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e1da9c56b4
* test * _ * _ * upd dev_poseest * SAE: finally collapses are fixed * fix batch size help
652 lines
No EOL
33 KiB
Python
652 lines
No EOL
33 KiB
Python
from functools import partial
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import numpy as np
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from nnlib import nnlib
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from models import ModelBase
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from facelib import FaceType
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from samplelib import *
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from interact import interact as io
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#SAE - Styled AutoEncoder
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class SAEModel(ModelBase):
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encoderH5 = 'encoder.h5'
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inter_BH5 = 'inter_B.h5'
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inter_ABH5 = 'inter_AB.h5'
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decoderH5 = 'decoder.h5'
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decodermH5 = 'decoderm.h5'
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decoder_srcH5 = 'decoder_src.h5'
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decoder_srcmH5 = 'decoder_srcm.h5'
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decoder_dstH5 = 'decoder_dst.h5'
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decoder_dstmH5 = 'decoder_dstm.h5'
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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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self.options['learn_mask'] = io.input_bool ("Learn mask? (y/n, ?:help skip:y) : ", True, 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['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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self.options['learn_mask'] = self.options.get('learn_mask', True)
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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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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['remove_gray_border'] = io.input_bool ("Remove gray border? (y/n, ?:help skip:n) : ", False, help_message="Removes gray border of predicted face, but requires more computing resources.")
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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['remove_gray_border'] = self.options.get('remove_gray_border', False)
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if is_first_run:
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self.options['multiscale_decoder'] = io.input_bool ("Use multiscale decoder? (y/n, ?:help skip:n) : ", False, help_message="Multiscale decoder helps to get better details.")
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else:
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self.options['multiscale_decoder'] = self.options.get('multiscale_decoder', False)
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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 ("Use pixel loss? (y/n, ?:help skip: %s ) : " % (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.")
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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."), 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."), 0.0, 100.0 )
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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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#override
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def onInitialize(self):
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exec(nnlib.import_all(), locals(), globals())
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SAEModel.initialize_nn_functions()
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self.set_vram_batch_requirements({1.5:4})
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resolution = self.options['resolution']
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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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d_residual_blocks = True
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bgr_shape = (resolution, resolution, 3)
