.is_first_run():
            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 .")
            resolution = np.clip ( (resolution // 32) * 32, min_res, max_res)
            self.options['resolution'] = resolution
            self.options['face_type'] = io.input_str ("Face type", default_face_type, ['f','wf','head'], help_message="whole face / head").lower()


        default_d_dims             = self.options['d_dims']             = self.load_or_def_option('d_dims', 64)

        default_d_mask_dims        = default_d_dims // 3
        default_d_mask_dims        += default_d_mask_dims % 2
        default_d_mask_dims        = self.options['d_mask_dims']        = self.load_or_def_option('d_mask_dims', default_d_mask_dims)

        if self.is_first_run():
            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 )
            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 )

            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 )
            self.options['e_dims'] = e_dims + e_dims % 2

            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 )
            self.options['d_dims'] = d_dims + d_dims % 2

            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 )
            self.options['d_mask_dims'] = d_mask_dims + d_mask_dims % 2

        if self.is_first_run() or ask_override:
            if self.options['face_type'] == 'wf' or self.options['face_type'] == 'head':
                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.")

            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.')
            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.')

        default_gan_power          = self.options['gan_power']          = self.load_or_def_option('gan_power', 0.0)
        default_gan_patch_size     = self.options['gan_patch_size']     = self.load_or_def_option('gan_patch_size', self.options['resolution'] // 8)
        default_gan_dims           = self.options['gan_dims']           = self.load_or_def_option('gan_dims', 16)

        if self.is_first_run() or ask_override:
            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.")

            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.")

            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 )

            if self.options['gan_power'] != 0.0:
                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 )
                self.options['gan_patch_size'] = gan_patch_size

                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 )
                self.options['gan_dims'] = gan_dims

            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.")
            self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")

            #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")

        self.gan_model_changed = (default_gan_patch_size != self.options['gan_patch_size']) or (default_gan_dims != self.options['gan_dims'])
        #self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)

    #override
    def on_initialize(self):
        device_config = nn.getCurrentDeviceConfig()
        devices = device_config.devices
        self.model_data_format = "NCHW"
        nn.initialize(data_format=self.model_data_format)
        tf = nn.tf

        self.resolution = resolution = self.options['resolution']

        input_ch=3
        ae_dims = self.ae_dims = self.options['ae_dims']
        inter_dims = self.inter_dims = self.options['inter_dims']
        e_dims = self.options['e_dims']
        d_dims = self.options['d_dims']
        d_mask_dims = self.options['d_mask_dims']
        inter_res = self.inter_res = resolution // (2**5)
        
        use_fp16 = True#self.options['use_fp16']
        conv_dtype = tf.float16 if use_fp16 else tf.float32
        
        class Downscale(nn.ModelBase):
            def on_build(self, in_ch, out_ch, kernel_size=5):
                self.conv1 = nn.Conv2D( in_ch, out_ch, kernel_size=kernel_size, strides=2, padding='SAME', dtype=conv_dtype)

            def forward(self, x):
                return tf.nn.leaky_relu(self.conv1(x), 0.1)

        class Upscale(nn.ModelBase):
            def on_build(self, in_ch, out_ch, kernel_size=3):
                self.conv1 = nn.Conv2D(in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)

            def forward(self, x):
                x = nn.depth_to_space(tf.nn.leaky_relu(self.conv1(x), 0.1), 2)
                return x

        class ResidualBlock(nn.ModelBase):
            def on_build(self, ch, kernel_size=3):
                self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
                self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)

            def forward(self, inp):
                x = self.conv1(inp)
                x = tf.nn.leaky_relu(x, 0.2)
                x = self.conv2(x)
                x = tf.nn.leaky_relu(inp+x, 0.2)
                return x

        class Encoder(nn.ModelBase):
            def on_build(self):
                self.down1 = Downscale(input_ch, e_dims, kernel_size=5)
                self.res1 = ResidualBlock(e_dims)
                self.down2 = Downscale(e_dims, e_dims*2, kernel_size=5)
                self.down3 = Downscale(e_dims*2, e_dims*4, kernel_size=5)
                self.down4 = Downscale(e_dims*4, e_dims*8, kernel_size=5)
                self.down5 = Downscale(e_dims*8, e_dims*8, kernel_size=5)
                self.res5 = ResidualBlock(e_dims*8)
                self.dense1 = nn.Dense( (( resolution//(2**5) )**2) * e_dims*8, ae_dims )

