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https://github.com/iperov/DeepFaceLab.git
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50f892d57d
If you want, you can manually remove unnecessary angles from src faceset after sort by yaw. Optimized sample generators (CPU workers). Now they consume less amount of RAM and work faster. added 4.2.other) data_src/dst util faceset pack.bat Packs /aligned/ samples into one /aligned/samples.pak file. After that, all faces will be deleted. 4.2.other) data_src/dst util faceset unpack.bat unpacks faces from /aligned/samples.pak to /aligned/ dir. After that, samples.pak will be deleted. Packed faceset load and work faster.
569 lines
32 KiB
Python
569 lines
32 KiB
Python
from functools import partial
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import numpy as np
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import mathlib
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from facelib import FaceType
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from interact import interact as io
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from models import ModelBase
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from nnlib import nnlib
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from samplelib import *
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#SAE - Styled AutoEncoder
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class SAEModel(ModelBase):
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#override
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def onInitializeOptions(self, is_first_run, ask_override):
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yn_str = {True:'y',False:'n'}
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default_resolution = 128
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default_archi = 'df'
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default_face_type = 'f'
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if is_first_run:
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resolution = io.input_int("Resolution ( 64-256 ?:help skip:128) : ", default_resolution, help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
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resolution = np.clip (resolution, 64, 256)
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while np.modf(resolution / 16)[0] != 0.0:
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resolution -= 1
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self.options['resolution'] = resolution
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self.options['face_type'] = io.input_str ("Half or Full face? (h/f, ?:help skip:f) : ", default_face_type, ['h','f'], help_message="Half face has better resolution, but covers less area of cheeks.").lower()
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else:
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self.options['resolution'] = self.options.get('resolution', default_resolution)
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self.options['face_type'] = self.options.get('face_type', default_face_type)
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default_learn_mask = self.options.get('learn_mask', True)
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if is_first_run or ask_override:
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self.options['learn_mask'] = io.input_bool ( f"Learn mask? (y/n, ?:help skip:{yn_str[default_learn_mask]} ) : " , default_learn_mask, help_message="Learning mask can help model to recognize face directions. Learn without mask can reduce model size, in this case converter forced to use 'not predicted mask' that is not smooth as predicted. Model with style values can be learned without mask and produce same quality result.")
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else:
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self.options['learn_mask'] = self.options.get('learn_mask', default_learn_mask)
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if (is_first_run or ask_override) and 'tensorflow' in self.device_config.backend:
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def_optimizer_mode = self.options.get('optimizer_mode', 1)
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self.options['optimizer_mode'] = io.input_int ("Optimizer mode? ( 1,2,3 ?:help skip:%d) : " % (def_optimizer_mode), def_optimizer_mode, help_message="1 - no changes. 2 - allows you to train x2 bigger network consuming RAM. 3 - allows you to train x3 bigger network consuming huge amount of RAM and slower, depends on CPU power.")
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else:
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self.options['optimizer_mode'] = self.options.get('optimizer_mode', 1)
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if is_first_run:
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self.options['archi'] = io.input_str ("AE architecture (df, liae ?:help skip:%s) : " % (default_archi) , default_archi, ['df','liae'], help_message="'df' keeps faces more natural. 'liae' can fix overly different face shapes.").lower() #-s version is slower, but has decreased change to collapse.
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else:
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self.options['archi'] = self.options.get('archi', default_archi)
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default_ae_dims = 256 if 'liae' in self.options['archi'] else 512
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default_e_ch_dims = 42
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default_d_ch_dims = default_e_ch_dims // 2
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def_ca_weights = False
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if is_first_run:
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self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dims (32-1024 ?:help skip:%d) : " % (default_ae_dims) , default_ae_dims, 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['e_ch_dims'] = np.clip ( io.input_int("Encoder dims per channel (21-85 ?:help skip:%d) : " % (default_e_ch_dims) , default_e_ch_dims, help_message="More encoder dims help to recognize more facial features, but require more VRAM. You can fine-tune model size to fit your GPU." ), 21, 85 )
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default_d_ch_dims = self.options['e_ch_dims'] // 2
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self.options['d_ch_dims'] = np.clip ( io.input_int("Decoder dims per channel (10-85 ?:help skip:%d) : " % (default_d_ch_dims) , default_d_ch_dims, help_message="More decoder dims help to get better details, but require more VRAM. You can fine-tune model size to fit your GPU." ), 10, 85 )
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self.options['ca_weights'] = io.input_bool (f"Use CA weights? (y/n, ?:help skip:{yn_str[def_ca_weights]} ) : ", def_ca_weights, help_message="Initialize network with 'Convolution Aware' weights. This may help to achieve a higher accuracy model, but consumes a time at first run.")