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mask_shape = (resolution, resolution, 1)
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self.ms_count = ms_count = 3 if (self.options['multiscale_decoder']) else 1
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masked_training = True
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warped_src = Input(bgr_shape)
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target_src = Input(bgr_shape)
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target_srcm = Input(mask_shape)
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warped_dst = Input(bgr_shape)
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target_dst = Input(bgr_shape)
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target_dstm = Input(mask_shape)
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target_src_ar = [ Input ( ( bgr_shape[0] // (2**i) ,)*2 + (bgr_shape[-1],) ) for i in range(ms_count-1, -1, -1)]
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target_srcm_ar = [ Input ( ( mask_shape[0] // (2**i) ,)*2 + (mask_shape[-1],) ) for i in range(ms_count-1, -1, -1)]
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target_dst_ar = [ Input ( ( bgr_shape[0] // (2**i) ,)*2 + (bgr_shape[-1],) ) for i in range(ms_count-1, -1, -1)]
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target_dstm_ar = [ Input ( ( mask_shape[0] // (2**i) ,)*2 + (mask_shape[-1],) ) for i in range(ms_count-1, -1, -1)]
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padding = 'zero'
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norm = ''
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if '-s' in self.options['archi']:
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norm = 'bn'
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common_flow_kwargs = { 'padding': padding,
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'norm': norm,
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'act':'' }
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weights_to_load = []
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if 'liae' in self.options['archi']:
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self.encoder = modelify(SAEModel.LIAEEncFlow(resolution, ch_dims=e_ch_dims, **common_flow_kwargs) ) (Input(bgr_shape))
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enc_output_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ]
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self.inter_B = modelify(SAEModel.LIAEInterFlow(resolution, ae_dims=ae_dims, **common_flow_kwargs)) (enc_output_Inputs)
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self.inter_AB = modelify(SAEModel.LIAEInterFlow(resolution, ae_dims=ae_dims, **common_flow_kwargs)) (enc_output_Inputs)
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inter_output_Inputs = [ Input( np.array(K.int_shape(x)[1:])*(1,1,2) ) for x in self.inter_B.outputs ]
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self.decoder = modelify(SAEModel.LIAEDecFlow (bgr_shape[2],ch_dims=d_ch_dims, multiscale_count=self.ms_count, add_residual_blocks=d_residual_blocks, **common_flow_kwargs)) (inter_output_Inputs)
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if self.options['learn_mask']:
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self.decoderm = modelify(SAEModel.LIAEDecFlow (mask_shape[2],ch_dims=d_ch_dims, **common_flow_kwargs)) (inter_output_Inputs)
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if not self.is_first_run():
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weights_to_load += [ [self.encoder , 'encoder.h5'],
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[self.inter_B , 'inter_B.h5'],
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[self.inter_AB, 'inter_AB.h5'],
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[self.decoder , 'decoder.h5'],
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]
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if self.options['learn_mask']:
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weights_to_load += [ [self.decoderm, 'decoderm.h5'] ]
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warped_src_code = self.encoder (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 (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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pred_src_src = self.decoder(warped_src_inter_code)