            def forward(self, x):
                if use_fp16:
                    x = tf.cast(x, tf.float16)
                
                x = self.down1(x)
                x = self.res1(x)
                x = self.down2(x)
                x = self.down3(x)
                x = self.down4(x)
                x = self.down5(x)
                x = self.res5(x)
                if use_fp16:
                    x = tf.cast(x, tf.float32)
                
                x = nn.pixel_norm(x, axes=[1,2,3])
                x = self.dense1(nn.flatten(x))
                return x


        class Inter(nn.ModelBase):
            def on_build(self):
                self.dense2 = nn.Dense(ae_dims, inter_res * inter_res * inter_dims)

            def forward(self, inp):
                x = inp
                x = self.dense2(x)
                x = nn.reshape_4D (x, inter_res, inter_res, inter_dims)
                return x


        class Decoder(nn.ModelBase):
            def on_build(self):
                self.upscale5 = Upscale(inter_dims, d_dims*8, kernel_size=3)
                self.upscale4 = Upscale(d_dims*8, d_dims*8, kernel_size=3)
                self.upscale3 = Upscale(d_dims*8, d_dims*4, kernel_size=3)
                self.upscale2 = Upscale(d_dims*4, d_dims*2, kernel_size=3)
                self.res5 = ResidualBlock(d_dims*8, kernel_size=3)
                self.res4 = ResidualBlock(d_dims*8, kernel_size=3)
                self.res3 = ResidualBlock(d_dims*4, kernel_size=3)
                self.res2 = ResidualBlock(d_dims*2, kernel_size=3)
                self.out_conv  = nn.Conv2D( d_dims*2, 3, kernel_size=1, padding='SAME', dtype=conv_dtype)
                self.out_conv1 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
                self.out_conv2 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
                self.out_conv3 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
                
                self.upscalem5 = Upscale(inter_dims, d_mask_dims*8, kernel_size=3)
                self.upscalem4 = Upscale(d_mask_dims*8, d_mask_dims*8, kernel_size=3)
                self.upscalem3 = Upscale(d_mask_dims*8, d_mask_dims*4, kernel_size=3)
                self.upscalem2 = Upscale(d_mask_dims*4, d_mask_dims*2, kernel_size=3)
                self.upscalem1 = Upscale(d_mask_dims*2, d_mask_dims*1, kernel_size=3)
                self.out_convm = nn.Conv2D( d_mask_dims*1, 1, kernel_size=1, padding='SAME', dtype=conv_dtype)
                
                
            def forward(self, z):
                if use_fp16:
                    z = tf.cast(z, tf.float16)

                x = self.upscale5(z)
                x = self.res5(x)
                x = self.upscale4(x)
                x = self.res4(x)
                x = self.upscale3(x)
                x = self.res3(x)
                x = self.upscale2(x)
                x = self.res2(x)

                x = tf.nn.sigmoid( nn.depth_to_space(tf.concat( (self.out_conv(x),
                                                                 self.out_conv1(x),
                                                                 self.out_conv2(x),
                                                                 self.out_conv3(x)), nn.conv2d_ch_axis), 2) )
                m = self.upscalem5(z)
                m = self.upscalem4(m)
                m = self.upscalem3(m)
                m = self.upscalem2(m)
                m = self.upscalem1(m)
                m = tf.nn.sigmoid(self.out_convm(m))
                
                if use_fp16:
                    x = tf.cast(x, tf.float32)
                    m = tf.cast(m, tf.float32)
                return x, m
                            
        self.face_type = {'f'  : FaceType.FULL,
                          'wf' : FaceType.WHOLE_FACE,
                          'head' : FaceType.HEAD}[ self.options['face_type'] ]

        if 'eyes_prio' in self.options:
            self.options.pop('eyes_prio')

        eyes_mouth_prio = self.options['eyes_mouth_prio']


        gan_power = self.gan_power = self.options['gan_power']
        random_warp = self.options['random_warp']
        random_src_flip = self.random_src_flip
        random_dst_flip = self.random_dst_flip 
        
        #pretrain = self.pretrain = self.options['pretrain']
        #if self.pretrain_just_disabled:
        #    self.set_iter(0)
        # self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
        # random_warp = False if self.pretrain else self.options['random_warp']
        # random_src_flip = self.random_src_flip if not self.pretrain else True
        # random_dst_flip = self.random_dst_flip if not self.pretrain else True