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else:
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self.options['ae_dims'] = self.options.get('ae_dims', default_ae_dims)
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self.options['e_ch_dims'] = self.options.get('e_ch_dims', default_e_ch_dims)
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self.options['d_ch_dims'] = self.options.get('d_ch_dims', default_d_ch_dims)
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self.options['ca_weights'] = self.options.get('ca_weights', def_ca_weights)
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default_face_style_power = 0.0
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default_bg_style_power = 0.0
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if is_first_run or ask_override:
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def_pixel_loss = self.options.get('pixel_loss', False)
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self.options['pixel_loss'] = io.input_bool (f"Use pixel loss? (y/n, ?:help skip:{yn_str[def_pixel_loss]} ) : ", def_pixel_loss, help_message="Pixel loss may help to enhance fine details and stabilize face color. Use it only if quality does not improve over time. Enabling this option too early increases the chance of model collapse.")
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default_face_style_power = default_face_style_power if is_first_run else self.options.get('face_style_power', default_face_style_power)
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self.options['face_style_power'] = np.clip ( io.input_number("Face style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_face_style_power), default_face_style_power,
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help_message="Learn to transfer face style details such as light and color conditions. Warning: Enable it only after 10k iters, when predicted face is clear enough to start learn style. Start from 0.1 value and check history changes. Enabling this option increases the chance of model collapse."), 0.0, 100.0 )
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default_bg_style_power = default_bg_style_power if is_first_run else self.options.get('bg_style_power', default_bg_style_power)
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self.options['bg_style_power'] = np.clip ( io.input_number("Background style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_bg_style_power), default_bg_style_power,
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help_message="Learn to transfer image around face. This can make face more like dst. Enabling this option increases the chance of model collapse."), 0.0, 100.0 )
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default_ct_mode = self.options.get('ct_mode', 'none')
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self.options['ct_mode'] = io.input_str (f"Color transfer mode apply to src faceset. ( none/rct/lct/mkl/idt/sot, ?:help skip:{default_ct_mode}) : ", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
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if nnlib.device.backend != 'plaidML': # todo https://github.com/plaidml/plaidml/issues/301
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default_clipgrad = False if is_first_run else self.options.get('clipgrad', False)
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self.options['clipgrad'] = io.input_bool (f"Enable gradient clipping? (y/n, ?:help skip:{yn_str[default_clipgrad]}) : ", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")
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else:
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self.options['clipgrad'] = False
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else:
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self.options['pixel_loss'] = self.options.get('pixel_loss', False)
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self.options['face_style_power'] = self.options.get('face_style_power', default_face_style_power)
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self.options['bg_style_power'] = self.options.get('bg_style_power', default_bg_style_power)
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self.options['ct_mode'] = self.options.get('ct_mode', 'none')
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self.options['clipgrad'] = self.options.get('clipgrad', False)
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if is_first_run:
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self.options['pretrain'] = io.input_bool ("Pretrain the model? (y/n, ?:help skip:n) : ", False, help_message="Pretrain the model with large amount of various faces. This technique may help to train the fake with overly different face shapes and light conditions of src/dst data. Face will be look more like a morphed. To reduce the morph effect, some model files will be initialized but not be updated after pretrain: LIAE: inter_AB.h5 DF: encoder.h5. The longer you pretrain the model the more morphed face will look. After that, save and run the training again.")