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pred_dst_dst = self.decoder(warped_dst_inter_code)
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pred_src_dst = self.decoder(warped_src_dst_inter_code)
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if self.options['learn_mask']:
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pred_src_srcm = self.decoderm(warped_src_inter_code)
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pred_dst_dstm = self.decoderm(warped_dst_inter_code)
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pred_src_dstm = self.decoderm(warped_src_dst_inter_code)
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elif 'df' in self.options['archi']:
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self.encoder = modelify(SAEModel.DFEncFlow(resolution, ae_dims=ae_dims, ch_dims=e_ch_dims, **common_flow_kwargs) ) (Input(bgr_shape))
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dec_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ]
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self.decoder_src = modelify(SAEModel.DFDecFlow (bgr_shape[2],ch_dims=d_ch_dims, multiscale_count=self.ms_count, add_residual_blocks=d_residual_blocks, **common_flow_kwargs )) (dec_Inputs)
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self.decoder_dst = modelify(SAEModel.DFDecFlow (bgr_shape[2],ch_dims=d_ch_dims, multiscale_count=self.ms_count, add_residual_blocks=d_residual_blocks, **common_flow_kwargs )) (dec_Inputs)
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if self.options['learn_mask']:
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self.decoder_srcm = modelify(SAEModel.DFDecFlow (mask_shape[2],ch_dims=d_ch_dims, **common_flow_kwargs )) (dec_Inputs)
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self.decoder_dstm = modelify(SAEModel.DFDecFlow (mask_shape[2],ch_dims=d_ch_dims, **common_flow_kwargs )) (dec_Inputs)
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if not self.is_first_run():
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weights_to_load += [ [self.encoder , 'encoder.h5'],
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[self.decoder_src, 'decoder_src.h5'],
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[self.decoder_dst, 'decoder_dst.h5']
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]
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if self.options['learn_mask']:
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weights_to_load += [ [self.decoder_srcm, 'decoder_srcm.h5'],
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[self.decoder_dstm, 'decoder_dstm.h5'],
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]
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warped_src_code = self.encoder (warped_src)
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warped_dst_code = self.encoder (warped_dst)
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pred_src_src = self.decoder_src(warped_src_code)
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pred_dst_dst = self.decoder_dst(warped_dst_code)
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pred_src_dst = self.decoder_src(warped_dst_code)
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if self.options['learn_mask']:
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pred_src_srcm = self.decoder_srcm(warped_src_code)
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pred_dst_dstm = self.decoder_dstm(warped_dst_code)
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pred_src_dstm = self.decoder_srcm(warped_dst_code)
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pred_src_src, pred_dst_dst, pred_src_dst, = [ [x] if type(x) != list else x for x in [pred_src_src, pred_dst_dst, pred_src_dst, ] ]
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if self.options['learn_mask']:
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pred_src_srcm, pred_dst_dstm, pred_src_dstm = [ [x] if type(x) != list else x for x in [pred_src_srcm, pred_dst_dstm, pred_src_dstm] ]
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target_srcm_blurred_ar = [ gaussian_blur( max(1, K.int_shape(x)[1] // 32) )(x) for x in target_srcm_ar]
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target_srcm_sigm_ar = target_srcm_blurred_ar #[ x / 2.0 + 0.5 for x in target_srcm_blurred_ar]