        # if self.pretrain:
        #     self.options_show_override['gan_power'] = 0.0
        #     self.options_show_override['random_warp'] = False
        #     self.options_show_override['lr_dropout'] = 'n'
        #     self.options_show_override['uniform_yaw'] = True

        masked_training = self.options['masked_training']
        ct_mode = self.options['ct_mode']
        if ct_mode == 'none':
            ct_mode = None

        models_opt_on_gpu = False if len(devices) == 0 else self.options['models_opt_on_gpu']
        models_opt_device = nn.tf_default_device_name if models_opt_on_gpu and self.is_training else '/CPU:0'
        optimizer_vars_on_cpu = models_opt_device=='/CPU:0'


        bgr_shape = self.bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
        mask_shape = nn.get4Dshape(resolution,resolution,1)
        self.model_filename_list = []

        with tf.device ('/CPU:0'):
            #Place holders on CPU
            self.warped_src = tf.placeholder (nn.floatx, bgr_shape, name='warped_src')
            self.warped_dst = tf.placeholder (nn.floatx, bgr_shape, name='warped_dst')

            self.target_src = tf.placeholder (nn.floatx, bgr_shape, name='target_src')
            self.target_dst = tf.placeholder (nn.floatx, bgr_shape, name='target_dst')

            self.target_srcm    = tf.placeholder (nn.floatx, mask_shape, name='target_srcm')
            self.target_srcm_em = tf.placeholder (nn.floatx, mask_shape, name='target_srcm_em')
            self.target_dstm    = tf.placeholder (nn.floatx, mask_shape, name='target_dstm')
            self.target_dstm_em = tf.placeholder (nn.floatx, mask_shape, name='target_dstm_em')

        # Initializing model classes

        with tf.device (models_opt_device):
            self.encoder = Encoder(name='encoder')
            self.inter = Inter(name='inter')
            self.decoder_src = Decoder(name='decoder_src')
            self.decoder_dst = Decoder(name='decoder_dst')
            self.true_face_gan = nn.CodeDiscriminator(inter_dims, code_res=self.inter_res, name='true_face_gan' )

            self.model_filename_list += [ [self.encoder,  'encoder.npy'],
                                          [self.inter,    'inter.npy'],
                                          [self.decoder_src, 'decoder_src.npy'],
                                          [self.decoder_dst, 'decoder_dst.npy'],
                                          [self.true_face_gan, 'true_face_gan.npy'],                                          
                                        ]

            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'], use_fp16=use_fp16, name="GAN")
                    self.model_filename_list += [ [self.GAN, 'GAN.npy'] ]

                # Initialize optimizers
                lr=5e-5
                lr_dropout = 0.3
                clipnorm = 1.0 if self.options['clipgrad'] else 0.0

                self.all_weights = self.true_face_gan.get_weights() + self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.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)
                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)
                    self.model_filename_list += [ (self.GAN_opt, 'GAN_opt.npy') ]
                
                #self.BGGAN_opt = nn.AdaBelief(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='BGGAN_opt')
                #self.BGGAN_opt.initialize_variables ( self.BGGAN.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
                #self.model_filename_list += [ (self.BGGAN_opt, 'BGGAN_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_pred_test_list = []
            gpu_pred_src_dst_bg_list = []
            
            gpu_src_losses = []
            gpu_dst_losses = []
            gpu_G_loss_gvs = []
            gpu_D_src_dst_loss_gvs = []
            gpu_D_code_loss_gvs = []
            gpu_D_bg_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_target_srcm_blur = nn.gaussian_blur(gpu_target_srcm,  max(1, resolution // 32) )
                    gpu_target_dstm_blur = nn.gaussian_blur(gpu_target_dstm,  max(1, resolution // 32) )
                    gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_blur, 0, 0.5) * 2
                    gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
                    
                    gpu_target_srcm_antiblur = 1.0-gpu_target_srcm_blur
                    gpu_target_dstm_antiblur = 1.0-gpu_target_dstm_blur
                    #gpu_target_dstm_edge = tf.clip_by_value(gpu_target_dstm_blur*gpu_target_dstm_antiblur*4, 0, 1)
                    #gpu_target_dst_edge = gpu_target_dst*gpu_target_dstm_edge
                    #gpu_pred_dst_dst_edge = gpu_pred_dst_dst*gpu_target_dstm_edge 
                    #gpu_pred_src_dst_edge = gpu_pred_src_dst*gpu_target_dstm_edge
                    #gpu_pred_test_list.append( tf.tile( tf.clip_by_value(gpu_target_dstm_blur*gpu_target_dstm_antiblur*4, 0, 1), (1,3,1,1) ) )
                    
                    gpu_src_code,   gpu_dst_code = self.inter(self.encoder(gpu_warped_src)), self.inter(self.encoder(gpu_warped_dst))
                    gpu_src_code_d, gpu_dst_code_d = self.true_face_gan(gpu_src_code), self.true_face_gan(gpu_dst_code)
                    
                    gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(gpu_src_code)
                    gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
                    gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
                    