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else:
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self.options['pretrain'] = False
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#override
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def onInitialize(self):
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exec(nnlib.import_all(), locals(), globals())
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self.set_vram_batch_requirements({1.5:4})
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resolution = self.options['resolution']
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learn_mask = self.options['learn_mask']
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ae_dims = self.options['ae_dims']
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e_ch_dims = self.options['e_ch_dims']
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d_ch_dims = self.options['d_ch_dims']
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self.pretrain = self.options['pretrain'] = self.options.get('pretrain', False)
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if not self.pretrain:
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self.options.pop('pretrain')
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bgr_shape = (resolution, resolution, 3)
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mask_shape = (resolution, resolution, 1)
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masked_training = True
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class SAEDFModel(object):
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def __init__(self, resolution, ae_dims, e_ch_dims, d_ch_dims, learn_mask):
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super().__init__()
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self.learn_mask = learn_mask
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output_nc = 3
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bgr_shape = (resolution, resolution, output_nc)
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mask_shape = (resolution, resolution, 1)
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lowest_dense_res = resolution // 16
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e_dims = output_nc*e_ch_dims
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def upscale (dim):
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def func(x):
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return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, kernel_size=3, strides=1, padding='valid')(ZeroPadding2D(1)(x))))
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return func
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def enc_flow(e_dims, ae_dims, lowest_dense_res):
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def func(x):
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x = LeakyReLU(0.1)(Conv2D(e_dims, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*2, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*4, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*8, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = Dense(ae_dims)(Flatten()(x))
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x = Dense(lowest_dense_res * lowest_dense_res * ae_dims)(x)
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x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims))(x)
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x = upscale(ae_dims)(x)
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return x
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return func
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def dec_flow(output_nc, d_ch_dims, add_residual_blocks=True):
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dims = output_nc * d_ch_dims
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def ResidualBlock(dim):
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def func(inp):
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x = Conv2D(dim, kernel_size=3, padding='valid')(ZeroPadding2D(1)(inp))
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x = LeakyReLU(0.2)(x)
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x = Conv2D(dim, kernel_size=3, padding='valid')(ZeroPadding2D(1)(x))
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x = Add()([x, inp])
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x = LeakyReLU(0.2)(x)
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return x
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return func
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def func(x):
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x = upscale(dims*8)(x)
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if add_residual_blocks:
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x = ResidualBlock(dims*8)(x)
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x = ResidualBlock(dims*8)(x)
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x = upscale(dims*4)(x)
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if add_residual_blocks:
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x = ResidualBlock(dims*4)(x)
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x = ResidualBlock(dims*4)(x)
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x = upscale(dims*2)(x)
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if add_residual_blocks:
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x = ResidualBlock(dims*2)(x)
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x = ResidualBlock(dims*2)(x)