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target_srcm_anti_sigm_ar = [ 1.0 - x for x in target_srcm_sigm_ar]
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target_dstm_blurred_ar = [ gaussian_blur( max(1, K.int_shape(x)[1] // 32) )(x) for x in target_dstm_ar]
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target_dstm_sigm_ar = target_dstm_blurred_ar#[ x / 2.0 + 0.5 for x in target_dstm_blurred_ar]
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target_dstm_anti_sigm_ar = [ 1.0 - x for x in target_dstm_sigm_ar]
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target_src_sigm_ar = target_src_ar#[ x + 1 for x in target_src_ar]
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target_dst_sigm_ar = target_dst_ar#[ x + 1 for x in target_dst_ar]
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pred_src_src_sigm_ar = pred_src_src#[ x + 1 for x in pred_src_src]
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pred_dst_dst_sigm_ar = pred_dst_dst#[ x + 1 for x in pred_dst_dst]
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pred_src_dst_sigm_ar = pred_src_dst#[ x + 1 for x in pred_src_dst]
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target_src_masked_ar = [ target_src_sigm_ar[i]*target_srcm_sigm_ar[i] for i in range(len(target_src_sigm_ar))]
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target_dst_masked_ar = [ target_dst_sigm_ar[i]*target_dstm_sigm_ar[i] for i in range(len(target_dst_sigm_ar))]
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target_dst_anti_masked_ar = [ target_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(target_dst_sigm_ar))]
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pred_src_src_masked_ar = [ pred_src_src_sigm_ar[i] * target_srcm_sigm_ar[i] for i in range(len(pred_src_src_sigm_ar))]
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pred_dst_dst_masked_ar = [ pred_dst_dst_sigm_ar[i] * target_dstm_sigm_ar[i] for i in range(len(pred_dst_dst_sigm_ar))]
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target_src_masked_ar_opt = target_src_masked_ar if masked_training else target_src_sigm_ar
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target_dst_masked_ar_opt = target_dst_masked_ar if masked_training else target_dst_sigm_ar
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pred_src_src_masked_ar_opt = pred_src_src_masked_ar if masked_training else pred_src_src_sigm_ar
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pred_dst_dst_masked_ar_opt = pred_dst_dst_masked_ar if masked_training else pred_dst_dst_sigm_ar
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psd_target_dst_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))]
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psd_target_dst_anti_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))]
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if self.is_training_mode:
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self.src_dst_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
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self.src_dst_mask_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
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if 'liae' in self.options['archi']:
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src_dst_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights
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if self.options['learn_mask']:
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src_dst_mask_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoderm.trainable_weights
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else:
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src_dst_loss_train_weights = self.encoder.trainable_weights + self.decoder_src.trainable_weights + self.decoder_dst.trainable_weights
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if self.options['learn_mask']:
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src_dst_mask_loss_train_weights = self.encoder.trainable_weights + self.decoder_srcm.trainable_weights + self.decoder_dstm.trainable_weights
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if not self.options['pixel_loss']:
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src_loss_batch = sum([ 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_ar_opt[i], pred_src_src_masked_ar_opt[i]) for i in range(len(target_src_masked_ar_opt)) ])