                    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_src_blur = gpu_target_src*gpu_target_srcm_blur 
                    gpu_pred_src_src_blur = gpu_pred_src_src*gpu_target_srcm_blur 
                    gpu_pred_dst_dst_blur = gpu_pred_dst_dst*gpu_target_dstm_blur 

                    gpu_src_loss  = tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src_blur, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
                    gpu_src_loss += tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src_blur, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
                    gpu_src_loss += tf.reduce_mean (10*tf.square(gpu_target_src-gpu_pred_src_src_blur), axis=[1,2,3])
                    gpu_src_loss += tf.reduce_mean ( 0.1*nn.dssim(gpu_pred_src_src*gpu_target_srcm_antiblur, gpu_target_src*gpu_target_srcm_antiblur, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
                    
                    if eyes_mouth_prio:
                        gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_srcm_em - gpu_pred_src_src_blur*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] )
                    
                    # sewing loss
                    #src_dstm_diff = tf.stop_gradient( (1-gpu_pred_src_dstm)*gpu_pred_dst_dstm + (1-gpu_pred_dst_dstm)*gpu_pred_src_dstm )
                    src_dstm_diff = nn.gaussian_blur(gpu_pred_dst_dstm, resolution//4)
                    src_dstm_diff += nn.gaussian_blur(gpu_pred_dst_dstm, resolution//8)
                    src_dstm_diff += nn.gaussian_blur(gpu_pred_dst_dstm, resolution//16)
                    src_dstm_diff *= (1-gpu_pred_dst_dstm)
                    src_dstm_diff = tf.stop_gradient(src_dstm_diff)
                    gpu_src_loss += tf.reduce_mean ( 5*nn.dssim (gpu_target_dst*src_dstm_diff, gpu_pred_src_dst*src_dstm_diff, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
                    gpu_pred_test_list.append( tf.tile( tf.clip_by_value( src_dstm_diff,0,1), (1,3,1,1) ) )  #src_dstm_diff + src_dstm_diff_blur * (1-gpu_pred_dst_dstm) 
  
                    gpu_dst_loss  = tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst_blur, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
                    gpu_dst_loss += tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst_blur, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
                    gpu_dst_loss += tf.reduce_mean (10*tf.square(gpu_target_dst-gpu_pred_dst_dst_blur ), axis=[1,2,3])
                    gpu_dst_loss += tf.reduce_mean ( 1*nn.dssim(gpu_pred_dst_dst*gpu_target_dstm_antiblur, gpu_target_dst*gpu_target_dstm_antiblur, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
                    
                    if eyes_mouth_prio:
                        gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_em - gpu_pred_dst_dst_blur*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_src_losses += [gpu_src_loss]
                    gpu_dst_losses += [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])
                    
                    #dst_dst_edge_loss  = tf.reduce_mean ( 5*nn.dssim (gpu_target_dst,gpu_pred_dst_dst_edge, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
                    #dst_dst_edge_loss += tf.reduce_mean ( 5*nn.dssim (gpu_target_dst,gpu_pred_dst_dst_edge, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
                    #dst_dst_edge_loss += tf.reduce_mean (10*tf.square(gpu_target_dst-gpu_pred_dst_dst_edge), axis=[1,2,3])
                    
                    gpu_G_loss = gpu_src_loss + gpu_dst_loss + 1.0*DLossOnes(gpu_src_code_d) 
                    