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return Conv2D(output_nc, kernel_size=5, padding='valid', activation='sigmoid')(ZeroPadding2D(2)(x))
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return func
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self.encoder = modelify(enc_flow(e_dims, ae_dims, lowest_dense_res)) ( Input(bgr_shape) )
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sh = K.int_shape( self.encoder.outputs[0] )[1:]
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self.decoder_src = modelify(dec_flow(output_nc, d_ch_dims)) ( Input(sh) )
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self.decoder_dst = modelify(dec_flow(output_nc, d_ch_dims)) ( Input(sh) )
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if learn_mask:
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self.decoder_srcm = modelify(dec_flow(1, d_ch_dims, add_residual_blocks=False)) ( Input(sh) )
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self.decoder_dstm = modelify(dec_flow(1, d_ch_dims, add_residual_blocks=False)) ( Input(sh) )
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self.src_dst_trainable_weights = self.encoder.trainable_weights + self.decoder_src.trainable_weights + self.decoder_dst.trainable_weights
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if learn_mask:
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self.src_dst_mask_trainable_weights = self.encoder.trainable_weights + self.decoder_srcm.trainable_weights + self.decoder_dstm.trainable_weights
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self.warped_src, self.warped_dst = Input(bgr_shape), Input(bgr_shape)
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src_code, dst_code = self.encoder(self.warped_src), self.encoder(self.warped_dst)
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self.pred_src_src = self.decoder_src(src_code)
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self.pred_dst_dst = self.decoder_dst(dst_code)
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self.pred_src_dst = self.decoder_src(dst_code)
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if learn_mask:
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self.pred_src_srcm = self.decoder_srcm(src_code)
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self.pred_dst_dstm = self.decoder_dstm(dst_code)
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self.pred_src_dstm = self.decoder_srcm(dst_code)
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def get_model_filename_list(self, exclude_for_pretrain=False):
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ar = []
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if not exclude_for_pretrain:
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ar += [ [self.encoder, 'encoder.h5'] ]
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ar += [ [self.decoder_src, 'decoder_src.h5'],
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[self.decoder_dst, 'decoder_dst.h5'] ]
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if self.learn_mask:
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ar += [ [self.decoder_srcm, 'decoder_srcm.h5'],
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[self.decoder_dstm, 'decoder_dstm.h5'] ]
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return ar
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class SAELIAEModel(object):
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def __init__(self, resolution, ae_dims, e_ch_dims, d_ch_dims, learn_mask):
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super().__init__()
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self.learn_mask = learn_mask
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output_nc = 3
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bgr_shape = (resolution, resolution, output_nc)
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mask_shape = (resolution, resolution, 1)
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e_dims = output_nc*e_ch_dims
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lowest_dense_res = resolution // 16
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def upscale (dim):
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def func(x):
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return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, kernel_size=3, strides=1, padding='valid')(ZeroPadding2D(1)(x))))
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return func
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def enc_flow(e_dims):
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def func(x):
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x = LeakyReLU(0.1)(Conv2D(e_dims, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*2, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*4, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = LeakyReLU(0.1)(Conv2D(e_dims*8, kernel_size=5, strides=2, padding='valid')(ZeroPadding2D(2)(x)))
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x = Flatten()(x)
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return x
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return func
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def inter_flow(lowest_dense_res, ae_dims):
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def func(x):
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x = Dense(ae_dims)(x)
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x = Dense(lowest_dense_res * lowest_dense_res * ae_dims*2)(x)