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else:
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src_loss_batch = sum([ K.mean ( 50*K.square( target_src_masked_ar_opt[i] - pred_src_src_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_src_masked_ar_opt)) ])
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src_loss = K.mean(src_loss_batch)
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face_style_power = self.options['face_style_power'] / 100.0
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if face_style_power != 0:
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src_loss += style_loss(gaussian_blur_radius=resolution//16, loss_weight=face_style_power, wnd_size=0)( psd_target_dst_masked_ar[-1], target_dst_masked_ar[-1] )
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bg_style_power = self.options['bg_style_power'] / 100.0
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if bg_style_power != 0:
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if not self.options['pixel_loss']:
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bg_loss = K.mean( (10*bg_style_power)*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( psd_target_dst_anti_masked_ar[-1], target_dst_anti_masked_ar[-1] ))
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else:
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bg_loss = K.mean( (50*bg_style_power)*K.square( psd_target_dst_anti_masked_ar[-1] - target_dst_anti_masked_ar[-1] ))
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src_loss += bg_loss
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if not self.options['pixel_loss']:
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dst_loss_batch = sum([ 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)(target_dst_masked_ar_opt[i], pred_dst_dst_masked_ar_opt[i]) for i in range(len(target_dst_masked_ar_opt)) ])
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else:
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|
dst_loss_batch = sum([ K.mean ( 50*K.square( target_dst_masked_ar_opt[i] - pred_dst_dst_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_dst_masked_ar_opt)) ])
|
|
|
|
dst_loss = K.mean(dst_loss_batch)
|
|
|
|
feed = [warped_src, warped_dst]
|
|
feed += target_src_ar[::-1]
|
|
feed += target_srcm_ar[::-1]
|
|
feed += target_dst_ar[::-1]
|
|
feed += target_dstm_ar[::-1]
|
|
|
|
self.src_dst_train = K.function (feed,[src_loss,dst_loss], self.src_dst_opt.get_updates(src_loss+dst_loss, src_dst_loss_train_weights) )
|
|
|
|
if self.options['learn_mask']:
|
|
src_mask_loss = sum([ K.mean(K.square(target_srcm_ar[-1]-pred_src_srcm[-1])) for i in range(len(target_srcm_ar)) ])
|
|
dst_mask_loss = sum([ K.mean(K.square(target_dstm_ar[-1]-pred_dst_dstm[-1])) for i in range(len(target_dstm_ar)) ])
|
|
|
|
feed = [ warped_src, warped_dst]
|
|
feed += target_srcm_ar[::-1]
|
|
feed += target_dstm_ar[::-1]
|
|
|
|
self.src_dst_mask_train = K.function (feed,[src_mask_loss, dst_mask_loss], self.src_dst_mask_opt.get_updates(src_mask_loss+dst_mask_loss, src_dst_mask_loss_train_weights) )
|
|
|
|
if self.options['learn_mask']:
|
|
self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src[-1], pred_dst_dst[-1], pred_dst_dstm[-1], pred_src_dst[-1], pred_src_dstm[-1]])
|
|
else:
|
|
self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src[-1], pred_dst_dst[-1], pred_src_dst[-1] ] )
|
|
|
|
self.load_weights_safe(weights_to_load)#, [ [self.src_dst_opt, 'src_dst_opt'], [self.src_dst_mask_opt, 'src_dst_mask_opt']])
|
|
else:
|
|
self.load_weights_safe(weights_to_load)
|
|
if self.options['learn_mask']:
|
|
self.AE_convert = K.function ([warped_dst],[ pred_src_dst[-1], pred_dst_dstm[-1], pred_src_dstm[-1] ])
|
|
else:
|
|
self.AE_convert = K.function ([warped_dst],[ pred_src_dst[-1] ])
|
|
|
|
|
|
if self.is_training_mode:
|
|
self.src_sample_losses = []
|
|
self.dst_sample_losses = []
|
|
|
|
f = SampleProcessor.TypeFlags
|
|
face_type = f.FACE_TYPE_FULL if self.options['face_type'] == 'f' else f.FACE_TYPE_HALF
|
|
|
|
output_sample_types=[ [f.WARPED_TRANSFORMED | face_type | f.MODE_BGR, resolution] ]
|
|
output_sample_types += [ [f.TRANSFORMED | face_type | f.MODE_BGR, resolution // (2**i) ] for i in range(ms_count)]
|
|
output_sample_types += [ [f.TRANSFORMED | face_type | f.MODE_M | f.FACE_MASK_FULL, resolution // (2**i) ] for i in range(ms_count)]
|
|
|
|
self.set_training_data_generators ([
|
|