                    
                    #gpu_G_loss += 0.1*(# DLossOnes(gpu_bg_src_dst_d) + DLossOnes(gpu_bg_src_dst_d2)  + \
                    #                    DLossOnes(gpu_bg_fg_dst_dst_d) + DLossOnes(gpu_bg_fg_dst_dst_d2) ) / 4.0
                    
                    gpu_D_code_loss = ( DLossOnes(gpu_dst_code_d) + DLossZeros(gpu_src_code_d) ) * 0.5
                    #gpu_bg_target_dst_d, gpu_bg_target_dst_d2
                    #gpu_D_bg_loss = ( DLossOnes(gpu_bg_dst_dst_d) + DLossOnes(gpu_bg_dst_dst_d2) + \
                    #                  DLossZeros(gpu_bg_fg_dst_dst_d) + DLossZeros(gpu_bg_fg_dst_dst_d2) \
                    #                 ) / 6.0
                                      #DLossZeros(gpu_bg_src_dst_d) + DLossZeros(gpu_bg_src_dst_d2) \
                                          
                                    
                                     
                    #gpu_D_bg_loss_gvs += [ nn.gradients (gpu_D_bg_loss, self.BGGAN.get_weights() ) ]

                    #
                    # Suppress random bright dots from BGGAN
                    #gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_dst_dst_anti_masked)
                    #gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_src_dst_anti_masked)
                    
                    if gan_power != 0:
                        gpu_pred_src_src_d, gpu_pred_src_src_d2 = self.GAN(gpu_pred_src_src_blur)
                        gpu_target_src_d, gpu_target_src_d2 = self.GAN(gpu_target_src_blur)
                        #gpu_pred_dst_dst_d, gpu_pred_dst_dst_d2 = self.GAN(gpu_pred_dst_dst_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) 
                                             ) * ( 1.0 / 8)
                                            #DLossOnes (gpu_target_dst_d)   + DLossOnes (gpu_target_dst_d2) + \
                                            #DLossZeros(gpu_pred_dst_dst_d) + DLossZeros(gpu_pred_dst_dst_d2)

                        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) ) * gan_power
                                       #DLossOnes(gpu_pred_dst_dst_d) + DLossOnes(gpu_pred_dst_dst_d2)

                        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.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights() ) ]
                    gpu_D_code_loss_gvs += [ nn.gradients (gpu_D_code_loss, self.true_face_gan.get_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)
                
                pred_test = nn.concat(gpu_pred_test_list, 0)
                
                #pred_dst_dst_bg = nn.concat(gpu_pred_dst_dst_bg_list, 0)
                #pred_src_dst_bg = nn.concat(gpu_pred_src_dst_bg_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))
                D_loss_gv_op = self.src_dst_opt.get_update_op (nn.average_gv_list(gpu_D_code_loss_gvs))
                
                #D_bg_loss_gv_op = self.BGGAN_opt.get_update_op (nn.average_gv_list(gpu_D_bg_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, D_loss_gv_op, ],#D_bg_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, target_dstm):
                return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm,
                                            pred_test],
                                            feed_dict={self.warped_src:warped_src, self.warped_dst:warped_dst,
                                                        self.target_dstm : target_dstm  })

            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.inter(self.encoder (self.warped_dst))
                
                gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
                _, gpu_pred_dst_dstm = self.decoder_dst(gpu_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 else None #and not self.pretrain 

            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},
                                                {'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},
                                                {'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=dst_generators_count )
                             ])

            self.last_src_samples_loss = []
            self.last_dst_samples_loss = []
            #if self.pretrain_just_disabled:
            #    self.update_sample_for_preview(force_new=True)


    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))
        
            gpu_dst_code = self.inter(self.encoder (warped_dst))
            
            gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
            _, gpu_pred_dst_dstm = self.decoder_dst(gpu_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')
        
        output_graph_def = tf.graph_util.convert_variables_to_constants(
            nn.tf_sess, 
            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'],
                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, SD, SDM, TEST = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + \
                                                    self.AE_view (target_src, target_dst, target_dstm) ) ]
        DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]

        target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )]

        n_samples = min(4, self.get_batch_size(), 800 // self.resolution )

        result = []

        st = []
        for i in range(n_samples):
            ar = S[i], SS[i], D[i], DD[i], SD[i], TEST[i]
            st.append ( np.concatenate ( ar, axis=1) )
        result += [ ('RTM', np.concatenate (st, axis=0 )), ]


        st_m = []
        for i in range(n_samples):

            ar = S[i]*target_srcm[i], SS[i], D[i]*target_dstm[i], DD[i]*DDM[i], SD[i]*SDM[i], TEST[i]
            st_m.append ( np.concatenate ( ar, axis=1) )

        result += [ ('RTM masked', np.concatenate (st_m, 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):
        import merger
        return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMasked(face_type=self.face_type, default_mode = 'overlay')

Model = RTMModel