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x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims*2))(x)
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x = upscale(ae_dims*2)(x)
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return x
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return func
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def dec_flow(output_nc, d_ch_dims, add_residual_blocks=True):
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d_dims = output_nc*d_ch_dims
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def ResidualBlock(dim):
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def func(inp):
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x = Conv2D(dim, kernel_size=3, padding='valid')(ZeroPadding2D(1)(inp))
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x = LeakyReLU(0.2)(x)
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x = Conv2D(dim, kernel_size=3, padding='valid')(ZeroPadding2D(1)(inp))
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x = Add()([x, inp])
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x = LeakyReLU(0.2)(x)
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return x
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return func
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def func(x):
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x = upscale(d_dims*8)(x)
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if add_residual_blocks:
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x = ResidualBlock(d_dims*8)(x)
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x = ResidualBlock(d_dims*8)(x)
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x = upscale(d_dims*4)(x)
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if add_residual_blocks:
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x = ResidualBlock(d_dims*4)(x)
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x = ResidualBlock(d_dims*4)(x)
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x = upscale(d_dims*2)(x)
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if add_residual_blocks:
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x = ResidualBlock(d_dims*2)(x)
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x = ResidualBlock(d_dims*2)(x)
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return Conv2D(output_nc, kernel_size=5, padding='valid', activation='sigmoid')(ZeroPadding2D(2)(x))
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return func
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self.encoder = modelify(enc_flow(e_dims)) ( Input(bgr_shape) )
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sh = K.int_shape( self.encoder.outputs[0] )[1:]
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self.inter_B = modelify(inter_flow(lowest_dense_res, ae_dims)) ( Input(sh) )
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self.inter_AB = modelify(inter_flow(lowest_dense_res, ae_dims)) ( Input(sh) )
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sh = np.array(K.int_shape( self.inter_B.outputs[0] )[1:])*(1,1,2)
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self.decoder = modelify(dec_flow(output_nc, d_ch_dims)) ( Input(sh) )
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if learn_mask:
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self.decoderm = modelify(dec_flow(1, d_ch_dims, add_residual_blocks=False)) ( Input(sh) )
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self.src_dst_trainable_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights
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if learn_mask:
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self.src_dst_mask_trainable_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoderm.trainable_weights
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self.warped_src, self.warped_dst = Input(bgr_shape), Input(bgr_shape)
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warped_src_code = self.encoder (self.warped_src)
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warped_src_inter_AB_code = self.inter_AB (warped_src_code)
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warped_src_inter_code = Concatenate()([warped_src_inter_AB_code,warped_src_inter_AB_code])
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warped_dst_code = self.encoder (self.warped_dst)
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warped_dst_inter_B_code = self.inter_B (warped_dst_code)
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warped_dst_inter_AB_code = self.inter_AB (warped_dst_code)
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warped_dst_inter_code = Concatenate()([warped_dst_inter_B_code,warped_dst_inter_AB_code])
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warped_src_dst_inter_code = Concatenate()([warped_dst_inter_AB_code,warped_dst_inter_AB_code])
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self.pred_src_src = self.decoder(warped_src_inter_code)
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self.pred_dst_dst = self.decoder(warped_dst_inter_code)
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self.pred_src_dst = self.decoder(warped_src_dst_inter_code)
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if learn_mask:
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self.pred_src_srcm = self.decoderm(warped_src_inter_code)
|
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self.pred_dst_dstm = self.decoderm(warped_dst_inter_code)