SampleGeneratorFace(self.training_data_src_path, sort_by_yaw_target_samples_path=self.training_data_dst_path if self.sort_by_yaw 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])+self.src_scale_mod / 100.0 ),
|
|
output_sample_types=output_sample_types ),
|
|
|
|
SampleGeneratorFace(self.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=output_sample_types )
|
|
])
|
|
|
|
#override
|
|
def onSave(self):
|
|
opt_ar = [ [self.src_dst_opt, 'src_dst_opt'],
|
|
[self.src_dst_mask_opt, 'src_dst_mask_opt']
|
|
]
|
|
ar = []
|
|
if 'liae' in self.options['archi']:
|
|
ar += [[self.encoder, 'encoder.h5'],
|
|
[self.inter_B, 'inter_B.h5'],
|
|
[self.inter_AB, 'inter_AB.h5'],
|
|
[self.decoder, 'decoder.h5']
|
|
]
|
|
if self.options['learn_mask']:
|
|
ar += [ [self.decoderm, 'decoderm.h5'] ]
|
|
elif 'df' in self.options['archi']:
|
|
ar += [[self.encoder, 'encoder.h5'],
|
|
[self.decoder_src, 'decoder_src.h5'],
|
|
[self.decoder_dst, 'decoder_dst.h5']
|
|
]
|
|
if self.options['learn_mask']:
|
|
ar += [ [self.decoder_srcm, 'decoder_srcm.h5'],
|
|
[self.decoder_dstm, 'decoder_dstm.h5'] ]
|
|
|
|
self.save_weights_safe(ar)
|
|
|
|
|
|
#override
|
|
def onTrainOneIter(self, generators_samples, generators_list):
|
|
src_samples = generators_samples[0]
|
|
dst_samples = generators_samples[1]
|
|
|
|
feed = [src_samples[0], dst_samples[0] ] + \
|
|
src_samples[1:1+self.ms_count*2] + \
|
|
dst_samples[1:1+self.ms_count*2]
|
|
|
|
src_loss, dst_loss, = self.src_dst_train (feed)
|
|
|
|
if self.options['learn_mask']:
|
|
feed = [ src_samples[0], dst_samples[0] ] + \
|
|
src_samples[1+self.ms_count:1+self.ms_count*2] + \
|
|
dst_samples[1+self.ms_count:1+self.ms_count*2]
|
|
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][1+self.ms_count][0:4] #first 4 samples
|
|
test_D = sample[1][1][0:4]
|
|
test_D_m = sample[1][1+self.ms_count][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(0, 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(0, 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):
|
|
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_converter(self):
|
|
base_erode_mask_modifier = 30 if self.options['face_type'] == 'f' else 100
|
|
base_blur_mask_modifier = 0 if self.options['face_type'] == 'f' else 100
|
|
|
|
default_erode_mask_modifier = 0
|
|
default_blur_mask_modifier = 100 if (self.options['face_style_power'] or self.options['bg_style_power']) and \
|
|
self.options['face_type'] == 'f' else 0
|
|
|
|
face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
|
|
|
|
from converters import ConverterMasked
|
|
return ConverterMasked(self.predictor_func,
|
|
predictor_input_size=self.options['resolution'],
|
|
predictor_masked=self.options['learn_mask'],
|
|
face_type=face_type,
|
|
default_mode = 1 if self.options['face_style_power'] or self.options['bg_style_power'] else 4,
|
|
base_erode_mask_modifier=base_erode_mask_modifier,
|
|
base_blur_mask_modifier=base_blur_mask_modifier,
|
|
default_erode_mask_modifier=default_erode_mask_modifier,
|
|
default_blur_mask_modifier=default_blur_mask_modifier,
|
|
clip_hborder_mask_per=0.0625 if (not self.options['remove_gray_border'] and self.options['face_type'] == 'f') else 0)
|
|
|
|
@staticmethod
|
|
def initialize_nn_functions():
|
|
exec (nnlib.import_all(), locals(), globals())
|
|
|
|
def NormPass(x):
|
|
return x
|
|
|
|
def Norm(norm=''):
|
|
if norm == 'bn':
|
|
return BatchNormalization(axis=-1)
|
|
else:
|
|
return NormPass
|
|
|
|
def Act(act='', lrelu_alpha=0.1):
|
|
if act == 'prelu':
|
|
return PReLU()
|
|
else:
|
|
return LeakyReLU(alpha=lrelu_alpha)
|
|
|
|
class ResidualBlock(object):
|
|
def __init__(self, filters, kernel_size=3, padding='zero', norm='', act='', **kwargs):
|
|
self.filters = filters
|
|
self.kernel_size = kernel_size
|
|
self.padding = padding
|
|
self.norm = norm
|
|
self.act = act
|
|
|
|
def __call__(self, inp):
|
|
x = inp
|
|
x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding)(x)
|
|
x = Act(self.act, lrelu_alpha=0.2)(x)
|
|
x = Norm(self.norm)(x)
|
|
x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding)(x)
|
|
x = Add()([x, inp])
|
|
x = Act(self.act, lrelu_alpha=0.2)(x)
|
|
x = Norm(self.norm)(x)
|
|
return x
|
|
SAEModel.ResidualBlock = ResidualBlock
|
|
|
|
def downscale (dim, padding='zero', norm='', act='', **kwargs):
|
|
def func(x):
|
|
return Norm(norm)( Act(act) (Conv2D(dim, kernel_size=5, strides=2, padding=padding)(x)) )