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self.pred_src_dstm = self.decoderm(warped_src_dst_inter_code)
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|
|
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def get_model_filename_list(self, exclude_for_pretrain=False):
|
|
ar = [ [self.encoder, 'encoder.h5'],
|
|
[self.inter_B, 'inter_B.h5'] ]
|
|
|
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if not exclude_for_pretrain:
|
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ar += [ [self.inter_AB, 'inter_AB.h5'] ]
|
|
|
|
ar += [ [self.decoder, 'decoder.h5'] ]
|
|
|
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if self.learn_mask:
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|
ar += [ [self.decoderm, 'decoderm.h5'] ]
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|
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|
return ar
|
|
|
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if 'df' in self.options['archi']:
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|
self.model = SAEDFModel (resolution, ae_dims, e_ch_dims, d_ch_dims, learn_mask)
|
|
elif 'liae' in self.options['archi']:
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|
self.model = SAELIAEModel (resolution, ae_dims, e_ch_dims, d_ch_dims, learn_mask)
|
|
|
|
loaded, not_loaded = [], self.model.get_model_filename_list()
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|
if not self.is_first_run():
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|
loaded, not_loaded = self.load_weights_safe(not_loaded)
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|
|
|
CA_models = []
|
|
if self.options.get('ca_weights', False):
|
|
CA_models += [ model for model, _ in not_loaded ]
|
|
|
|
CA_conv_weights_list = []
|
|
for model in CA_models:
|
|
for layer in model.layers:
|
|
if type(layer) == keras.layers.Conv2D:
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|
CA_conv_weights_list += [layer.weights[0]] #- is Conv2D kernel_weights
|
|
|
|
if len(CA_conv_weights_list) != 0:
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|
CAInitializerMP ( CA_conv_weights_list )
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|
|
|
warped_src = self.model.warped_src
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|
target_src = Input ( (resolution, resolution, 3) )
|
|
target_srcm = Input ( (resolution, resolution, 1) )
|
|
|
|
warped_dst = self.model.warped_dst
|
|
target_dst = Input ( (resolution, resolution, 3) )
|
|
target_dstm = Input ( (resolution, resolution, 1) )
|
|
|
|
target_src_sigm = target_src
|
|
target_dst_sigm = target_dst
|
|
|
|
target_srcm_sigm = gaussian_blur( max(1, K.int_shape(target_srcm)[1] // 32) )(target_srcm)
|
|
target_dstm_sigm = gaussian_blur( max(1, K.int_shape(target_dstm)[1] // 32) )(target_dstm)
|
|
target_dstm_anti_sigm = 1.0 - target_dstm_sigm
|
|
|
|
target_src_masked = target_src_sigm*target_srcm_sigm
|
|
target_dst_masked = target_dst_sigm*target_dstm_sigm
|
|
target_dst_anti_masked = target_dst_sigm*target_dstm_anti_sigm
|
|
|
|
target_src_masked_opt = target_src_masked if masked_training else target_src_sigm
|
|
target_dst_masked_opt = target_dst_masked if masked_training else target_dst_sigm
|
|
|
|
pred_src_src = self.model.pred_src_src
|
|
pred_dst_dst = self.model.pred_dst_dst
|
|
pred_src_dst = self.model.pred_src_dst
|
|
if learn_mask:
|
|
pred_src_srcm = self.model.pred_src_srcm
|
|
pred_dst_dstm = self.model.pred_dst_dstm
|
|
pred_src_dstm = self.model.pred_src_dstm
|
|
|
|
pred_src_src_sigm = self.model.pred_src_src
|
|
pred_dst_dst_sigm = self.model.pred_dst_dst
|
|
pred_src_dst_sigm = self.model.pred_src_dst
|
|
|
|
pred_src_src_masked = pred_src_src_sigm*target_srcm_sigm
|
|
pred_dst_dst_masked = pred_dst_dst_sigm*target_dstm_sigm
|
|
|
|
pred_src_src_masked_opt = pred_src_src_masked if masked_training else pred_src_src_sigm
|
|
pred_dst_dst_masked_opt = pred_dst_dst_masked if masked_training else pred_dst_dst_sigm
|
|
|
|
psd_target_dst_masked = pred_src_dst_sigm*target_dstm_sigm
|
|
psd_target_dst_anti_masked = pred_src_dst_sigm*target_dstm_anti_sigm
|
|
|
|
if self.is_training_mode:
|
|
self.src_dst_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
|
|
self.src_dst_mask_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
|
|
|
|
if not self.options['pixel_loss']:
|
|
src_loss = K.mean ( 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_opt, pred_src_src_masked_opt) )
|
|
else:
|
|
src_loss = K.mean ( 50*K.square( target_src_masked_opt - pred_src_src_masked_opt ) )
|
|
|
|
face_style_power = self.options['face_style_power'] / 100.0
|
|
if face_style_power != 0:
|
|
src_loss += style_loss(gaussian_blur_radius=resolution//16, loss_weight=face_style_power, wnd_size=0)( psd_target_dst_masked, target_dst_masked )
|
|
|
|
bg_style_power = self.options['bg_style_power'] / 100.0
|
|
if bg_style_power != 0:
|
|
if not self.options['pixel_loss']:
|
|
src_loss += K.mean( (10*bg_style_power)*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( psd_target_dst_anti_masked, target_dst_anti_masked ))
|
|
else:
|
|
src_loss += K.mean( (50*bg_style_power)*K.square( psd_target_dst_anti_masked - target_dst_anti_masked ))
|
|
|
|
if not self.options['pixel_loss']:
|
|
dst_loss = K.mean( 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)(target_dst_masked_opt, pred_dst_dst_masked_opt) )
|
|
else:
|
|
dst_loss = K.mean( 50*K.square( target_dst_masked_opt - pred_dst_dst_masked_opt ) )
|
|
|
|