|
|
return func
|
|
SAEModel.downscale = downscale
|
|
|
|
def upscale (dim, padding='zero', norm='', act='', **kwargs):
|
|
def func(x):
|
|
return SubpixelUpscaler()(Norm(norm)(Act(act)(Conv2D(dim * 4, kernel_size=3, strides=1, padding=padding)(x))))
|
|
return func
|
|
SAEModel.upscale = upscale
|
|
|
|
def to_bgr (output_nc, padding='zero', **kwargs):
|
|
def func(x):
|
|
return Conv2D(output_nc, kernel_size=5, padding=padding, activation='sigmoid')(x)
|
|
return func
|
|
SAEModel.to_bgr = to_bgr
|
|
|
|
@staticmethod
|
|
def LIAEEncFlow(resolution, ch_dims, **kwargs):
|
|
exec (nnlib.import_all(), locals(), globals())
|
|
upscale = partial(SAEModel.upscale, **kwargs)
|
|
downscale = partial(SAEModel.downscale, **kwargs)
|
|
|
|
def func(input):
|
|
dims = K.int_shape(input)[-1]*ch_dims
|
|
|
|
x = input
|
|
x = downscale(dims)(x)
|
|
x = downscale(dims*2)(x)
|
|
x = downscale(dims*4)(x)
|
|
x = downscale(dims*8)(x)
|
|
|
|
x = Flatten()(x)
|
|
return x
|
|
return func
|
|
|
|
@staticmethod
|
|
def LIAEInterFlow(resolution, ae_dims=256, **kwargs):
|
|
exec (nnlib.import_all(), locals(), globals())
|
|
upscale = partial(SAEModel.upscale, **kwargs)
|
|
lowest_dense_res=resolution // 16
|
|
|
|
def func(input):
|
|
x = input[0]
|
|
x = Dense(ae_dims)(x)
|
|
x = Dense(lowest_dense_res * lowest_dense_res * ae_dims*2)(x)
|
|
x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims*2))(x)
|
|
x = upscale(ae_dims*2)(x)
|
|
return x
|
|
return func
|
|
|
|
@staticmethod
|
|
def LIAEDecFlow(output_nc,ch_dims, multiscale_count=1, add_residual_blocks=False, **kwargs):
|
|
exec (nnlib.import_all(), locals(), globals())
|
|
upscale = partial(SAEModel.upscale, **kwargs)
|
|
to_bgr = partial(SAEModel.to_bgr, **kwargs)
|
|
dims = output_nc * ch_dims
|
|
ResidualBlock = partial(SAEModel.ResidualBlock, **kwargs)
|
|
|
|
def func(input):
|
|
x = input[0]
|
|
|
|
outputs = []
|
|
x1 = upscale(dims*8)( x )
|
|
|
|
if add_residual_blocks:
|
|
x1 = ResidualBlock(dims*8)(x1)
|
|
x1 = ResidualBlock(dims*8)(x1)
|
|
|
|
if multiscale_count >= 3:
|
|
outputs += [ to_bgr(output_nc) ( x1 ) ]
|
|
|
|
x2 = upscale(dims*4)( x1 )
|
|
|
|
if add_residual_blocks:
|
|
x2 = ResidualBlock(dims*4)(x2)
|
|
x2 = ResidualBlock(dims*4)(x2)
|
|
|
|
if multiscale_count >= 2:
|
|
outputs += [ to_bgr(output_nc) ( x2 ) ]
|
|
|
|
x3 = upscale(dims*2)( x2 )
|
|
|
|
if add_residual_blocks:
|
|
x3 = ResidualBlock( dims*2)(x3)
|
|
x3 = ResidualBlock( dims*2)(x3)
|
|
|
|
outputs += [ to_bgr(output_nc) ( x3 ) ]
|
|
|
|
return outputs
|
|
return func
|
|
|
|
@staticmethod
|
|
def DFEncFlow(resolution, ae_dims, ch_dims, **kwargs):
|
|
exec (nnlib.import_all(), locals(), globals())
|
|
upscale = partial(SAEModel.upscale, **kwargs)
|
|
downscale = partial(SAEModel.downscale, **kwargs)#, kernel_regularizer=keras.regularizers.l2(0.0),
|
|
lowest_dense_res = resolution // 16
|
|
|
|
def func(input):
|
|
x = input
|
|
|
|
dims = K.int_shape(input)[-1]*ch_dims
|
|
x = downscale(dims)(x)
|
|
x = downscale(dims*2)(x)
|
|
x = downscale(dims*4)(x)
|
|
x = downscale(dims*8)(x)
|
|
|
|
x = Dense(ae_dims)(Flatten()(x))
|
|
x = Dense(lowest_dense_res * lowest_dense_res * ae_dims)(x)
|
|
x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims))(x)
|
|
x = upscale(ae_dims)(x)
|
|
return x
|
|
return func
|
|
|
|
@staticmethod
|
|
def DFDecFlow(output_nc, ch_dims, multiscale_count=1, add_residual_blocks=False, **kwargs):
|
|
exec (nnlib.import_all(), locals(), globals())
|
|
upscale = partial(SAEModel.upscale, **kwargs)
|
|
to_bgr = partial(SAEModel.to_bgr, **kwargs)
|
|
dims = output_nc * ch_dims
|
|
ResidualBlock = partial(SAEModel.ResidualBlock, **kwargs)
|
|
|
|
def func(input):
|
|
x = input[0]
|
|
|
|
outputs = []
|
|
x1 = upscale(dims*8)( x )
|
|
|
|
if add_residual_blocks:
|
|
x1 = ResidualBlock( dims*8 )(x1)
|
|
x1 = ResidualBlock( dims*8 )(x1)
|
|
|
|
if multiscale_count >= 3:
|
|
outputs += [ to_bgr(output_nc) ( x1 ) ]
|
|
|
|
x2 = upscale(dims*4)( x1 )
|
|
|
|
if add_residual_blocks:
|
|
x2 = ResidualBlock( dims*4)(x2)
|
|
x2 = ResidualBlock( dims*4)(x2)
|
|
|
|
if multiscale_count >= 2:
|
|
outputs += [ to_bgr(output_nc) ( x2 ) ]
|
|
|
|
x3 = upscale(dims*2)( x2 )
|
|
|
|
if add_residual_blocks:
|
|
x3 = ResidualBlock( dims*2)(x3)
|
|
x3 = ResidualBlock( dims*2)(x3)
|
|
|
|
outputs += [ to_bgr(output_nc) ( x3 ) ]
|
|
|
|
return outputs
|
|
return func
|
|
|
|
|
|
Model = SAEModel |