self.src_dst_train = K.function ([warped_src, warped_dst, target_src, target_srcm, target_dst, target_dstm],[src_loss,dst_loss], self.src_dst_opt.get_updates(src_loss+dst_loss, self.model.src_dst_trainable_weights) )
|
|
|
|
if self.options['learn_mask']:
|
|
src_mask_loss = K.mean(K.square(target_srcm-pred_src_srcm))
|
|
dst_mask_loss = K.mean(K.square(target_dstm-pred_dst_dstm))
|
|
self.src_dst_mask_train = K.function ([warped_src, warped_dst, target_srcm, target_dstm],[src_mask_loss, dst_mask_loss], self.src_dst_mask_opt.get_updates(src_mask_loss+dst_mask_loss, self.model.src_dst_mask_trainable_weights ) )
|
|
|
|
if self.options['learn_mask']:
|
|
self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm])
|
|
else:
|
|
self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src, pred_dst_dst, pred_src_dst ])
|
|
|
|
else:
|
|
if self.options['learn_mask']:
|
|
self.AE_convert = K.function ([warped_dst],[ pred_src_dst, pred_dst_dstm, pred_src_dstm ])
|
|
else:
|
|
self.AE_convert = K.function ([warped_dst],[ pred_src_dst ])
|
|
|
|
|
|
if self.is_training_mode:
|
|
t = SampleProcessor.Types
|
|
face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF
|
|
|
|
t_mode_bgr = t.MODE_BGR if not self.pretrain else t.MODE_BGR_SHUFFLE
|
|
|
|
training_data_src_path = self.training_data_src_path
|
|
training_data_dst_path = self.training_data_dst_path
|
|
|
|
if self.pretrain and self.pretraining_data_path is not None:
|
|
training_data_src_path = self.pretraining_data_path
|
|
training_data_dst_path = self.pretraining_data_path
|
|
|
|
self.set_training_data_generators ([
|
|
SampleGeneratorFace(training_data_src_path, random_ct_samples_path=training_data_dst_path if self.options['ct_mode'] != 'none' else None,
|
|
debug=self.is_debug(), batch_size=self.batch_size,
|
|
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05]) ),
|
|
output_sample_types = [ {'types' : (t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr), 'resolution':resolution, 'ct_mode': self.options['ct_mode'] },
|
|
{'types' : (t.IMG_TRANSFORMED, face_type, t_mode_bgr), 'resolution': resolution, 'ct_mode': self.options['ct_mode'] },
|
|
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'resolution': resolution } ]
|
|
),
|
|
|
|
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
|
|
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ),
|
|
output_sample_types = [ {'types' : (t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr), 'resolution':resolution},
|
|
{'types' : (t.IMG_TRANSFORMED, face_type, t_mode_bgr), 'resolution': resolution},
|
|
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'resolution': resolution} ])
|
|
])
|
|
|
|
#override
|
|
def get_model_filename_list(self):
|
|
ar = self.model.get_model_filename_list ( exclude_for_pretrain=(self.pretrain and self.iter != 0) )
|
|
return ar
|
|
|
|
#override
|
|
def onSave(self):
|
|
self.save_weights_safe( self.get_model_filename_list() )
|
|
|
|
#override
|
|
def onTrainOneIter(self, generators_samples, generators_list):
|
|
warped_src, target_src, target_srcm = generators_samples[0]
|
|
warped_dst, target_dst, target_dstm = generators_samples[1]
|
|
|
|
feed = [warped_src, warped_dst, target_src, target_srcm, target_dst, target_dstm]
|
|
|
|
src_loss, dst_loss, = self.src_dst_train (feed)
|
|
|
|
if self.options['learn_mask']:
|
|
feed = [ warped_src, warped_dst, target_srcm, target_dstm ]
|
|
src_mask_loss, dst_mask_loss, = self.src_dst_mask_train (feed)
|
|
|
|
return ( ('src_loss', src_loss), ('dst_loss', dst_loss), )
|
|
|
|
#override
|
|
def onGetPreview(self, sample):
|
|
test_S = sample[0][1][0:4] #first 4 samples
|
|
test_S_m = sample[0][2][0:4] #first 4 samples
|
|
test_D = sample[1][1][0:4]
|
|
test_D_m = sample[1][2][0:4]
|
|
|
|
if self.options['learn_mask']:
|
|
S, D, SS, DD, DDM, SD, SDM = [ np.clip(x, 0.0, 1.0) for x in ([test_S,test_D] + self.AE_view ([test_S, test_D]) ) ]
|
|
DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
|
|
else:
|
|
S, D, SS, DD, SD, = [ np.clip(x, 0.0, 1.0) for x in ([test_S,test_D] + self.AE_view ([test_S, test_D]) ) ]
|
|
|
|
result = []
|
|
st = []
|
|
for i in range(len(test_S)):
|
|
ar = S[i], SS[i], D[i], DD[i], SD[i]
|
|
|
|
st.append ( np.concatenate ( ar, axis=1) )
|
|
|
|
result += [ ('SAE', np.concatenate (st, axis=0 )), ]
|
|
|
|
if self.options['learn_mask']:
|
|
st_m = []
|
|
for i in range(len(test_S)):
|
|
ar = S[i]*test_S_m[i], SS[i], D[i]*test_D_m[i], DD[i]*DDM[i], SD[i]*(DDM[i]*SDM[i])
|
|
st_m.append ( np.concatenate ( ar, axis=1) )
|
|
|
|
result += [ ('SAE masked', np.concatenate (st_m, axis=0 )), ]
|
|
|
|
return result
|
|
|
|
def predictor_func (self, face=None, dummy_predict=False):
|
|
if dummy_predict:
|
|
self.AE_convert ([ np.zeros ( (1, self.options['resolution'], self.options['resolution'], 3), dtype=np.float32 ) ])
|
|
else:
|
|
if self.options['learn_mask']:
|
|
bgr, mask_dst_dstm, mask_src_dstm = self.AE_convert ([face[np.newaxis,...]])
|
|
mask = mask_dst_dstm[0] * mask_src_dstm[0]
|
|
return bgr[0], mask[...,0]
|
|
else:
|
|
bgr, = self.AE_convert ([face[np.newaxis,...]])
|
|
return bgr[0]
|
|
|
|
#override
|
|
def get_ConverterConfig(self):
|
|
face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
|
|
|
|
import converters
|
|
return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), converters.ConverterConfigMasked(face_type=face_type,
|
|
default_mode = 'overlay' if self.options['ct_mode'] != 'none' or self.options['face_style_power'] or self.options['bg_style_power'] else 'seamless',
|
|
clip_hborder_mask_per=0.0625 if (self.options['face_type'] == 'f') else 0,
|
|
)
|
|
|
|
Model = SAEModel
|